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Evolutionary Computation, IEEE Transactions on

Issue 1 • Date Feb. 2008

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Displaying Results 1 - 14 of 14
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

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  • An Artificial Immune System Heuristic for Generating Short Addition Chains

    Page(s): 1 - 24
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    This paper deals with the optimal computation of finite field exponentiation, which is a well-studied problem with many important applications in the areas of error-correcting codes and cryptography. It has been shown that the optimal computation of finite field exponentiation is a problem which is closely related to finding a suitable addition chain with the shortest possible length. However, it is also known that obtaining the shortest addition chain for a given arbitrary exponent is an NP-hard problem. As a consequence, heuristics are an obvious choice to compute field exponentiation with a semi-optimal number of underlying arithmetic operations. In this paper, we propose the use of an artificial immune system to tackle this problem. Particularly, we study the problem of finding both the shortest addition chains for exponents e with moderate size (i.e., with a length of less than 20 bits), and for the huge exponents typically adopted in cryptographic applications, (i.e., in the range from 128 to 2048 bits). View full abstract»

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  • Applying a Traffic Lights Evolutionary Optimization Technique to a Real Case: “Las Ramblas” Area in Santa Cruz de Tenerife

    Page(s): 25 - 40
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    In previous research, we have designed and successfully tested a traffic light cycles evolutionary optimization architecture. In this paper, we attempt to validate those results with a real-world test case. For a wide area in Santa Cruz de Tenerife city - Canary islands - we have improved traffic behavior, using our optimized traffic light cycle times in a simulated environment. Throughout this paper, we present some of the experiences, knowledge, and problems encountered. View full abstract»

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  • RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm

    Page(s): 41 - 63
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    Under mild conditions, it can be induced from the Karush-Kuhn-Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewise continuous (m - 1)-D manifold, where m is the number of objectives. Based on this regularity property, we propose a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for continuous multiobjective optimization problems with variable linkages. At each generation, the proposed algorithm models a promising area in the decision space by a probability distribution whose centroid is a (m - 1)-D piecewise continuous manifold. The local principal component analysis algorithm is used for building such a model. New trial solutions are sampled from the model thus built. A nondominated sorting-based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, RM-MEDA outperforms three other state-of-the-art algorithms, namely, GDE3, PCX-NSGA-II, and MIDEA, on a set of test instances with variable linkages. We have demonstrated that, compared with GDE3, RM-MEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages. A few shortcomings of RM-MEDA have also been identified and discussed in this paper. View full abstract»

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  • Opposition-Based Differential Evolution

    Page(s): 64 - 79
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    Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy. View full abstract»

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  • An Adaptive Tradeoff Model for Constrained Evolutionary Optimization

    Page(s): 80 - 92
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    In this paper, an adaptive tradeoff model (ATM) is proposed for constrained evolutionary optimization. In this model, three main issues are considered: (1) the evaluation of infeasible solutions when the population contains only infeasible individuals; (2) balancing feasible and infeasible solutions when the population consists of a combination of feasible and infeasible individuals; and (3) the selection of feasible solutions when the population is composed of feasible individuals only. These issues are addressed in this paper by designing different tradeoff schemes during different stages of a search process to obtain an appropriate tradeoff between objective function and constraint violations. In addition, a simple evolutionary strategy (ES) is used as the search engine. By integrating ATM with ES, a generic constrained optimization evolutionary algorithm (ATMES) is derived. The new method is tested on 13 well-known benchmark test functions, and the empirical results suggest that it outperforms or performs similarly to other state-of-the-art techniques referred to in this paper in terms of the quality of the resulting solutions. View full abstract»

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  • Evolving Output Codes for Multiclass Problems

    Page(s): 93 - 106
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    In this paper, we propose an evolutionary approach to the design of output codes for multiclass pattern recognition problems. This approach has the advantage of taking into account the different aspects that are relevant for a code matrix to achieve a good performance. We define a fitness function made up of five terms that refer to overall classifier accuracy, binary classifiers' accuracy, classifiers' diversity, minimum Hamming distance among codewords, and margin of classification. These five factors have not been considered together in previous works. We perform a study of these five terms to obtain a fitness function with three of them. We test our approach on 27 datasets from the UCI Machine Learning Repository, using three different base learners: C4.5, neural networks, and support vector machines. We show a better performance than most of the current standard methods, namely, randomly generated codes with approximately equal random split, codes designed using a CHC algorithm, and one-vs-all and one-vs-one methods. View full abstract»

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  • Accelerating Differential Evolution Using an Adaptive Local Search

    Page(s): 107 - 125
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    We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented. View full abstract»

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  • IEEE Symposium on Computational Intelligence and Games

    Page(s): 126
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  • Put your technology leadership in writing [advertisement]

    Page(s): 127
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  • Order form for reprints

    Page(s): 128
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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

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