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Evolutionary Computation, IEEE Transactions on

Issue 2 • Date July 1999

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Displaying Results 1 - 10 of 10
  • Proceedings of the 1998 IEEE International Conference on Evolutionary Computation [Book Review]

    Page(s): 153 - 156
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    Freely Available from IEEE
  • Evolvable systems 1998-Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware (ICES'98) [Book Review]

    Page(s): 157 - 158
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    Freely Available from IEEE
  • Adaptive Computing in Design and Manufacture [Book Review]

    Page(s): 159
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    Freely Available from IEEE
  • Parameter control in evolutionary algorithms

    Page(s): 124 - 141
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    The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research View full abstract»

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  • Evolutionary computation applied to mesh optimization of a 3-D facial image

    Page(s): 113 - 123
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    We apply evolutionary algorithms to the approximation of a three-dimensional image of a human face using a triangular mesh. The problem is how to locate a limited number of node points such that the mesh approximates the facial surface as closely as possible. Two evolutionary algorithms are implemented and compared. The first does selection and reproduction in the population of node points in a single triangulation. The second is a genetic algorithm in which a set of different triangulations is regarded as a population. We expect that such evolutionary computation can be used in other engineering applications which share the same problem of surface approximation View full abstract»

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  • Clustering with a genetically optimized approach

    Page(s): 103 - 112
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    Describes a genetically guided approach to optimizing the hard (J 1) and fuzzy (Jm) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm (GA) can ameliorate the difficulty of choosing an initialization for the c-means clustering algorithms. Experiments use six data sets, including the Iris data, magnetic resonance, and color images. The genetic algorithm approach is generally able to find the lowest known Jm value or a Jm associated with a partition very similar to that associated with the lowest Jm value. On data sets with several local extrema, the GA approach always avoids the less desirable solutions. Degenerate partitions are always avoided by the GA approach, which provides an effective method for optimizing clustering models whose objective function can be represented in terms of cluster centers. A series random initializations of fuzzy/hard c-means, where the partition associated with the lowest Jm value is chosen, can produce an equivalent solution to the genetic guided clustering approach given the same amount of processor time in some domains View full abstract»

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  • Evolutionary programming made faster

    Page(s): 82 - 102
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    Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In the paper, a “fast EP” (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. The paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. The paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested View full abstract»

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  • Synthesizing a predatory search strategy for VLSI layouts

    Page(s): 147 - 152
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    When searching for prey, many predator species exhibit a remarkable behavior: after prey capture, the predators promptly engage in “area-restricted search”, probing for consecutive captures nearby. Biologists have been surprised with the efficiency and adaptability of this search strategy to a great number of habitats and prey distributions. We propose to synthesize a similar search strategy for the massively multimodal problems of combinatorial optimization. The predatory search strategy restricts the search to a small area after each new improving solution is found. Subsequent improvements are often found during area-restricted search. Results of this approach to gate matrix layout, an important problem arising in very large scale integrated (VLSI) architectures, are presented. Compared to established methods over a set of benchmark circuits, predatory search is able to either match or outperform the best-known layouts. Additional remarks address the relation of predatory search to the “big-valley” hypothesis and to the field of artificial life View full abstract»

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  • Inductive reasoning and bounded rationality reconsidered

    Page(s): 142 - 146
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    Complex adaptive systems have historically been studied using simplifications that mandate deterministic interactions between agents or instead treat their interactions only with regard to their statistical expectation. This has led to an anticipation, even in the case of agents employing inductive reasoning in light of limited information, that such systems may have equilibria that can be predicted a priori. This hypothesis is tested here using a simulation of a simple market economy in which each agent's behavior is based on the result of an iterative evolutionary process of variation and selection applied to competing internal models of its environment. The results indicate no tendency for convergence to stability or a long-term equilibrium and highlight fundamental differences between deterministic and stochastic models of complex adaptive systems View full abstract»

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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
Garrison W. Greenwood, Ph.D. P.E
Portland State University