As per our discussion in our recent editorial board meeting, our current model for open access publishing, “Open Choice,” is described here.
I am delighted to announce that our Advisory Board has been renewed and now contains the following senior members of our community (with an asterisk marking each new role):
* Wolfgang Banzhaf (on Advisory Board as well as Founding Editor)
* Stephanie Forrest (new to board)
* Marc Schoenauer (elevation from regular board member)
* Andy Tyrrell (elevation from Associate Editor)
I am also quite excited about the renewal/expansion of the Associate Editors, including the addition of a new Thematic Area Editor for Software Engineering:
James A. Foster (Area Editor for Life Sciences)
* Mark Harman (new to board, Area Editor for Software Engineering, which is a new Thematic Area)
William B. Langdon (Resource Review Editor)
* Alberto Moraglio (elevation from regular board member)
Una-May O’Reilly (Area Editor for Data Analytics and Knowledge Discovery)
* Lukas Sekanina (elevation from regular board member)
Moshe Sipper (Area Editor for Games)
* Martin Trefzer (elevation from regular board member)
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 second issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.
“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
“Anthony Brabazon, Michael O’Neill, Sean McGarraghy: Natural computing algorithms”
by Simone A. Ludwig
“Gusz Eiben and Jim Smith (Eds): Introduction to evolutionary computing”
by Jeffrey L. Popyack
“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.
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.
This paper introduces sparse representation into spectral clustering and provides a sparse spectral clustering framework via a multiobjective evolutionary algorithm. In contrast to conventional spectral clustering, the main contribution of this paper is to construct the similarity matrix using a sparse representation approach by modeling spectral clustering as a constrained multiobjective optimization problem. Specific operators are designed to obtain a set of high quality solutions in the optimization process. Furthermore, we design a method to select a tradeoff solution from the Pareto front using a measurement called ratio cut based on an adjacency matrix constructed by all the nondominated solutions. We also extend the framework to the semi-supervised clustering field by using the semi-supervised information brought by the labeled samples to set some constraints or to guide the searching process. Experiments on commonly used datasets show that our approach outperforms four well-known similarity matrix construction methods in spectral clustering, and one multiobjective clustering algorithm. A practical application in image segmentation also demonstrates the efficiency of the proposed algorithm.
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.