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.

Tuesday, May 31, 2016

CFP: Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation


Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation

Call for Papers

Guest Editors:

Dr. Su Nguyen, Victoria University of Wellington, New Zealand (su.nguyen@ecs.vuw.ac.nz)

Dr. Yi Mei, Victoria University of Wellington, New Zealand (yi.mei@ecs.vuw.ac.nz)

Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand (Mengjie.Zhang@ecs.vuw.ac.nz)

For a formatted PDF version of this CFP click here.

Scheduling and combinatorial optimisation problems appear in many practical applications in production and service industries and have been the research interest of researchers from operations research and computer science. These problems are usually challenging in terms of both complexity and dynamic changes, which requires the development of innovative solution methods. Although the research in this field has made a lot of progress, designing effective algorithms/heuristics for scheduling and combinatorial optimisation problems is still a hard and tedious task. In the last decade, there has been a growing interest in applying computational intelligence (particularly evolutionary computation) techniques to help facilitate the design of scheduling algorithms and many state-of-the-art methods have been developed.

This special issue aims to present the most recent advances in scheduling and combinatorial optimisation with a special focus on automated heuristic design and self-adaptive algorithms. This includes (1) offline approaches to automatically discover new and powerful algorithms/heuristics for scheduling and combinatorial optimisation problems, and (2) online approaches which allow scheduling algorithms to self- adapt during the solving process. We encourage papers employing variable-length representations for scheduling algorithms. Here are a number of potential techniques which are highly relevant to this special issue:

- Hyper-heuristics for heuristic/operator selection
- Hyper-heuristics for generating new operators and algorithms
- Memetic algorithms
- Genetic programming
- Evolutionary design of heuristics
- Self-adaptive evolutionary algorithms
- Machine learning-based meta-heuristics
- Learning classifier systems
- Scheduling or optimisation of algorithms and machines

Topics of interest include, but are not limited to:

- Production scheduling
- Timetabling
- Vehicle routing
- Grid/cloud scheduling
- 2D/3D strip packing
- Space/resource allocation
- Automated heuristic design
- Innovative applications of scheduling and combinatorial optimisation
- Web service composition
- Wireless networking state location allocation
- Airport runway scheduling
- Project scheduling
- Traffic control


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 “Automated Design and Adaptation” as the article type when submitting.

Important Dates

Submission deadline: Oct. 1, 2016
Notification of first review:December 1, 2016
Resubmission: January 20, 2017
Final acceptance notification: February 20, 2017

References

[1] J. Branke, S. Nguyen, C. W. Pickardt, and M. Zhang, “Automated Design of Production Scheduling Heuristics: A Review,” IEEE Trans. Evol. Comput., vol. 20, no. 1, pp. 110–124, Feb. 2016.
[2] E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. R. Woodward, “Exploring Hyper-heuristic Methodologies with Genetic Programming,” in Computational Intelligence, vol. 1, C. Mumford and L. Jain, Eds. Springer Berlin Heidelberg, 2009, pp. 177–201.
[3] L. Feng, Y.-S. Ong, M.-H. Lim, and I. W. Tsang, “Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP,” IEEE Trans. Evol. Comput., vol. 19, no. 5, pp. 644–658, Oct. 2015.
[4] G. Kendall and N. M. Hussin, “A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology,” in Practice and Theory of Automated Timetabling V, vol. 3616, E. Burke and M. Trick, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 270–293.
[5] N. R. Sabar, M. Ayob, G. Kendall, and Rong Qu, “Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems,” IEEE Trans. Evol. Comput., vol. 19, no. 3, pp. 309–325, Jun. 2015.
[6] J. H. Drake, E. Özcan, and E. K. Burke, “A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem,” Evol. Comput., vol. 24, no. 1, pp. 113–141, Mar. 2016.

Saturday, February 27, 2016

GPEM 17(1) is now available

The first issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download. This is a special issue on Semantic Methods in Genetic Programming, edited by Michel O'Neill, and it also contains two book reviews (which are now free downloads).

The complete contents are:

"Editorial introduction"
by Lee Spector

"Semantic methods in genetic programming"
by Michael O’Neill

"Progress properties and fitness bounds for geometric semantic search operators"
by Tomasz P. Pawlak and Krzysztof Krawiec

"Subtree semantic geometric crossover for genetic programming"
by Quang Uy Nguyen and Tuan Anh Pham

"Self-tuning geometric semantic Genetic Programming"
by Mauro Castelli and  Luca Manzoni

BOOK REVIEW
"Malachy Eaton: Evolutionary humanoid robotics"
by Jürgen Leitner

BOOK REVIEW
"Stephen H. Muggleton and Hiroaki Watanabe (Eds.): Latest advances in inductive logic programming"
by Man Leung Wong

"Acknowledgment to Reviewers"
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
doi:10.1007/s10710-015-9241-9

"Controlling code growth by dynamically shaping the genotype size distribution"
by Marc-Andre Gardner and Christian Gagne and Marc Parizeau
doi:10.1007/s10710-015-9242-8

"Prudent alignment and crossover of decision trees in genetic programming"
Matej Sprogar
doi:10.1007/s10710-015-9243-7

"Neutral genetic drift: an investigation using Cartesian Genetic Programming"
Andrew Turner and Julian Miller
doi:10.1007/s10710-015-9244-6

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"
doi:10.1007/s10710-015-9250-8
(Remember book reviews are now free to download:-)

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

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.


References:
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.