About the GPEMjournal blog

This is the editor's blog for the journal Genetic Programming and Evolvable Machines. The official web site for the journal, maintained by the publisher (Springer) is here. The GPEMjournal blog is authored and maintained by Lee Spector.

Sunday, December 20, 2015

GPEM 16(4) available

GPEM 16(4) is now available. It contains 4 original papers:

"A learning automata-based memetic algorithm"
by M. Rezapoor Mirsaleh and M. R. Meybodi

"Controlling code growth by dynamically shaping the genotype size distribution"
by Marc-Andre Gardner and Christian Gagne and Marc Parizeau

"Prudent alignment and crossover of decision trees in genetic programming"
Matej Sprogar

"Neutral genetic drift: an investigation using Cartesian Genetic Programming"
Andrew Turner and Julian Miller

Finally we finish 2015 with Leonardo Trujillo's review of
Ken Stanley and Joel Lehman's book
"Why greatness cannot be planned: the myth of the objective"
(Remember book reviews are now free to download:-)

As you would expect;-) these are now included in the GP bibliography

Sunday, October 18, 2015

Book and resource reviews unlocked from the pay wall

As some of you may have noticed, newly published book and resource reviews are now being posted on the journal's website without being locked behind the pay wall. This means that they can be accessed by anyone for free, without requiring a personal or institutional subscription. Thanks to the folks at Springer for making this happen.

Monday, August 24, 2015

GPEM 16(3) available

GPEM 16(3) is now available. This issue features THREE resource reviews (thanks both to the authors and to tireless Resource Review Editor Bill Langdon) and four interesting regular articles. Specifically, it contains:

"Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification"
by Rodrigo C. Barros, Márcio P. Basgalupp & André C. P. L. F. de Carvalho

"Evolutionary model building under streaming data for classification tasks: opportunities and challenges"
by Malcolm I. Heywood

"A study on Koza’s performance measures"
by David F. Barrero, Bonifacio Castaño, María D. R-Moreno & David Camacho

"Review and comparative analysis of geometric semantic crossovers"
by Tomasz P. Pawlak, Bartosz Wieloch & Krzysztof Krawiec

Software Review
"Software review: the KNIME workflow environment and its applications in genetic programming and machine learning"
by Steve O’Hagan & Douglas B. Kell

Book Review
"Patricia Vargas, Ezequiel Di Paolo, Inman Harvey, and Phil Husbands (eds), The Horizons of Evolutionary Robotics, The MIT Press, 2014, ISBN: 978-0-262-02676-5, Hardcover book, 302 pages"
by Joel Lehman

Book Review
"Angelo Cangelosi and Matthew Schlesinger: Developmental robotics"
by Lisa A. Meeden

Friday, August 7, 2015

CFP: Special Issue on Genetic Improvement

Special Issue on  Genetic Improvement

Call for Papers

Guest Editor: Justyna Petke, University College London, London;  j.petke@ucl.ac.uk

Genetic Improvement is the application of evolutionary and search-based optimisation methods to the improvement of existing software. For example, it may be used to automate the process of bug-fixing or to minimise bandwidth, memory or energy use. Genetic programming can use human-written software as a feed stock for GI and is able to evolve mutant software tailored to solving particular problems. Other interesting areas are automatic software transplantation, as well as “grow-and-graft” genetic programming, where software is incubated outside its target human written code and subsequently grafted into it via genetic improvement.

Work on genetic improvement has resulted in several awards, including three “Humies”, awarded for human-competitive results. This includes the bug fixing work that led to the construction of the GenProg tool [1]. More recently, genetic improvement was able to automatically transplant new functionality into existing software [2], which resulted in a ACM SIGSOFT Distinguished Paper Award at ISSTA 2015.

Scope: We invite submissions on any aspect of genetic improvement, including, but not limited to, theoretical results and interesting new applications.  Suggested topics include automatic:

- bandwidth minimisation
- latency minimisation
- fitness optimisation
- energy optimisation
- software specialisation
- memory optimisation
- software transplantation
- bug fixing
- multi-objective optimisation
- trading between quality and non-functional properties

Important Dates (note: fixed since first posting):
GPEM Special Issue Submission Deadline: 19 December 2015
First Reviews: March 2016
Post Review Submission Deadline: April 2016
Acceptance Notification: June 2016
Camera-ready Paper Deadline: July 2016

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

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.

Manuscripts should be submitted to: http://GENP.edmgr.com.
Choose “ Genetic Improvement ” as the article type when submitting.

1  “A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each” (ICSE 2012) by Claire Le Goues, Michael Dewey-Vogt, Stephanie Forrest* and Westley Weimer (University of Virginia, University of New Mexico*)

2 “Automated Software Transplantation”  (ISSTA 2015) by Earl T. Barr, Mark Harman, Yue Jia, Alexandru Marginean and Justyna Petke (University College London)

Monday, June 1, 2015

Patterns and Tilings Competition

Günter Bachelier asked if I might publicize this to the Genetic Programming and Evolvable Machines community, and I do think that there may be interest.

The goal of this competition is to produce, presumably by evolution, 2D tiling patterns with no gaps or overlaps, with the initial targets being the known tilings that are available in a public database. The competition apparently originated in work in evolutionary art, but it may be relevant to other image processing applications as well. 

For more information see the call for participation and technical details.

Monday, April 13, 2015

GPEM 16(2) available

GPEM 16(2) is now available. It contains:

"Evolving robot sub-behaviour modules using Gene Expression Programming"
by Jonathan Mwaura & Ed Keedwell

"Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression"
by Samaneh Yazdani & Jamshid Shanbehzadeh

"A hierarchical genetic algorithm approach for curve fitting with B-splines"
by C. H. Garcia-Capulin, F. J. Cuevas, G. Trejo-Caballero & H. Rostro-Gonzalez

"Multiobjective optimization algorithms for motif discovery in DNA sequences"
by David L. González-Álvarez, Miguel A. Vega-Rodríguez & Álvaro Rubio-Largo

"Exploring non-photorealistic rendering with genetic programming"
by Maryam Baniasadi & Brian J. Ross

Sunday, March 1, 2015

GPEM 16(1) available

As Bill notes below, GPEM 16(1) is now available. It contains:

"Editorial Introduction"
by Lee Spector

"Training genetic programming classifiers by vicinal-risk minimization"
by Ji Ni & Peter Rockett

"Improving GP generalization: a variance-based layered learning approach"
by Maryam Amir Haeri, Mohammad Mehdi Ebadzadeh & Gianluigi Folino

"GA-based approach to find the stabilizers of a given sub-space"
by Mahboobeh Houshmand, Morteza Saheb Zamani, Mehdi Sedighi & Monireh Houshmand

"A C++ framework for geometric semantic genetic programming"
by Mauro Castelli, Sara Silva & Leonardo Vanneschi

"Introducing a cross platform open source Cartesian Genetic Programming library"
by Andrew James Turner & Julian Francis Miller

by L. Spector

Friday, February 27, 2015