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, April 10, 2018

GPEM 19(1&2) is now available

The first issue of Volume 19 (a double issue, numbers 1 and 2) of Genetic Programming and Evolvable Machines is now available for download.

It contains:

Editorial introduction
by Lee Spector

Acknowledgment to reviewers
by L. Spector

Guest editorial: special issue on automated design and adaptation of heuristics for scheduling and combinatorial optimisation
by Su Nguyen, Yi Mei & Mengjie Zhang

Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment
by Marko Ɖurasević & Domagoj Jakobović

Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment
by Marko Ɖurasević & Domagoj Jakobović

Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics
by Rinde R. S. van Lon, Juergen Branke & Tom Holvoet

A hyperheuristic approach based on low-level heuristics for the travelling thief problem
by Mohamed El Yafrani, Marcella Martins, Markus Wagner, Belaïd Ahiod, Myriam Delgado & Ricardo Lüders

Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems
by Juan Carlos Gomez & Hugo Terashima-Marín

Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem
by Ayad Turky, Nasser R. Sabar & Andy Song

Comparing three online evolvable hardware implementations of a classification system
by Oscar Garnica, Kyrre Glette & Jim Torresen

Evolution of shared grammars for describing simulated spatial scenes with grammatical evolution
by Jack Mario Mingo & Ricardo Aler

Implementing the template method pattern in genetic programming for improved time series prediction
by David Moskowitz

BOOK REVIEW
Hod Lipson and Melba Kurman: Driverless: intelligent cars and the road ahead
by Christine Zarges

BOOK REVIEW
Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning
by Jeff Heaton

BOOK REVIEW
Christian Blum and Günther R. Raidl: Hybrid metaheuristics—powerful tools for optimization
by Ofer M. Shir

Wednesday, December 20, 2017

Deadline extended for Special Issue on Genetic Programming, Evolutionary Computation and Visualization

The deadline for submissions to the Special Issue on Genetic Programming, Evolutionary Computation and Visualization (Guest Editors: Nadia Boukhelifa and Evelyne Lutton) has been extended to January 22, 2018. The call for papers is here.

Friday, November 17, 2017

Sharing GPEM articles

This isn't news, but I don't think it is yet as widely known as it should be: GPEM authors can post shareable links to view-only versions of their articles, through Springer's "SharedIt" service. The details are here.

Friday, October 20, 2017

GPEM 18(4) is available

The fourth issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download. It contains:

A meta-grammatical evolutionary process for portfolio selection and trading
by Iván Contreras, J. Ignacio Hidalgo, Laura Nuñez-Letamendía & J. Manuel Velasco

Affective evolutionary music composition with MetaCompose
by Marco Scirea, Julian Togelius, Peter Eklund & Sebastian Risi

Understanding grammatical evolution: initialisation
by Miguel Nicolau

BOOK REVIEW
Gustavo Olague: Evolutionary computer vision, the first footprints
by Evelyne Lutton

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.