Category: GPEM

GPEM impact factor!

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science…

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

GPEM impact factor!

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science…

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

GPEM impact factor!

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science…

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

GPEM impact factor!

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science…

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

GPEM impact factor!

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science…

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

GPEM impact factor!

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science…

I am very pleased to announce that Genetic Programming and Evolvable Machines has received its first official impact factor, of 1.091. We are ranked by ISI in the Artificial Intelligence category as 66th out of 102 journals, and in the Computer Science Theory and Methods category as 47th out of 91 journals. Considering our youth and the fact that this is our first ranking I think that this is quite strong.

GPEM 11(2) now available online

The second issue of volume 11 of Genetic Programming and Evolvable Machines is now available online. This is Part 2 of the special issue on parallel and distributed evolutionary algorithms, and it contains the following articles:”Guest editorial: speci…

The second issue of volume 11 of Genetic Programming and Evolvable Machines is now available online. This is Part 2 of the special issue on parallel and distributed evolutionary algorithms, and it contains the following articles:

“Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two”
by Marco Tomassini and Leonardo Vanneschi
“An ensemble-based evolutionary framework for coping with distributed intrusion detection”
by Gianluigi Folino, Clara Pizzuti and Giandomenico Spezzano
“Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms”
by Garnett Wilson and Wolfgang Banzhaf
“Simdist: a distribution system for easy parallelization of evolutionary computation”
by Boye Annfelt Høverstad
“Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm”
by Ting Hu, Simon Harding and Wolfgang Banzhaf
“EvAg: a scalable peer-to-peer evolutionary algorithm”
by J. L. J. Laredo, A. E. Eiben, M. van Steen and J. J. Merelo

GPEM 11(1) hardcopy — new color!

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

GPEM 11(1) now available online

The first issue of volume 11 of Genetic Programming and Evolvable Machines is now available online, with the following articles:”Editorial Introduction” and “Acknowledgments” by Lee Spector”The influence of mutation on population dynamics in multiobjec…

The first issue of volume 11 of Genetic Programming and Evolvable Machines is now available online, with the following articles:

“Editorial Introduction” and “Acknowledgments” by Lee Spector
“The influence of mutation on population dynamics in multiobjective genetic programming”
by Khaled Badran & Peter I. Rockett
“Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure”
by Vyung-Joong Kim, Adrian Wong & Hod Lipson
“GP challenge: evolving energy function for protein structure prediction” by Pawel Widera, Jonathan M. Garibaldi & Natalio Krasnogor
“The identification and exploitation of dormancy in genetic programming” by David Jackson
“Book Review: Michael Affenzeller, Stefan Wagner, Stephan Winkler and Andreas Beham: Genetic algorithms and genetic programming modern concepts and practical applications” by Gisele L. Pappa
“Book Review: Melanie Mitchell: Complexity a guided tour”
by Felix Streichert

GPEM 10(4) now available online

The fourth issue of volume 10 of Genetic Programming and Evolvable Machines is now available online. This is the first part of the two-part Special Issue on Parallel and Distributed Evolutionary Algorithms, and it contains the following articles:Introd…

The fourth issue of volume 10 of Genetic Programming and Evolvable Machines is now available online. This is the first part of the two-part Special Issue on Parallel and Distributed Evolutionary Algorithms, and it contains the following articles:

Introduction: special issue on parallel and distributed evolutionary algorithms, part I
by Marco Tomassini & Leonardo Vanneschi
Distributed differential evolution with explorative–exploitative population families
by Matthieu Weber, Ferrante Neri & Ville Tirronen
A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
by V. G. Asouti, I. C. Kampolis & K. C. Giannakoglou
Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
by Asim Munawar, Mohamed Wahib, Masaharu Munetomo & Kiyoshi Akama
Parallel evolution using multi-chromosome cartesian genetic programming
by James Alfred Walker, Katharina Völk, Stephen L. Smith & Julian Francis Miller
Genetic programming on graphics processing units
by Denis Robilliard, Virginie Marion-Poty & Cyril Fonlupt
Book Review: Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots
by Navneet Bhalla