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.

Thursday, August 3, 2017

GPEM 18(3) is available, with a peer commentary special section

The third issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download. Along with two regular articles and a book review, it contains a peer commentary special section, with a target article by Peter A. Whigham, Grant Dick, and James Maclaurin, seven commentaries, and a response by the target article authors. The special section is also available as a "topical collection" with its own page here.

GPEM 18(3) contains:

A univariate marginal distribution algorithm based on extreme elitism and its application to the robotic inverse displacement problem
by Shujun Gao & Clarence W. de Silva

A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks
by Ali Mohammad Saghiri & Mohammad Reza Meybodi

Introduction to the peer commentary special section on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Lee Spector

On the mapping of genotype to phenotype in evolutionary algorithms
by Peter A. Whigham, Grant Dick & James Maclaurin

Probing the axioms of evolutionary algorithm design: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Lee Altenberg

Genotype–phenotype mapping implications for genetic programming representation: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Anikó Ekárt & Peter R. Lewis

Evolutionary algorithms and synthetic biology for directed evolution: commentary on “on the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Douglas B. Kell

Distilling the salient features of natural systems: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Whigham, Dick and Maclaurin
by Michael O’Neill & Miguel Nicolau

A rebuttal to Whigham, Dick, and Maclaurin by one of the inventors of Grammatical Evolution: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by Conor Ryan

(Over-)Realism in evolutionary computation: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by G. Squillero & A. Tonda

Taking “biology” just seriously enough: Commentary on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin
by James A. Foster

Just because it works: a response to comments on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms”
by Peter A. Whigham, Grant Dick & James Maclaurin

BOOK REVIEW
Sebastian Ventura and Jose Maria Luna: Pattern mining with evolutionary algorithms
by Bing Xue

Sunday, June 25, 2017

CACM editor-in-chief steps down advice for editorial board

In this month's Communications of the ACM Moshe Vardi, the current editor-in-chief, publishes a one page valedictory article

Tuesday, June 20, 2017

Parameters, Parameters, Parameters

The practice of evolutionary algorithms involves a mundane yet inescapable phase, namely, finding parameters that work well. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? All these nagging questions need good answers if one is to embrace success. Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters. We aver that this renders the life of the practitioner that much easier, and cap off our study with an advisory digest for the weary.

Wanna learn more? The full paper is here.

Friday, June 16, 2017

CFP: Special Issue on Genetic Programming, Evolutionary Computation and Visualization

Guest Editors: Nadia Boukhelifa and Evelyne Lutton (both at INRA, Versailles-Grignon, France). Please see the full CFP on the Springer site.

Thursday, May 18, 2017

GPEM 18(2) is available

The second issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

An iterative genetic programming approach to prototype generation
by José María Valencia-Ramírez, Mario Graff, Hugo Jair Escalante & Jaime Cerda-Jacobo

Recursion in tree-based genetic programming
by Alexandros Agapitos, Michael O’Neill, Ahmed Kattan & Simon M. Lucas

Recurrent Cartesian Genetic Programming of Artificial Neural Networks
by Andrew James Turner & Julian Francis Miller

An analysis of the genetic marker diversity algorithm for genetic programming
by Armand R. Burks & William F. Punch

Solving metameric variable-length optimization problems using genetic algorithms
by Matthew L. Ryerkerk, Ronald C. Averill, Kalyanmoy Deb & Erik D. Goodman

SOFTWARE REVIEW
Software review: CGP-Library
by Emerson Carlos Pedrino, Paulo Cesar Donizeti Paris, Denis Pereira de Lima & Valentin Obac Roda

Wednesday, March 29, 2017

EvoStar panel on open-access publishing

Of possible interest to GPEM-affiliated folks who will be attending EvoStar:

Jacqueline Heinerman has organized a lunchtime panel discussion session on Wednesday, on the topic of open access publishing, which will be sponsored by NWO, Netherlands Organisation for Scientific Research. Panel speakers will include Ronan Nugent from Springer, Emma Hart (editor of ECJ), and a representative of the VU university library.

h/t Jennifer Willies and James McDermott for the notice.

Monday, March 6, 2017

GPEM 18(1) is available

The first issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download.

This is a special issue on Genetic Improvement, edited by Justyna Petke, and it also contains three book reviews.

The complete contents are:

"Editorial introduction"
by Lee Spector Pages 1-2

"Preface to the Special Issue on Genetic Improvement"
by Justyna Petke

"Genetic improvement of GPU software"
by William B. Langdon, Brian Yee Hong Lam, Marc Modat, Justyna Petke, and Mark Harman

"Trading between quality and non-functional properties of median filter in embedded systems"
by Zdenek Vasicek and Vojtech Mrazek

"Online Genetic Improvement on the java virtual machine with ECSELR"
by Kwaku Yeboah-Antwi and Benoit Baudry

BOOK REVIEW
"Krzysztof Krawiec: Behavioral program synthesis with genetic programming" by Raja Muhammad Atif Azad

BOOK REVIEW
"Paul Rendell: Turing machine universality of the Game of Life"
by Moshe Sipper

BOOK REVIEW
"James Keller, Derong Liu, and David Fogel: Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation" by Steven Michael Corns

"Acknowledgment to Reviewers"
by L. Spector

Tuesday, January 3, 2017

new members to the GPEM editorial board

 GPEM welcomes the following new members to the editorial board: 
 Anna I Esparcia Alcazar,
 Muhammad Atif Azad,
 Mauro Castelli,
 Ting Hu,
 Michael Lones,
 Evelyne Lutton,
 James Mcdermott,
 Xuan Hoai Nguyen,
 Gabriela Ochoa,
 Gisele Pappa,
 Justyna Petke,
 Leonardo Trujillo Reyes,
 Federica Sarro,
 and Alberto Tonda.