2015 Impact Factor

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.

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Singing and rapping evolution

Enjoy (hopefully) …

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GPEM 17(2) is now available

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

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IEEE Transactions on Evolutionary Computation information for authors

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

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The Permutation in a Haystack Problem and the Calculus of Search Landscapes

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.

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Member Get-A-Member (MGM) Program

Advertisement, IEEE.

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A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm

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.

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2016 IEEE Symposium Series on Computational Intelligence

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms

Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose anMOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters.

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Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm

A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.

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