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.

Saturday, August 3, 2013

GPEM 14(4) available online, including special section: Best of EuroGP/EvoBio

The 4th issue of volume 14 of Genetic Programming and Evolvable Machines is now available online. It contains:

"A high performance genetic algorithm using bacterial conjugation operator (HPGA)"
by Amir Mehrafsa, Alireza Sokhandan & Ghader Karimian

=== Special Section: Best of EuroGP/EvoBio

Guest editorial 
"Introduction to special section: Best of EuroGP/EvoBio"
by James A. Foster

"Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches"
by Leonardo Vanneschi, Matteo Mondini, Martino Bertoni, Alberto Ronchi & Mattia Stefano
"A new genetic programming framework based on reaction systems"
by Luca Manzoni, Mauro Castelli & Leonardo Vanneschi
Book Review 
"Thomas Jansen: Analyzing Evolutionary Algorithms: The Computer Science Perspective"
by Andrew M. Sutton

Wednesday, June 19, 2013

GPEM 14(3) available online: special issue on biologically inspired music, sound, art and design

The third issue of volume 14 of Genetic Programming and Evolvable Machines is now available online. This is a special issue on biologically inspired music, sound, art and design, edited by Juan Romero, Penousal Machado & Adrian Carballal. It contains:

"Guest editorial: special issue on biologically inspired music, sound, art and design"
by Juan Romero, Penousal Machado & Adrian Carballal

"A methodology for user directed search in evolutionary design"
by Jonathan Byrne, Erik Hemberg, Michael O’Neill & Anthony Brabazon

"Learning aesthetic judgements in evolutionary art systems"
by Yang Li, Changjun Hu, Leandro L. Minku & Haolei Zuo

"Aesthetic 3D model evolution"
by Steve Bergen & Brian J. Ross

"Graph grammars for evolutionary 3D design"
by James McDermott

Saturday, April 6, 2013

GPEM 14(2) available online

The second issue of volume 14 of Genetic Programming and Evolvable Machines is now available online, containing:

"Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework"
by Michele Amoretti

"Inference of hidden variables in systems of differential equations with genetic programming"
by Theodore W. Cornforth and Hod Lipson

"A module-level three-stage approach to the evolutionary design of sequential logic circuits"
by Yanyun Tao, Yuzhen Zhang, Jian Cao and Yalong Huang

"Improving analytical models of circular concrete columns with genetic programming polynomials"
by Hsing-Chih Tsai and Chan-Ping Pan

"Controllable procedural map generation via multiobjective evolution"
by Julian Togelius, Mike Preuss, Nicola Beume, Simon Wessing, Johan Hagelb├Ąck, Georgios N. Yannakakis & Corrado Grappiolo

"John H. Holland: Signals and boundaries: building blocks for complex adaptive systems"
by Denis Robilliard

Friday, March 8, 2013

Alma Lilia Garcia Almanza and Edward Tsang reply to Blake Lebaron's book review

Edward Tsang and Alma Lilia Garcia Almanza have written the following reply to the review of their book, Evolutionary applications for financial prediction: classification methods to gather patterns using genetic programming, which was written by Blake Lebaron and published in Volume 13, Issue 4 of Genetic Programming and Evolvable Machines:

It is good to receive a review of our book by an expert in the field. The review is insightful. It highlights the strength as well as weakness of the book. Here we would like to address LeBaron's major concern: "... My biggest concern is that the authors should have tried some more standard financial forecasting tests. This would include setting these classifiers up as a trading rule, and seeing how well they do."

Let us emphasize that our main goal is to prove that the new methods improve over genetic programming. For example, given a set of decision generated by genetic programming, the Repository Method will generate a rule set with better predictive performance. Given that genetic programming is an important technique in machine learning, our improvement on it is significant. We made no attempt to establish our methods to be "the best methods for forecasting". That is the reason why we have not compared it with trading rules generated by other techniques and test benchmarks. 

Besides, one major difficulty in comparing our methods against others is that our approaches were designed to generate a set of results (rules)  to satisfy different levels of risk preferences. The results of our methods are plotted in ROC curves. Other predictive techniques would typically generate just one prediction rule, which means they would produce just one point in the ROC space; hence fair comparison would be difficult.

The onus is on the authors to make our objectives clear. Lebaron's review suggests that we have not explained the above points clearly. We are therefore grateful to the reviewer and the Journal to give us a chance to clarify them.

Wednesday, February 13, 2013

GPEM 14(1) available online

The first issue of volume 14 of Genetic Programming and Evolvable Machines is now available online, containing:

by Lee Spector

"Better GP benchmarks: community survey results and proposals"
by David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Ja?kowski, Una-May O’Reilly & Sean Luke

"Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators"
by Krzysztof Krawiec & Tomasz Pawlak

"A comparison of grammatical genetic programming grammars for controlling femtocell network coverage"
by Erik Hemberg, Lester Ho, Michael O’Neill & Holger Claussen

"Multi-objective optimization of QCA circuits with multiple outputs using genetic programming"
by Razieh Rezaee, Mahboobeh Houshmand & Monireh Houshmand


"Franz Rothlauf: Design of Modern Heuristics"
by Dario Landa-Silva

"Andrew Adamatzky: Physarum Machines: Computers from Slime Mould"
by Vincenzo Bonifaci

by Lee Spector