The LCS and GBML community stop
The 2015 Impact Factor for Genetic Programming and Evolvable Machines is 1.143 (an increase from .903 last year).The 5-year Impact Factor is 1.475.
The 2015 Impact Factor for Genetic Programming and Evolvable Machines is 1.143 (an increase from .903 last year).
The 5-year Impact Factor is 1.475.
Evolutionary Computation, Volume 24, Issue 3, Page 521-544, Fall 2016. <br/>
Evolutionary Computation, Volume 24, Issue 3, Page 521-544, Fall 2016.
Enjoy (hopefully) …
Enjoy (hopefully) …
Evolutionary Computation, Volume 24, Issue 4, Page 637-666, Winter 2016. <br/>
Evolutionary Computation, Volume 24, Issue 4, Page 637-666, Winter 2016.
Evolutionary Computation, Ahead of Print. <br/>
Evolutionary Computation, Ahead of Print.
The second issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.
It contains:
“Partial-DNA cyclic memory for bio-inspired electronic cell”
by Sai Zhu, Jin-yan Cai, and Ya-feng Meng
“Grammar-based generation of variable-selection heuristics for constraint satisfaction problems”
by Alejandro Sosa-Ascencio, Gabriela Ochoa, Hugo Terashima-Marin, and Santiago Enrique Conant-Pablos
“A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization”
by Behnaz Moradabadi, Mohammad Mahdi Ebadzadeh, and Mohammad Reza Meybodi
“Evolutionary design of complex approximate combinational circuits”
by Zdenek Vasicek and Lukas Sekanina
BOOK REVIEW
“Anthony Brabazon, Michael O’Neill, Sean McGarraghy: Natural computing algorithms”
by Simone A. Ludwig
BOOK REVIEW
“Gusz Eiben and Jim Smith (Eds): Introduction to evolutionary computing”
by Jeffrey L. Popyack
ERRATUM
“Erratum to: Gusz Eiben and Jim Smith: Introduction to evolutionary computing (second edition)”
by Jeffrey L. Popyack
The second issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.
It contains:
“Partial-DNA cyclic memory for bio-inspired electronic cell”
by Sai Zhu, Jin-yan Cai, and Ya-feng Meng
“Grammar-based generation of variable-selection heuristics for constraint satisfaction problems”
by Alejandro Sosa-Ascencio, Gabriela Ochoa, Hugo Terashima-Marin, and Santiago Enrique Conant-Pablos
“A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization”
by Behnaz Moradabadi, Mohammad Mahdi Ebadzadeh, and Mohammad Reza Meybodi
“Evolutionary design of complex approximate combinational circuits”
by Zdenek Vasicek and Lukas Sekanina
BOOK REVIEW
“Anthony Brabazon, Michael O’Neill, Sean McGarraghy: Natural computing algorithms”
by Simone A. Ludwig
BOOK REVIEW
“Gusz Eiben and Jim Smith (Eds): Introduction to evolutionary computing”
by Jeffrey L. Popyack
ERRATUM
“Erratum to: Gusz Eiben and Jim Smith: Introduction to evolutionary computing (second edition)”
by Jeffrey L. Popyack
The second issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.
It contains:
“Partial-DNA cyclic memory for bio-inspired electronic cell”
by Sai Zhu, Jin-yan Cai, and Ya-feng Meng
“Grammar-based generation of variable-selection heuristics for constraint satisfaction problems”
by Alejandro Sosa-Ascencio, Gabriela Ochoa, Hugo Terashima-Marin, and Santiago Enrique Conant-Pablos
“A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization”
by Behnaz Moradabadi, Mohammad Mahdi Ebadzadeh, and Mohammad Reza Meybodi
“Evolutionary design of complex approximate combinational circuits”
by Zdenek Vasicek and Lukas Sekanina
BOOK REVIEW
“Anthony Brabazon, Michael O’Neill, Sean McGarraghy: Natural computing algorithms”
by Simone A. Ludwig
BOOK REVIEW
“Gusz Eiben and Jim Smith (Eds): Introduction to evolutionary computing”
by Jeffrey L. Popyack
ERRATUM
“Erratum to: Gusz Eiben and Jim Smith: Introduction to evolutionary computing (second edition)”
by Jeffrey L. Popyack
The second issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.
It contains:
“Partial-DNA cyclic memory for bio-inspired electronic cell”
by Sai Zhu, Jin-yan Cai, and Ya-feng Meng
“Grammar-based generation of variable-selection heuristics for constraint satisfaction problems”
by Alejandro Sosa-Ascencio, Gabriela Ochoa, Hugo Terashima-Marin, and Santiago Enrique Conant-Pablos
“A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization”
by Behnaz Moradabadi, Mohammad Mahdi Ebadzadeh, and Mohammad Reza Meybodi
“Evolutionary design of complex approximate combinational circuits”
by Zdenek Vasicek and Lukas Sekanina
BOOK REVIEW
“Anthony Brabazon, Michael O’Neill, Sean McGarraghy: Natural computing algorithms”
by Simone A. Ludwig
BOOK REVIEW
“Gusz Eiben and Jim Smith (Eds): Introduction to evolutionary computing”
by Jeffrey L. Popyack
ERRATUM
“Erratum to: Gusz Eiben and Jim Smith: Introduction to evolutionary computing (second edition)”
by Jeffrey L. Popyack
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
The natural encoding for many search and optimization problems is the permutation, such as the traveling salesperson, vehicle routing, scheduling, assignment and mapping problems, among others. The effectiveness of a given mutation or crossover operato…
The natural encoding for many search and optimization problems is the permutation, such as the traveling salesperson, vehicle routing, scheduling, assignment and mapping problems, among others. The effectiveness of a given mutation or crossover operator depends upon the nature of what the permutation represents. For some problems, it is the absolute locations of the elements that most directly influences solution fitness; while for others, element adjacencies or even element precedences are most important. Different permutation operators respect different properties. We aim to provide the genetic algorithm or metaheuristic practitioner with a framework enabling effective permutation search landscape analysis. To this end, we contribute a new family of optimization problems, the permutation in a haystack, that can be parameterized to the various types of permutation problem (e.g., absolute versus relative positioning). Additionally, we propose a calculus of search landscapes, enabling analysis of search landscapes through examination of local fitness rates of change. We use our approach to analyze the behavior of common permutation mutation operators on a variety of permutation in a haystack landscapes; and empirically validate the prescriptive power of the search landscape calculus via experiments with simulated annealing.
