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IEEE Transactions on Evolutionary Computation

Issue 2 • April 2012

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Displaying Results 1 - 12 of 12
  • Table of contents

    Publication Year: 2012, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2012, Page(s): C2
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  • A Multiobjective Evolutionary Algorithm That Diversifies Population by Its Density

    Publication Year: 2012, Page(s):149 - 172
    Cited by:  Papers (22)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6470 KB) | HTML iconHTML

    Most existing multiobjective evolutionary algorithms (MOEAs) assume the existence of Pareto-optimal solutions/Pareto-optimal objective vectors in a neighborhood of an obtained Pareto-optimal set (PS)/Pareto-optimal front (PF). Obviously, this assumption does not work well on the multiobjective problem (MOP) whose true PF and true PS are in the form of multiple segments-truly disconnected MOP (TYD-... View full abstract»

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  • Toward Understanding EDAs Based on Bayesian Networks Through a Quantitative Analysis

    Publication Year: 2012, Page(s):173 - 189
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (9579 KB) | HTML iconHTML

    The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a methodology of analysis whi... View full abstract»

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  • Adaptation in Dynamic Environments: A Case Study in Mission Planning

    Publication Year: 2012, Page(s):190 - 209
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5530 KB) | HTML iconHTML

    Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potentia... View full abstract»

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  • Cooperatively Coevolving Particle Swarms for Large Scale Optimization

    Publication Year: 2012, Page(s):210 - 224
    Cited by:  Papers (180)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6019 KB) | HTML iconHTML

    This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving large-scale optimization problems (up to 2000 real-valued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping technique random grouping.... View full abstract»

    Open Access
  • On the Impact of Mutation-Selection Balance on the Runtime of Evolutionary Algorithms

    Publication Year: 2012, Page(s):225 - 241
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1277 KB) | HTML iconHTML

    The interplay between mutation and selection plays a fundamental role in the behavior of evolutionary algorithms (EAs). However, this interplay is still not completely understood. This paper presents a rigorous runtime analysis of a non-elitist population-based EA that uses the linear ranking selection mechanism. The analysis focuses on how the balance between parameter η, controlling the s... View full abstract»

    Open Access
  • Evolving Distributed Algorithms With Genetic Programming

    Publication Year: 2012, Page(s):242 - 265
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (12466 KB) | HTML iconHTML

    In this paper, we evaluate the applicability of genetic programming (GP) for the evolution of distributed algorithms. We carry out a large-scale experimental study in which we tackle three well-known problems from distributed computing with six different program representations. For this purpose, we first define a simulation environment in which phenomena such as asynchronous computation at changi... View full abstract»

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  • An Improved Ant-Based Algorithm for the Degree-Constrained Minimum Spanning Tree Problem

    Publication Year: 2012, Page(s):266 - 278
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (9058 KB) | HTML iconHTML

    The degree-constrained minimum spanning tree (DCMST) problem is the problem of finding the minimum cost spanning tree in an edge weighted complete graph such that each vertex in the spanning tree has degree ≤ d for some d ≥ 2. The DCMST problem is known to be NP-hard. This paper presents an ant-based algorithm to find low cost degree-constrained spanning trees (DCST). T... View full abstract»

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  • The Effects of Constant and Bit-Wise Neutrality on Problem Hardness, Fitness Distance Correlation and Phenotypic Mutation Rates

    Publication Year: 2012, Page(s):279 - 300
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5034 KB) | HTML iconHTML

    Kimura's neutral theory of evolution has inspired researchers from the evolutionary computation community to incorporate neutrality into evolutionary algorithms (EAs) in the hope that it can aid evolution. The effects of neutrality on evolutionary search have been considered in a number of studies, the results of which, however, have been highly contradictory. In this paper, we analyze the reasons... View full abstract»

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  • IEEE Computational Intelligence Society Information

    Publication Year: 2012, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation information for authors

    Publication Year: 2012, Page(s): C4
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Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

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Meet Our Editors

Editor-in-Chief
Professor Kay Chen Tan (IEEE Fellow)
Department of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Email: kaytan@cityu.edu.hk