Genetic Programming and Evolvable Machines

MIT story on GPEM article on sensory evaluation data

2012-01-25T13:00:00.000-08:00

There's a nice story on MIT news, "The mathematics of taste," which is also being picked up on some other sites, about the GPEM article on "Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization" by Kalyan Veeramachaneni, Ekaterina Vladislavleva and Una-May O’Reilly.

Eureqa in Science News

2011-12-31T08:30:00.000-08:00

There's a nice little piece in the January 14, 2012 issue of Science News on Lipson and Schmidt's Eureqa system. (h/t to Stuart Card for the pointer.)

New Book (Free): "Evolved to Win", by Moshe Sipper

2011-11-29T05:53:00.000-08:00

Recent years have seen a sharp increase in the application of evolutionary computation techniques within the domain of games. Situated at the forefront of this research tidal wave, Moshe Sipper and his group have produced a plethora of award-winning results, in numerous games of diverse natures, evidencing the success and efficiency of evolutionary algorithms in general—and genetic programming in particular—at producing top-notch, human-competitive game strategies. From classic chess and checkers, through simulated car racing and virtual warfare, to mind-bending puzzles, this book serves both as a tour de force of the research landscape and as a guide to the application of evolutionary computation within the domain of games.

An outstanding, timely book in the rapidly growing area of computational intelligence in games. A must read for both the neophyte and the seasoned researcher, with all the hallmarks of a landmark book.

John Koza, author of Genetic Programming tetralogy

In Evolved to Win Moshe Sipper provides a treasure trove of detailed examples and advice on using evolutionary computation, in conjunction with human expertise, to solve hard puzzles and to win a wide variety of challenging games. Sipper and his colleagues know this field better than anyone else, having produced some of the field's strongest and most exciting results, and this book provides a comprehensive tour of their results along with ample guidance for newcomers to the field.

Lee Spector, Professor of Computer Science, Hampshire College, and Editor-in-Chief of the journal Genetic Programming and Evolvable Machines

Free download Hard copy

citing Journal > conference > tech reports

2011-11-21T08:39:00.000-08:00

I have seen recently several papers which cite technical reports.
May be there are good reasons for this but it was my understanding
that where work is available in a number of places we should cite
journal articles before conference papers and only cite technical
reports if the work is not otherwise available.

Bill

Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

2011-10-03T11:44:00.000-07:00

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent's letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach", of which I was a co-author.
We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.
We would like to clarify one point: while the review reports that the book ignores "simulators that cheat", the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it. (See pp. 157-167, including the section "SPICE can lie".)
The broader issue -- trustworthy synthesis -- is a broad challenge that the last half of the book addresses. Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon. The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.
As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.

-- Trent McConaghy, October 3, 2011

New impact factor

2011-09-18T19:58:00.000-07:00

The journal received its first impact factor from Journal Citation Reports last year (2010), for the 2009 publication year. Now (in 2011) we have new numbers for the 2010 publication year. They have improved somewhat and are, I think, strong for a journal as young as Genetic Programming and Evolvable Machines:

Impact factor: 1.167
Rank in category: Artificial Intelligence: 63 out of 108
Rank in category: Theory and Methods: 41 out of 97

Journal Citation Reports also provides an Immediacy Index, for which our numbers are:

Immediacy index: 0.143
Rank in category: Artificial Intelligence: 64 out of 108
Rank in category: Theory and Methods: 52 out of 97

Programming excel from training examples

2011-09-16T07:02:00.000-07:00

Remember people said GP could not program a spread sheet, well it seems microsoft has done it. At PASTE 2011 last week Sumit Gulwani gave a keynote which included discussion of his recent paper at PLDI http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/Harris_2011_SIGPlan.html in which he describes a research tool which allowed users to program microsoft tools (such as their spread sheet excell and their word processor) from training examples. Ie automatically create macros and short scripts for users who do not wish to remember how to program. Technical details are a bit sketchy but it seems something like GP is in there. It may be available commercially in the future.

Bill

GPEM 12(4) now available online

2011-09-03T19:39:00.000-07:00

The fourth issue of volume 12 of Genetic Programming and Evolvable Machines is now available online, with the following articles:

"Evolution of human-competitive lossless compression algorithms with GP-zip2"

by Ahmed Kattan and Riccardo Poli

"Defining locality as a problem difficulty measure in genetic programming"

by Edgar Galván-López, James McDermott, Michael O’Neill and Anthony Brabazon

"Accelerating floating-point fitness functions in evolutionary algorithms: a FPGA-CPU-GPU performance comparison"

by Juan A. Gomez-Pulido, Miguel A. Vega-Rodriguez, Juan M. Sanchez-Perez, Silvio Priem-Mendes and Vitor Carreira

"Long memory time series forecasting by using genetic programming"

by Emiliano Carreño Jara

SOFTWARE REVIEW

"GPLAB: software review"

by Indriyati Atmosukarto

BOOK REVIEW

"Trent McConaghy, P. Palmers, G. Peng, Michiel Steyaert, Georges Gielen: Variation-aware analog structural synthesis: a computational intelligence approach"

by John Rieffel

BOOK REVIEW

"Paul Coates: Programming architecture"

by Benachir Medjdoub

95% confident = 950/1000 for any distribution

2011-08-31T05:26:00.000-07:00

A technique I have seen used for statistical confidence testing of non-Gaussian distributions is to generate 1000 random examples of the distribution. If the you want to be 95% confident that answer to be checked comes from the same distribution then it should be "like" 950 of the 1000 examples.

Eg if the distribution is reasonably well behaved then if the answer to be checked lies outside the range of the 25th to 975th example we can say we confidently reject the null hypothesis and say our answer is not from the distribution used to generate the 1000 examples. We do not need Z-scores, t-tests etc.

This non-parametric test should be ok with any distribution. We are effectively burning CPU cyles rather than spending brain cycles on devising and validating a statistical technique specifically for our new distribution.

Computational Intelligence Using Genetic Programming - GPTP'11

2011-08-17T06:31:00.000-07:00

This is a repost of my piece on GPTP'11 from Epistasis Blog.

I just returned from the IXth Genetic Programming Theory and Practice Workshop held by the Center for the Study of Complex Systems at the University of Michigan. This is an invitation only workshop that brings together theorists and practitioners interested in the development and application of computer systems that can solve complex problems by developing their own programs (i.e. automatic programming). This group focuses on the use of genetic programming or GP to discover useful computer programs using the principles of evolution by natural selection. The proceedings from this workshop are published each year in a book that can be found on Amazon. The proceedings from this year will be published in late 2011 or early 2012.

The real value of this workshop is the large amount of time dedicted to open-ended discussion about how solve complex problems in medicine, industry, finance, etc. My own motivation for working with GP is to teach the computer how to solve a complex human genetics problem as I would. I do not believe that naive computer programs or analysis strategies such as those used in the agnostics genome-wide association study (GWAS) paradigm will be successful in addressing the complexity of the genotype-phenotype relationship. We, as human analysis engines, don't ignore the pathobiology of disease when we look at data. Why should we instruct the computer to do the same? Given infinite time, each of us would tinker and try new and different things with the data until we found a good answer that made biological sense. We would use our knoweldge of biochemistry, genomics, molecular biology, pathology and physiology to both frame the analysis and interpret the results. Our series of papers published as part of GPTP since 2006 have focused on adaptive computer programs that harness this kind of biological and biomedical knowledge to explore the space of computer programs that can build models of genetic architecture.

One of the more interesting and extended discussions at GPTP this year was about novelty-seeking. Ken Stanley gave a great talk about rewarding computer programs that explore new and different solutions to a problem (read more). His Picbreeder program is a nice example of novelty search in the sense that you can discover and develop interesting pictures without a clear initial objective in mind (e.g. evolve a picture of a car). An analogy in human genetics would be to reward computer program that generate genetic models of disease by exploring new biochemical pathways. I am working on approaches to try this within our own genetic analysis system. I like Ken's quote: "To achieve your highest goals, you must be willing to abandon them."

It is very clear that GP has been used to solve problems that humans or other computer programs haven't been able to. For example, Moshe Sipper has developed computer game players that rival human players (read more). Some of the participants (e.g. Michael Korns) even invest and make money using GP. This is a powerful way to do automatic programming and should be part of the broader toolbox of any complex problem-solver. I would be happy to send you a pre-print of our current GPTP paper.

EC presence at biology meetings

2011-08-16T15:47:00.001-07:00

I've just come back from a round of biology meetings. Few biologists know that evolution in silica exists. They may know that GA is "something that GARLI does", but that's about it (GARLI is a GA-based phylogeny estimation package). Every time I speak of EC, it is very well received, with surprise and wonder.

It would be good if we could publicize at evolutionary biology meetings more. At a minimum, Springer reps should bring GPEM when they attend evolution meetings.

Non standard terminology

2011-08-08T01:32:00.000-07:00

I came across two non standard uses of evolutionary computing jargon at GECCO. EC, like the rest of technical literature, is full of jargon. Jargon can be helpful where people agree on its meaning but confuses when it is misused. I have posted a link to the online glossary from "Genetic programming and data structures" on the GP mailing list. Hans-Georg Beyer has also defined evolutionary algorithms terminology.

GECCO 2011 bibtex GP bibliography

2011-08-04T08:26:00.000-07:00

The latest release of the GP biblio contains more than one hundred entries from last month's GECCO conference in Dublin.
Bibtex files for the proceedings and companion are also online or searchable via CCSB

Wiki GP Bibliography

2011-08-04T08:23:00.000-07:00

Adrian Carballal has created a wiki which allows you to maintain the GP bibliography's links to your homepage.

25 July 2011, University College, London, Genetic Programming for Software Engineering

2011-08-04T07:58:00.000-07:00

The 14th CREST open workshop proved to be so popular that the free registrations were closed some weeks before hand.
Stephanie Forrest gave the keynote on evolving fixes to software for which she won the Humie.
Two other international speakers were Federica Sarro, who talked on estimating time to produce software using GP and Wasif Afzal, who reviewed GP for prediction (Slides, Video).
David White talked about new work on optimising server farms, JVM in cloud computing systems (Slides, Video). I talked about evolving a CUDA kernel for gzip running on a GeForce 295 GTX GPU (paper)

GPEM 12(3) [SI: Evolvable Hardware Challenges] now available online

2011-07-23T15:08:00.000-07:00

The third issue of volume 12 of Genetic Programming and Evolvable Machines is now available online, with articles listed below. This is a big and exciting one, the special issue on Evolvable Hardware Challenges, edited by Pauline Haddow. Note also that the introduction and two of the other articles are available for free under open access.

"Introduction: special issue on evolvable hardware challenges"

by Pauline C. Haddow

"Challenges of evolvable hardware: past, present and the path to a promising future"

by Pauline C. Haddow & Andy M. Tyrrell

"An evolved anti-jamming adaptive beamforming network"

by Jason D. Lohn, Jonathan M. Becker & Derek S. Linden

"The evolution of standard cell libraries for future technology nodes"

by James Alfred Walker, James A. Hilder, Dave Reid, Asen Asenov, Scott Roy, Campbell Millar & Andy M. Tyrrell

"Hardware spiking neural network prototyping and application"

by Seamus Cawley, Fearghal Morgan, Brian McGinley, Sandeep Pande, Liam McDaid, Snaider Carrillo & Jim Harkin

"The route to a defect tolerant LUT through artificial evolution"

by Asbjoern Djupdal & Pauline C. Haddow

"Formal verification of candidate solutions for post-synthesis evolutionary optimization in evolvable hardware"

by Zdenek Vasicek & Lukas Sekanina

Software Review: "Open BEAGLE: a generic framework for evolutionary computations"

by Dmitry Batenkov

Book Review: "Sean Luke: essentials of metaheuristics"

by Michael Lones

GPEM 12(2) now available online

2011-04-29T15:18:00.000-07:00

The second issue of volume 12 of Genetic Programming and Evolvable Machines is now available online, with the following articles:

"Semantically-based crossover in genetic programming: application to real-valued symbolic regression"

by Nguyen Quang Uy, Nguyen Xuan Hoai, Michael O’Neill, R. I. McKay & Edgar Galván-López

"Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis"

by Kfir Wolfson, Shay Zakov, Moshe Sipper & Michal Ziv-Ukelson

"Tracer spectrum: a visualisation method for distributed evolutionary computation"

by Michael O’Neill, Anthony Brabazon & Erik Hemberg

Software Review: "Eureqa: software review"

by Renáta Dubcáková

Book Review: "Hitoshi Iba, Topon Kumar Paul, Yoshohiko Hasegawa: Applied genetic programming and machine learning"

by Kalyan Veeramachaneni

CFP: Special Issue on Systems Identification

2011-02-24T17:41:00.000-08:00

Guest Editors:

Steven Gustafson
GE Global Research, USA
steven D0T gustafson AT research D0T ge D0T com

Una-May O’Reilly
MIT, USA
unamay AT csail D0T mit D0T edu

Genetic programming is a valuable tool for reverse engineering data. Solutions found by Genetic Programming are in the form of algorithms that can be inspected, model checked, verified, and optimized. While this is possible with other classification methods, the intuitive representations GP employs makes it amenable to systems identification, defined here as:

Systems Identification: the process of exploring and identifying the variables, coefficients, and

model forms that best or most efficiently represent a system.

A recent example of GP for systems identification can be found in Schmidt and Lipson’s 2009 Science article “Distilling Free-form Natural Laws from Experimental Data” (Schmidt and Lipson 2009). Similarly, scientists at Dow Chemical, University of Antwerp, and Evolved Analytics LLC, have developed flexible and robust GP systems that provide key statistics and visualizations during the evolutionary process to guide the human user (Kotanchek, Vladislavleva, and Smits 2009). In this Special Issue, we would like to bring a focus on this unique but extremely valuable application of GP for Systems Identification. Topics of interest include:

• GP approaches to learn laws of various systems, e.g. biological, mechanical and artificial.

• GP approaches to uncover nonlinear relationships between variables in complex systems

• Scalable GP systems that can handle one or more orders of magnitude more than typical systems to enable more real-world Systems Identification, e.g. financial anomaly detection.

• GP systems that provide an improved understanding of the solutions, from variable interaction to improved confidence bounding, e.g. providing statistics of similar to modern packages like Minitab, Matlab, R.

• Approaches that move GP closer to systems like CART as a way to explore variables, relationships, and data, where users can quickly inspect solutions and modify the system to improve performance and capability.

We encourage all prospective authors to contact the guest editors, at the address below, as early as possible, to indicate your intention to submit a paper to this special issue.

Submission Deadline: September 1, 2011

Acceptance Notification: November 15, 2011

Final Manuscript Deadline: January 15, 2012

References

Kotanchek, M. E., Vladislavleva, E. Y., and Smits, G. F. (2009). Symbolic Regression via GP
as a Discovery Engine: Insights on Outliers and Prototypes. In Riolo, R., O'Reilly, U.-M., and

McConaghy, T. Genetic Programming Theory and Practice VII, pp. 55-72, Springer.
http://www.springerlink.com/content/p508hr96008h61t5/

Schmidt, M., and Lipson, H. (2009). Distilling Free-Form Natural Laws from Experimental
Data. Science 324(5923) pp. 81 - 85.

GPEM 12(1) now available online

2011-01-30T16:52:00.000-08:00

The first issue of volume 12 of Genetic Programming and Evolvable Machines is now available online, with the following articles:

"Editorial introduction"

by Lee Spector

"Acknowledgement"

by Lee Spector

"Expert-driven genetic algorithms for simulating evaluation functions"

by Omid David-Tabibi, Moshe Koppel & Nathan S. Netanyahu

"Autonomous experimental design optimization of a flapping wing"

by Markus Olhofer, Dilyana Yankulova & Bernhard Sendhoff

"Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis"

by Ukrit Watchareeruetai, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo & Noboru Ohnishi

Book Review: "Justin Lee: Morphogenetic Evolvable Hardware"

by Martin A. Trefzer

Book Review: "Gisele L. Pappa, Alex Freitas: Automating the design of data mining algorithms, an evolutionary computation approach"

by John Woodward

Book Review: "Arthur K. Kordon: Applying computational intelligence: how to create value"

by Guillermo Leguizamón

"Erratum to: Stochastic optimization of a biologically plausible spino-neuromuscular system model"

by Stanley Gotshall, Kathy Browder, Jessica Sampson, Terence Soule & Richard Wells

"Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies"

by Ivan Garibay

GECCO-2011 (Dublin) deadlines extended

2011-01-20T20:18:00.000-08:00

The GECCO-2011 regular paper submission deadline has been extended to February 9.

GECCO in Dublin, deadlines approaching

2010-12-10T18:46:00.000-08:00

GECCO will be in Dublin in 2011, July 12-16. The main paper submission deadline is January 26, 2011. Full details (including track descriptions) are at: http://www.sigevo.org/gecco-2011/

Deadline extended for Special Issue on Evolutionary Algorithms for Data Mining

2010-12-05T17:49:00.000-08:00

The submission deadline for the Special Issue on Evolutionary Algorithms for Data Mining has been extended by 15 days. The new sunmission deadline is January 1st, 2011. The CFP (including the other dates) remains otherwise unchanged.

BibTex citations available from SpringerLink

2010-08-27T13:11:00.000-07:00

With the new version of SpringerLink that runs the journal's website you can now get citations for articles in BibTex format, for easy inclusion in new articles prepared with LaTex. From the main page you can get to article listings through the search field or links such as Online First Articles or Current Issue. Then when you click on a particular article's title you'll get a page with the abstract and (among other things) an "EXPORT CITATION" link. Click that and then set the options as follows:

Export: Citation Only

Select Format: Plain Text

Select Citation Manager: BibTex

Then when you click "EXPORT CITATION" it will download a text file containing the citation in BibTex.

The other options should help those of you using other citation formats or citation managers, but since several people had specifically requested support for BibTex I'm quite happy to see that this is working.

David Goldberg wins EC Pioneer Award

2010-07-28T02:51:00.000-07:00

Many congratulations to David Goldberg (Advisory Board Member of GPEM) who has been awarded the IEEE's Computational Intelligence Society Evolutionary Computation Pioneer Award at IEEE World Congress on Computational Intelligence in Barcelona last week.

CFP: Special Issue on Evolutionary Algorithms for Data Mining

2010-07-11T15:20:00.000-07:00

Call for Papers: Special Issue on Evolutionary Algorithms for Data Mining

Journal: Genetic Programming and Evolvable Machines (pub. by Springer)

Guest Editors: Pierre Collet, Université de Strasbourg, France; Man Leung Wong, Lingnan University, Hong Kong

Editor-in-Chief: Lee Spector, Hampshire College

The corporate and scientific communities are overwhelmed with an influx of data that is stored in on-line databases. Analyzing these data and extracting meaningful information in a timely fashion is intractable without computer assistance and powerful analytical tools. Data Mining is defined as the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in databases (Fayyad et al. 1996). It is an active research area with promise for high payoffs in many business and scientific applications including direct marketing, trend analysis, fraud detection, stock price forecasting, option trading, bond rating, portfolio management, shopping patterns analysis, and medical diagnosis (Simoudis et al. 1996, Groth 1998).

The future of scientific computing is parallel and Artificial Evolution is inherently parallel. Therefore, all the recent successes in Data Mining obtained using Genetic Programming, Genetic Algorithms, Evolutionary Programming, Evolution Strategies, etc. bear a great potential. The grand challenge of using Evolutionary Algorithms for Data Mining is to automatically process large quantities of raw incomplete data with noisy information, identify the most significant and meaningful information/knowledge and present it for achieving the user's goals.

The aim of this special issue is to provide authors with a possibility to publish their work in a renowned journal and the reader with an understanding of the grand challenge, novel approaches in tackling the grand challenge, and some real-life applications, be they already parallelized or not.

We encourage submission of high quality papers (original work that has neither appeared in, nor is under consideration by, other journals), both theoretical and empirical, discussing novel Evolutionary Algorithms for Data Mining. Practical papers that describe successful applications of Evolutionary Algorithms for challenging real-life data mining problems are also sought. Subjects will include (but are not limited to):

- Parallel evolutionary algorithms for data mining

- Data mining from incomplete, imprecise, noisy, imbalanced, and huge databases

- Evolutionary algorithms for cost-sensitive data mining

- Evolutionary ensemble techniques for data mining

- Evolutionary algorithms for supervised and unsupervised classification of data

We encourage all prospective authors to contact the guest editors, at the address below, as early as possible, to indicate your intention to submit a paper to this special issue.

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Manuscripts should be submitted to: http://GENP.edmgr.com. Choose "Evol. Algorithms for Data Mining" as the article type when submitting.

Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.

== Guest Editors:

Pierre Collet, Université de Strasbourg, France; pierre.collet@unistra.fr

Man Leung Wong, Lingnan University, Hong Kong. mlwong@ln.edu.hk

== Important Dates:

Paper submission deadline: Dec 15, 2010

First Notification: March 15, 2011

Final manuscript: April 30, 2011

== References:

Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery in Data Mining, pp. 1-34. Menlo Park, CA: AAAI Press.

Groth, R. (1998). Data Mining: A Hands-On Approach for Business Professionals. Upper Saddle River NJ: Prentice Hall.

Simoudis, E., Han, J., and Fayyd, U. (1996). Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Menlo Park CA: AAAI Press.

GPEM 11:3/4, Anniversary Special Issue, FREE for a limited time

2010-07-03T07:50:00.000-07:00

I am pleased to announce that the Genetic Programming and Evolvable Machines "Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines," edited by Julian Miller and Riccardo Poli, is now available online.

This is a really nice special issue containing some excellent summaries of the state of the field and directions in which it is expected to move in the near future.

I am also happy to announce that the entire issue is FREE online for the month of July, 2010. In addition, two of the issue's articles (the one by Koza and the one by Langdon Gustafson) are published under Open Access and will therefore be free forever.

The articles in this issue are:

"Editorial to tenth anniversary issue on progress in genetic programming and evolvable machines"

by Julian F. Miller and Riccardo Poli

"Human-competitive results produced by genetic programming"

by John R. Koza

"Theoretical results in genetic programming: the next ten years?"

by Riccardo Poli, Leonardo Vanneschi, William B. Langdon and Nicholas Freitag McPhee

"Genetic Programming and Evolvable Machines: ten years of reviews"

by W. B. Langdon and S. M. Gustafson

"Open issues in genetic programming"

by Michael O’Neill, Leonardo Vanneschi, Steven Gustafson and Wolfgang Banzhaf

"Grammar-based Genetic Programming: a survey"

by Robert I. McKay, Nguyen Xuan Hoai, Peter Alexander Whigham, Yin Shan and Michael O’Neill

"Developments in Cartesian Genetic Programming: self-modifying CGP"

by Simon Harding, Julian F. Miller and Wolfgang Banzhaf

Book Review (not part of the special issue per se): "Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies"

by Ivan Garibay

"Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two"

by Marco Tomassini and Leonardo Vanneschi

"An ensemble-based evolutionary framework for coping with distributed intrusion detection"

by Gianluigi Folino, Clara Pizzuti and Giandomenico Spezzano

"Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms"

by Garnett Wilson and Wolfgang Banzhaf

"Simdist: a distribution system for easy parallelization of evolutionary computation"

by Boye Annfelt Høverstad

"Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm"

by Ting Hu, Simon Harding and Wolfgang Banzhaf

"EvAg: a scalable peer-to-peer evolutionary algorithm"

by J. L. J. Laredo, A. E. Eiben, M. van Steen and J. J. Merelo

GPEM impact factor!

2010-06-24T11:53:00.000-07:00

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

SIGEVOlution Volume 5, Issue 1, is now available

2010-05-27T03:16:00.000-07:00

The SIGEVOlution newsletter Volume 5 Issue 1 is now available for download from: http://www.sigevolution.org

The new issue features:

EC Testing of Embedded Systems by Peter M. Kruse, Joachim Wegener
and Stefan Wappler
Competitions @ GECCO-2010 by Christian Gagné
Events reports: NICSO-2010 by David Pelta
Dissertation Corner
New issues of journals
Calls and calendar

The newsletter is intended to be viewed electronically.

Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

CFP: Special Issue on Evolvable Hardware Challenges

2010-05-26T04:30:00.000-07:00

Call for Papers: Special Issue on Evolvable Hardware Challenges

Journal: Genetic Programming and Evolvable Machines (pub. by Springer)

Guest Editor: Pauline C. Haddow, Norwegian Univ. of Science and Technology

Editor-in-Chief: Lee Spector, Hampshire College

Evolvable Hardware, the application of evolutionary techniques as hardware design techniques, is still in its infancy despite a 15 year lifespan. After the initial excitement in the late 1990s there have been many successes but perhaps not at the rate or to the extent of the original expectations. There are many challenges inherent in Evolvable Hardware that are currently being addressed or need to be addressed so as to unlock the true potential of the field. Such work, together with research on real world applications, will lead to a clearer definition of the field and thus pave a future path for Evolvable Hardware. The aim of this special issue is to provide the reader with contributions that we feel provide strong contributions towards this goal.

Two articles by leading researchers have already been commissioned:

- The Evolution of Standard Cell Libraries for Future Technology Nodes

James Walker, James Hilder & Andy Tyrrell

- An Evolved Anti-Jamming Antenna Beamforming Network

Jason Lohn, Derek Linden & Jonathan Becker

== Open submissions

We encourage submission of high quality papers, both theoretical and practical, describing newer approaches that address key challenges facing Evolvable Hardware today. Application papers that illustrate that Evolvable Hardware can achieve results that are challenging for today’s more traditional hardware design techniques are also sought. In addition, we are interested in contributions that address the computational design challenge in tomorrow’s technologies through the application of bio-inspired techniques. Subjects will include (but are not limited to):

- Evolvable hardware design

- Adaptive hardware

- Evolutionary robotics

- Formal models of bio-inspired hardware

- Generative and developmental approaches

- Real-world applications of evolvable hardware

- Bio-inspired computation on future technology

We encourage all prospective authors to contact the guest editor, at the address below, as early as possible to indicate your intention to submit a paper to this special issue.

Guest Editor:

Pauline C. Haddow pauline@idi.ntnu.no

Dept. of Computer and Information Science

The Norwegian University of Science and Technology

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Manuscripts should be submitted to: http://GENP.edmgr.com. Choose "Evolvable Hardware" as the article type when submitting.

== Important Dates:

Paper submission deadline: Sept, 1, 2010

Notification of acceptance: Oct 8, 2010

Final manuscript: Oct 29, 2010

Evolutionary computation in Communications of the ACM

2010-05-25T14:01:00.000-07:00

The latest Communications of the ACM includes an article on "Automatic Program Repair with Evolutionary Computation" by Westley Weimer, Stephanie Forrest, Claire Le Goues, and ThanhVu Nguyen. This article is based on very nice work which, as noted last summer on this blog, won the top prize in the Human-Competitive Results Competition (the "Humies") at GECCO-2009.

(Hat tip to Bill Langdon.)

#gptp on twitter

2010-05-21T13:13:00.001-07:00

Interesting presentations and discussions taking place at the Genetic Programming Theory and Practice workshop in Ann Arbor, and a couple of people are tweeting highlights. Search for #gptp at twitter.com to see what has been going on.

GPEM 11(2) now available online

2010-04-24T13:00:00.000-07:00

The second issue of volume 11 of Genetic Programming and Evolvable Machines is now available online. This is Part 2 of the special issue on parallel and distributed evolutionary algorithms, and it contains the following articles:

"Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two"

by Marco Tomassini and Leonardo Vanneschi

"An ensemble-based evolutionary framework for coping with distributed intrusion detection"

by Gianluigi Folino, Clara Pizzuti and Giandomenico Spezzano

"Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms"

by Garnett Wilson and Wolfgang Banzhaf

"Simdist: a distribution system for easy parallelization of evolutionary computation"

by Boye Annfelt Høverstad

"Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm"

by Ting Hu, Simon Harding and Wolfgang Banzhaf

"EvAg: a scalable peer-to-peer evolutionary algorithm"

by J. L. J. Laredo, A. E. Eiben, M. van Steen and J. J. Merelo

GECCO-2010 competitions announced

2010-04-24T12:56:00.000-07:00

The announcement for the GECCO-2010 competitions is out, and it looks good!

GP for self-replication in cellular automata

2010-04-19T18:14:00.000-07:00

A new piece on the use of genetic programming for self-replication in cellular automata, by Zhijian Pan and James A. Reggia, has been published in the journal Artificial Life. Previous work on this topic using non-GP genetic algorithms was intriguing, but I always thought that GP could produce even more interesting results. This new article appears to bear that out.

GP (by any name) on RadioLab

2010-04-13T19:41:00.000-07:00

Hod Lipson and Michael Schmidt were on the ever-entertaining RadioLab (in the last segment of this show on "Limits"), discussing their remarkable work on "Distilling Free-Form Natural Laws from Experimental Data" that was published in Science last year. Although I don't think they call it GP explicitly in the radio show or even in the main Science article, GP is central to their work, as is explained in detail in the article's Supporting Online Material (links to Science are not free).

SIGEVOlution Volume 4, Issue 4, is now available

2010-03-30T03:08:00.000-07:00

The SIGEVOlution newsletter Volume 4 Issue 4 is now available for download from: http://www.sigevolution.org

The new issue features:

Galactic Arms Race by Erin J. Hastings & Kenneth O. Stanley
A Perl Primer for EA Practitioners by Juan-Julián Merelo
New issues of journals
Calls & calendar

The newsletter is intended to be viewed electronically.

Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

SIGEVOlution Volume 4, Issue 3, is now available

2010-02-17T12:09:00.000-08:00

New issues of the SIGEVOlution newsletter are appearing quite rapidly and the latest---Volume 4 Issue 3---is now available for you to download from: http://www.sigevolution.org

The new issue features:

Issues in Applying Computational Intelligence by Arthur Kordon
JavaXCSF by Patrick O. Stalph & Martin V. Butz
Dissertation corner
New issues of journals
Calls & calendar

The newsletter is intended to be viewed electronically.

Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

GPEM 11(1) hardcopy -- new color!

2010-02-17T12:07:00.000-08:00

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

FOGA XI CFP

2010-02-07T07:57:00.000-08:00

The 11th Foundations of Genetic Algorithms conference will be held on January 5-9, 2011 in Schwarzenberg, Austria. Submissions (10-12 pages) on the theoretical foundations of any type of evolutionary computation can be emailed to foga@fhv.at. The deadline for submissions is July 5, 2010. For more details see http://www.sigevo.org/foga-2011.

GPEM 11(1) now available online

2010-01-27T20:42:00.000-08:00

The first issue of volume 11 of Genetic Programming and Evolvable Machines is now available online, with the following articles:

"Editorial Introduction" and "Acknowledgments" by Lee Spector

"The influence of mutation on population dynamics in multiobjective genetic programming"

by Khaled Badran & Peter I. Rockett

"Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure"

by Vyung-Joong Kim, Adrian Wong & Hod Lipson

"GP challenge: evolving energy function for protein structure prediction" by Pawel Widera, Jonathan M. Garibaldi & Natalio Krasnogor

"The identification and exploitation of dormancy in genetic programming" by David Jackson

"Book Review: Michael Affenzeller, Stefan Wagner, Stephan Winkler and Andreas Beham: Genetic algorithms and genetic programming modern concepts and practical applications" by Gisele L. Pappa

"Book Review: Melanie Mitchell: Complexity a guided tour"

by Felix Streichert

SIGEVOlution Volume 4, Issue 2, is now available

2010-01-18T10:46:00.000-08:00

The new issue of the SIGEVOlution newsletter, Volume 4 Issue 2, is now available for you to download from: http://www.sigevolution.org

The new issue features:

45 Years of Evolution Strategies: Hans-Paul Schwefel Interviewed for the Genetic Argonaut Blog
CIG-2009
Dissertation Corner
Calls & calendar

The newsletter is intended to be viewed electronically.

Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

GECCO-2010 deadline extended

2010-01-07T06:32:00.000-08:00

The submission deadline for the 2010 Genetic and Evolutionary Computation Conference (GECCO-2010) has been extended to January 27, 2010. See the conference web site for more details.

Bacteria that turn gears

2009-12-27T20:14:00.000-08:00

An interesting inversion of several of the themes covered in this journal:

http://news.yahoo.com/s/livescience/20091226/sc_livescience/scientistsharnessbacteriatoturnmicroscopicgears

It would be even more interesting if gear-turning performance drove selection...

-Lee

SIGEVOlution Volume 4, Issue 1, is now available

2009-11-09T04:59:00.000-08:00

The new issue of the SIGEVOlution newsletter, Volume 4 Issue 1, is now available for you to download from: http://www.sigevolution.org

The new issue features:

"Computational Intelligence Marketing" by Arthur Kordon
"Pyevolve: a Python Open-Source Framework for Genetic Algorithms" by Christian S. Perone
Calls & calendar

The newsletter is intended to be viewed electronically.

Thanks to Pier Luca Lanzi, SIGEvolution Editor-in-Chief.

GPEM 10(4) now available online

2009-11-06T16:35:00.000-08:00

The fourth issue of volume 10 of Genetic Programming and Evolvable Machines is now available online. This is the first part of the two-part Special Issue on Parallel and Distributed Evolutionary Algorithms, and it contains the following articles:

Introduction: special issue on parallel and distributed evolutionary algorithms, part I

by Marco Tomassini & Leonardo Vanneschi

Distributed differential evolution with explorative–exploitative population families

by Matthieu Weber, Ferrante Neri & Ville Tirronen

A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

by V. G. Asouti, I. C. Kampolis & K. C. Giannakoglou

Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework

by Asim Munawar, Mohamed Wahib, Masaharu Munetomo & Kiyoshi Akama

Parallel evolution using multi-chromosome cartesian genetic programming

by James Alfred Walker, Katharina Völk, Stephen L. Smith & Julian Francis Miller

Genetic programming on graphics processing units

by Denis Robilliard, Virginie Marion-Poty & Cyril Fonlupt

Book Review: Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots

by Navneet Bhalla

Deadline extended for Tenth Anniversary Special Issue on Progress in Genetic Programming and Evolvable Machines

2009-09-25T17:06:00.000-07:00

The deadline has been extended for submissions to the Tenth Anniversary Special Issue on Progress in Genetic Programming and Evolvable Machines; see the call for papers for details.

New issue of SIGEVOlution

2009-09-22T04:42:00.000-07:00

The new issue of SIGEVOlution is now available for you to download from:

http://www.sigevolution.org

The issue features:

An Interview with Hans-Paul Schwefel with an introduction by Günter Rudolph
Memetic Algorithms by Natalio Krasnogor
Learning From Failures in Evolutionary Computation @ GECCO-2009
new issues of journals
calls & calendar

Journal Publication versus Conference Contribution?

2009-08-11T17:01:00.000-07:00

In a recent issue of the Communications of the ACM, Moshe Vardi discusses the pros and cons of journal archival publications versus conference contributions. The upshot of his statement, which points to two recent contributions to the viewpoint columns of the journal [1], [2] is that perhaps it is time for Computer Scientists to shift emphasis away from conference and workshop contributions, and start publishing in journal as all other sciences do. A lively discussion followed, see among others, the opinion piece of Lance Fortnow.

As an editor myself of Genetic Programming and Evolvable Machines I have always wondered why it would be more attractive for people in our discipline to publish in conference venues than in archival journals. Are there not enough journals to allow for scientific progress? Or is there a dire need to communicate with colleagues in spatial co-location? Well, to my mind, none of the two! We are not the types of people that wanted to discuss our results to extreme length. Our conferences and workshops usually operate under tight time constraints, and one to three questions is about the average a presenter receives, anything else would eat into the next presenter's time and is discouraged. Also, the number of journals now accepting work from our field has grown over the years to a very reasonable number so that there is no shortage of places where quality work could find a home.

What is it then, that makes us submit and publish so much at conferences? Possible explanations are the existence of deadlines and the incremental nature of much of the work published. The existence of deadlines is a valuable selection pressure in our hectic times where everything is under the dictate of time-driven priorities. It can only be mimicked by journals through the introduction of regular "special issues" which also come with this requirement, and usually are successful in attracting work. As for the second possible explanation, I'd like to cite from [1] on the pitfalls of program committee work: "And arguably it is the more innovative papers that suffer because they are time consuming to read and understand, so they are the most likely to be either completely misunderstood or underappreciated by an increasingly error-prone process." So while innovative work has a harder time at conferences, "our culture creates more units to review with a lower density of new ideas." It is not only that we get to review smaller pieces of work, we are also more busy, with all the workshops and conferences that make us look at these papers. "Genuinely innovative papers that have issues, but could have been conditionally accepted, are all too often rejected in this climate of negativism. So the less ambitious, but well-executed work trumps what could have been the more exciting result." Those would have to be revised and revised and revised again, and there is no time to do this for conferences. Journal articles, on the other hand, can be worked on for a long time, if need be, and there is no time pressure except for the fact that delays could be unbearable and make results obsolete.

In the end, however, it is the impact of the work that counts most. And it is my experience that a carefully edited journal paper is worth the effort, as it produces impact on a scale that conference papers have diffulty to achieve.

[1] K. Birman and F.B. Schneider. Comm. ACM, 52(5) 2009, p. 34
[2] J. Crowcroft, S. Keshav, and N. McKeown, Comm. ACM, 52(1) 2009, p. 27

GPEM 10(3) now available online

2009-07-28T12:17:00.000-07:00

The third issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:

A three-step decomposition method for the evolutionary design of sequential logic circuits

by Houjun Liang, Wenjian Luo and Xufa Wang

Evolutionary design of evolutionary algorithms

by Laura Dioşan and Mihai Oltean

Semantic analysis of program initialisation in genetic programming

by Lawrence Beadle and Colin G. Johnson

Additional awards at GECCO-2009

2009-07-18T20:37:00.000-07:00

There were several awards presented at the GECCO-2009 conference aside from the Human-Competitive Results Awards (Humies) awards about which Wolfgang posted previously, and for which I've listed the other winners below. Of particular interest to readers of this blog may be the Best Paper awards from each of the technical tracks; these are generally awarded for exciting new results, several of which may soon be appearing in more complete form in our field's journals. In addition, this year was the first year of the SIGEVO GECCO Impact Award, for the papers with the most citations from the GECCO conference 10 years ago.

Congratulations to all of the winners!

2009 SIGEVO GECCO Impact Awards, for the papers with the most citations from GECCO 1999

M. Pelikan, D. Goldberg, E. Cantu-Paz: "BOA: The Bayesian Optimization Algorithm"

Citations: 447

S. Hofmeyer, S. Forrest: "Immunity by Design: An Artificial Immune System"

Citations: 212

2009 "Humies" Awards for Human-Competitive Results Produced by Genetic and Evolutionary Computation

Humies BRONZE MEDALS

Perez, Olague: "Evolutionary Learning of Local Descriptor Operators for Object Recognition"

AND Hauptpman, Elyasay, Sipper, Karman: "GP to Evolve Solvers for the Rush Hour Problem"

Humies SILVER MEDAL

Shahzad, Zahid, Farooq, Khayam: "GA+PSO for User ID on Smart Phones"

Humies GOLD MEDAL

Forrest, Le Goues, Nguyen, Weimer: "GP for Automated Software Repair"

2009 GECCO Best Paper Awards

Ant Colony Optimization and Swarm Intelligence: "Parallel Shared Memory Strategies for Ant-Based Optimization Algorithms" by T. Bui, T. Nguyen, J. R. Rizzo Jr.

Artificial Life, Evolutionary Robotics, Adaptive Behavior, Evolvable Hardware: "How Novelty Search Escapes the Deceptive Trap of Learning to Learn" by S. Risi, S. D. Vanderbleek, C. E. Hughes, K. O. Stanley

Bioinformatics and Computational Biology Modeling: "Evolutionary Fitness for DNA Motif Discovery" by S. Rahmann, T. Marschall, F. Behler, O. Kramer

Combinatorial Optimization and Metaheuristics: "Fixed-Parameter Evolutionary Algorithms and the Vertex Cover Problem" by S. Kratsch, F. Neumann

Estimation of Distribution Algorithms: "EDA-RL: Estimation of Distribution Algorithms for Reinforcement Learning Problems" by H. Handa

AND

"Approximating the Search Distribution to the Selection Distribution in EDAs" by S. I. Valdez-Peña, A. Hernández-Aguirre, S. Botello-Rionda

Evolution Strategies and Evolutionary Programming: "Efficient Natural Evolution Strategies" by Y. Sun, D. Wierstra, T. Schaul, J. Schmidhuber

Evolutionary Multiobjective Optimization: "Multiplicative Approximations and the Hypervolume Indicator" by T. Friedrich, C. Horoba, F. Neumann

Generative and Developmental Systems: "The Sensitivity of HyperNEAT to Different Geometric Representations of a Problem" by J. Clune, C. Ofria, R. T. Pennock

Genetic Algorithms: "Tunneling Between Optima: Partition Crossover for the Traveling Salesman Problem" by D. Whitley, A. Howe, D. Hains

Genetic Programming: "A Genetic Programming Approach to Automated Software Repair" by S. Forrest, T.V. Nguyen, W. Weimer, C. Le Goues

Genetics-Based Machine Learning: "Learning Sensorimotor Control Structures with XCSF" by M. V. Butz, G. K. M. Pedersen, P. O. Stalph

AND

"New Entropy Model for Extraction of Structural Information from XCS Population" by W. K. Park, J. C. Oh

Parallel Evolutionary Systems: "Strategies to Minimise the Total Run Time of Cyclic Graph Based Genetic Programming with GPUs" by T. E. Lewis, G. D. Magoulas

Real World Applications: "Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm" by F.-M. De Rainville, C. Gagné, O. Teytaud, D. Laurendeau

Search Based Software Engineering: "Software Project Planning for Robustness and Completion Time in the Presence of Uncertainty using Multi Objective Search Based Software Engineering" by S. Gueorguiev, M. Harman, G. Antoniol

Theory: "Dynamic Evolutionary Optimisation: An Analysis of Frequency and Magnitude of Change" by P. Rohlfshagen. P. K. Lehre, X. Yao

GECCO Graduate Student Workshop: "Learnable Evolution Model Performance Impaired by Binary Tournament Survival Selection" by M. Coletti (George Mason University)

GECCO Humies Award (GOLD) 2009

2009-07-14T07:09:00.000-07:00

GP was well featured at this year's GECCO Humies Awards. The most spectacular application which was subsequently awarded first prize (GOLD) was based on two papers by Weimer/Nguyen/Le Goues/Forrest
published in proceedings of the 31st International Conference on Software Engineering (ICSE) in May 2009 and Forrest/Weimer/Nguyen/Le Goes in this year's GECCO proceedings. Both papers won awards from the respective conferences, and winning the Humies award was the "icing on the cake".

The authors apply a specialized/improved form of Genetic Programming to locate and repair software bugs. Repairing software bugs is a time consuming and commercially very costly activity. To date, automating the process has been very difficult. The GP method proposed by our Gold Medal winners takes down the average repair time for software bugs from more than 3 hours per bug to 3 minutes.

The authors rightly claim that "showing how to use GP in the context of modern software systems and integrating GP into modern software practice will help evolutionary computation to become more widely accepted by computer scientists."

Congratulations to the authors for a prize well deserved!

Award for David E. Goldberg

2009-07-02T05:21:00.000-07:00

David E. Goldberg has been awarded an Evolutionary Computation Pioneer Award by the Computational Intelligence Society. More details here. Well-deserved congratulations to David, who has given so much to our field!

SIGEVOlution Volume 3, Issue 3, is now available

2009-06-23T07:30:00.000-07:00

I'm a little late with this post because I couldn't reach the blog from China (where I was for the 2009 World Summit on Genetic and Evolutionary Computation). Aside from the web access issues it was an interesting conference and I had a great visit in China more generally. I'm now in Tokyo where I'll visit GPEM associate editor Hitoshi Iba tomorrow, and the only web challenge appears to be in getting Blogger's menus to appear in English rather than Japanese... but I've managed.

Anyway, in the interim I have received mail from Pier Luca Lanzi informing me that the latest issue of SIGEVOlution, the SIGEVO newsletter, has just been released. It is available from http://www.sigevolution.org and features:

An Interview with John H. Holland with an introduction by Lashon Booker
It's Not Junk! by Clare Bates Congdon, H. Rex Gaskins, Gerardo M. Nava & Carolyn Mattingly
Car racing @ CIG-2008
GECCO-2009 competitions
New issues of journals
Calls & calendar

Don't be confused by the "Autumn 2008" cover date. It is indeed a new issue that just came out in June, 2009, but the volume/date correspondence has slipped (and will probably be adjusted soon).

Editorial board renewed

2009-06-04T19:05:00.000-07:00

We have just completed the "renewal" process for the Genetic Programming and Evolvable Machines editorial board, the most exciting aspect of which is that we now have a new Associate Editor, Pauline C. Haddow (of The Norwegian University of Science and Technology, Norway) and four new regular members of the editorial board: Marc Ebner (of Eberhard Karls Universität Tübingen, Germany), Jason H. Moore (of Dartmouth Medical School, USA), Sara Silva (of Universidade de Coimbra, Portugal), and Tina Yu (of Memorial University of Newfoundland, Canada). Thanks to all of the continuing associate editors for helping with this process, and welcome to the new editors! I think that the journal will be even stronger with these additions.

The full editorial board can be found here.

23 submissions to special issue

2009-05-30T14:46:00.000-07:00

We received 23 submissions to the Special Issue on Parallel and Distributed Evolutionary Algorithms. This is a very healthy number, indicating strong interest in the area and good prospects for an exciting special issue. Congratulations to guest editors Marco Tomassini and Leonardo Vanneschi, thanks to all of the submitters, and thanks in advance to all of the reviewers!

Search related journals

2009-05-21T15:20:00.000-07:00

You can use the following forms to search for text in GPEM-related journals via Google Scholar. Google Scholar doesn't make it easy to do this perfectly, so I have employed some tricks and you still may get some false hits. This should nonetheless be useful in helping you to find and cite related work.

Artificial Life
BioSystems
Complex Systems
Evolutionary Computation
Genetic Prog. and Evol. Mach.
IEEE Trans. on Evol. Comp.
J. Machine Learning Research
Machine Learning

Origins of life research and evolutionary computing

2009-05-14T13:02:00.000-07:00

A considerable amount of research in genetic and evolutionary computing is concerned to some degree with self-adaptation -- that is, with the adaptation and improvement of an evolutionary system over evolutionary time. (Try searching for "self-adaptive" in the GPEM journal search and GP-bibliography search boxes on the left.) This work connects not only to research in evolutionary biology but also to research on the origins of life, since it is concerned with the ways in which adaptive systems can themselves arise and become more adaptive.

In this context it is interesting to see today's announcement of an apparent breakthrough in origins of life research, on a possible scenario for the emergence of RNA on prebiotic Earth. This is work by Matthew W. Powner, Beatrice Gerland, and John D. Sutherland at the University of Manchester. There's a write-up in the New York Times, and the full report and a commentary by Jack W. Szostak are available in today's Nature (subscription required for full text).

Among the reasons this might interest GPEM readers is the fact that the discovery was made through an intensive search of the space of chemical reaction sequences. This may be a search space within which genetic and evolutionary computation can help to find new and interesting things, if the right kinds of computational chemistry simulation systems (of which there are many) can be used for fitness testing on with the right kinds of problems. Putting all of this together to make significant discoveries will be non-trivial, but it seems to me to have potential.

Incidentally, searching for "origins" or "chemistry" in the journal, using the top search box on the left, produces several items of related interest that were published previously in GPEM.

CFP: Tenth Anniversary Special Issue on Progress in Genetic Programming and Evolvable Machines

2009-05-14T08:44:00.000-07:00

Genetic Programming and Evolvable Machines

Tenth Anniversary Special Issue on Progress in Genetic Programming and Evolvable Machines

(Revised May 19, 2009; please note revised title and deadlines. 2nd revision July 15, 2009. 3rd revision September 25, 2009; please note revised schedule)

Genetic Programming and Evolvable Machines is ten years old in 2010. To mark this, a prestigious special issue of the journal will be published. A number of articles by leading figures have already been commissioned:

"Theoretical Results in Genetic Programming: The next ten years?" by Riccardo Poli, William B. Langdon, Nic McPhee and Leonardo Vanneschi
"Human Competitive Results Using Genetic Programming" by John Koza
"Genetic Programming and Evolvable Machines: Ten Years of Reviews" by William B. Langdon and Steven Gustafson

Open submissions

We encourage the submission of high quality papers that review or analyze progress in the field, present the state-of-the-art in the evolution of software and hardware, describe promising new approaches or application areas, or foundational topics in genetic programming and evolvable machines.

Subjects include, but are not limited to:

- Theoretical understanding of Genetic Programming

- Important Application Areas of Genetic Programming and Evolvable Machines

- New approaches and paradigms

- Fundamental Issues

- Wide ranging reviews and/or analysis of Research in Genetic and Evolvable Machines

Important Dates

- Paper submission deadline: November 23, 2009

- Notification of acceptance: January 15, 2009

- Final manuscript: February 15, 2010

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.All open submissions will be peer reviewed subject to the standards of the journal. Manuscripts based on previously published conference papers must be extended substantially.

Manuscripts should be submitted to: http://GENP.edmgr.com. This online system offers easy and straightforward log-in and submission procedures, and supports a wide range of submission file formats.

All enquiries on this special issue by prospective authors should be sent to the guest editors at the addresses below.

Guest editors

Julian Miller

Department of Electronics

University of York,

Heslington, York,

YO10 5DD, UK

jfm7@ohm.york.ac.uk

Riccardo Poli

School of Computer Science and Electronic Engineering,

University of Essex,

Wivenhoe Park, Colchester,

CO4 3SQ, UK

rpoli@essex.ac.uk

Editor-in-Chief: Lee Spector, Hampshire College

Founding Editor: Wolfgang Banzhaf, Memorial University of Newfoundland

Journal Website: www.springer.com/10710

The Genie in the Machine: How Computer-Automated Inventing is Revolutionizing Law and Buisness

2009-05-05T13:33:00.000-07:00

Robert Plotkin has just published a new book for general readers on computer-automated invention and its legal and business implications. I haven't yet read it all the way through but I see that it focuses quite heavily on invention by means of genetic and evolutionary computation. The author consulted with many researchers in developing the ideas -- including myself and several other GPEM editors and authors, listed in the acknowledgments -- so I think that he is well informed about the underlying science and engineering.

The book is The Genie in the Machine: How Computer-Automated Inventing is Revolutionizing Law and Business, published by Stanford University Press, May 2009, ISBN 978-0804756990.

Deadline extended for Special Issue on Parallel and Distributed Evolutionary Algorithms

2009-04-29T05:10:00.000-07:00

The deadline for submitting papers to the Genetic Programming and Evolvable Machines Special Issue on Parallel and Distributed Evolutionary Algorithms has been extended.

The new deadline is: May 15, 2009

More information about the special issue is available here.

Evolution of Modularity, GP, and a new PLoS Computational Biology paper by Kashtan et al.

2009-04-13T08:20:00.000-07:00

A new paper by Kashtan et al. in PLoS Computational Biology presents an interesting study of the evolution of modularity, extending their previous work showing "that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals."

The evolution of modularity is a topic of longstanding interest in GP and evolutionary computation more generally, within which we often seek to evolve modular programs or structures. Many also seek to leverage the modularity of representations to accelerate evolution. A lot of the work on automatically defined functions, etc., has been concerned with these issues and I think that cross-fertilization with the new computational biology results could be fruitful.

The closest thing that I know of in the GP literature to the Kashtan et al. results is a paper by Terry Van Belle and David Ackley in GECCO 2002, in which they observed the "evolution of evolvability in experiments using genetic programming to solve a symbolic regression problem that varies in a partially unpredictable manner." Alan Robinson and I were inspired by this to do a similar experiment in PushGP, which allows modularity to arise from scratch via code self-manipulation, and we wrote it up briefly in a GECCO 2002 Workshop paper (see section 3.2).

GPEM 10(2) now available online

2009-04-10T05:53:00.000-07:00

The second issue of volume 10 of Genetic Programming and Evolvable Machines is now available online, containing the following articles:

Incorporating characteristics of human creativity into an evolutionary art algorithm

by Steve DiPaola, Liane Gabora

Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

by Stephan M. Winkler, Michael Affenzeller, Stefan Wagner

Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

by Sara Silva, Ernesto Costa

A review of procedures to evolve quantum algorithms

by Adrian Gepp, Phil Stocks

Book Review: Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming

by Michael O’Neill

Website for The Art of Artificial Evolution

2009-04-01T11:48:00.000-07:00

A nice website has been set up for The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, a book edited by Juan Romero and Penousal Machado that was reviewed by Jeroen Eggermont in GPEM 10(1).

Medical applications as a growth area for genetic and evolutionary computing

2009-03-20T07:27:00.000-07:00

Within the last week we've been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

Our special issue on "Medical Applications of Genetic and Evolutionary Computation" (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue -- for example we published "Use of genetic programming to diagnose venous thromboembolism in the emergency department" by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 ("Genetic programming for medical classification: a program simplification approach" by Mengjie Zhang and Phillip Wong, and "Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks" by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: "Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis" by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.

I think that there's a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.

GPEM 10(1) hits the streets

2009-02-25T08:38:00.000-08:00

My hardcopy arrived in my mailbox today and it looks good! If you have a subscription yours should arrive soon.

Evolution of life in 60 seconds

2009-02-14T09:35:00.000-08:00

Via seedmagazine.com, a very nice animation of the time scale of biological evolution on Earth. I think it makes its point beautifully, with an aesthetic that echoes Powers of Ten and The Outer Limits. The shape of the underlying curve is probably worth keeping in mind for artificial evolutionary systems as well.

GECCO conference highly ranked

2009-02-13T03:46:00.000-08:00

According to the rankings at this site, the Genetic and Evolutionary Computation Conference (GECCO) ranks 11th out of 701 considered conferences in "Artificial Intelligence / Machine Learning / Robotics / Human Computer Interaction." The rankings are based on citation of papers, quality of referees' reports, availability of resources to students by the conference, conference papers accepted/appeared in reputable journals after the conference, and indexing (details here).

CFP: Special Issue on Parallel and Distributed Evolutionary Algorithms

2009-02-12T04:57:00.000-08:00

Genetic Programming and Evolvable Machines

Special Issue on Parallel and Distributed Evolutionary Algorithms

(Revised March 27, 2009; please note revised submission procedures.)

(Revised April 29, 2009; extended submission deadline.)

Genetic Programming, and Evolutionary Computation at
large have been extremely successful in the last decade across
a wide range of problems and applications. Current applications are
characterized by an ever growing complexity and a pronounced
distributed nature. While the use of centralized or hierarchical
architectures and algorithms has been dominant so far, they are
now becoming impractical because they have poor scalability and
fault-tolerance characteristics. Since evolutionary algorithms are
ideally suited to population partitioning and structuring, distributed
and parallel approaches appear to be a natural way to
cope with the growing computational burden associated with large
problems.

The aim of this Special Issue is to provide the reader with
contributions discussing recent advances and an indication of
future trends in the theory, development, and application of
parallel and distributed evolutionary algorithms. We encourage
submission of papers describing new concepts, models, and
strategies, along with papers describing systems and tools that
provide practical implementations. Papers describing either
hardware or software aspects of parallel and distributed
architectures are welcome. In addition, we are interested in
application papers discussing the power and applicability of these
parallel methods to real-world problems in any area of interest,
such as evolutionary design, optimization, and emerging fields
such as computational biology.

Subjects will include (but are not limited to):

- parallel and distributed evolutionary algorithms models

- theory of structured evolutionary algorithms

- performance evaluation of parallel and distributed
evolutionary algorithms

- applications of parallel and distributed evolutionary computing

- parallel and distributed implementations: software and
hardware aspects

Important dates:

* Paper submission deadline: May 15, 2009 [extended from April 30, 2009]
* Notification of acceptance: June 30, 2009
* Final manuscript: August 31, 2009

Paper Submission:

Authors are encouraged to submit high-quality, original work

that has neither appeared in, nor is under consideration by, other

journals. All submissions will be peer reviewed subject to the

standards of the journal. Manuscripts based on previously

published conference papers must be extended substantially.

Springer offers authors, editors and reviewers of Genetic

Programming and Evolvable Machines a web-enabled online

manuscript submission and review system. Our online system

offers authors the ability to track the review process of their

manuscript.

Manuscripts should be submitted to: http://GENP.edmgr.com. This

online system offers easy and straightforward log-in and submission

procedures, and supports a wide range of submission file formats.

All enquiries on this special issue by perspective authors should
be sent to the guest editors at the addresses below.

Guest editors:

Marco Tomassini
Information Systems Institute
University of Lausanne, Lausanne, Switzerland
marco.tomassini@unil.ch
Tel: +41 21 6923589

Leonardo Vanneschi
Department of Informatics, Systems and Communication (D.I.S.Co.)
Building U14, Office n. 2004
viale Sarca, 336
University of Milano-Bicocca, Milano, Italy
vanneschi@disco.unimib.it
Tel.: +39 02 64487874

Editor-in-Chief: Lee Spector, Hampshire College
Founding Editor: Wolfgang Banzhaf, Memorial University of Newfoundland
Journal Website: www.springer.com/10710

Evolectronica

2009-02-11T17:56:00.000-08:00

Bob MacCallum has rolled out his new evolutionary music website -- using genetic programming and human fitness assessment -- just in time for Darwin's 200th birthday. The interface still isn't 100% smooth (on my mac at least), but it works and it produces some interesting output. Check it out at http://evolectronica.com.

Get GPEM tables of contents by email

2009-02-08T11:40:00.000-08:00

It is easy to sign up for the GPEM "Table of Contents Alert" service, which will send you an email whenever a new issue is published, listing the table of contents for the issue. Just go to Springer's GPEM page and type your email address in the "Table of Contents Alert" section on the right side of the page.

Evolutionary computing and boron

2009-02-03T11:58:00.000-08:00

Today's New York Times features an article describing a new discovery about the element boron, made in part by evolutionary computing. Full details of the discovery are provided in a January 29, 2009 letter in Nature (subscription required). They (Oganov et al.) used a special purpose evolutionary algorithm called USPEX that is not really described in the Nature piece, but it is described elsewhere including here.

Contents of Volume 10, Number 1

2009-01-31T15:17:00.000-08:00

From the Introduction to Volume 10, Number 1:

The present issue includes three full research articles and two book reviews.

In "Scaling of Program Functionality" W. B. Langdon provides a novel theoretical analysis of the relations between size and functionality for several classes of programs. Many aspects of his analysis apply to all possible systems that search for computer programs, but Dr. Langdon also describes specific implications of his analysis for genetic programming and provides experimental confirmation of his results.

In "An improved representation for evolving programs" M. S. Withall, C.J. Hinde, and R. G. Stone describe a new representation for evolving programs that combines features of traditional linear and tree-based representations. They present the results of several experiments using their new representation and they discuss implications for the scalability of genetic programming to more complex problems.

In "Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming" P. Balasubramaniam and A. Vincent Antony Kumar show how genetic programming can be used to solve differential equations of a particular important class. They compare the genetic programming approach to the traditional Runge Kutta method and they provide experimental confirmation of efficiency improvements.

The book reviews in this issue, edited by W. B. Langdon, cover two edited volumes: The Mechanical Mind in History, which was edited by P. Husbands, O. Holland and M. Wheeler (reviewed by P. Collet), and Evolutionary Computation in Practice: Studies in Computational Intelligence, which was edited by T. Yu, D. Davis, C. Baydar, and R. Roy (reviewed by L. M. Deschaine).