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Evolutionary Computation, IEEE Transactions on

Issue 5 • Date Oct. 2003

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Displaying Results 1 - 6 of 6
  • Comparing evolutionary algorithms on binary constraint satisfaction problems

    Page(s): 424 - 444
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (904 KB) |  | HTML iconHTML  

    Constraint handling is not straightforward in evolutionary algorithms (EAs) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade, numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the evolutionary computing field. View full abstract»

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  • A collective-based adaptive symbiotic model for surface reconstruction in area-based stereo

    Page(s): 482 - 502
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4511 KB) |  | HTML iconHTML  

    This paper proposes a novel optimization algorithm for image-space matching and three-dimensional space analysis, using an adapted scheme of evolutionary computation that employs the concept of symbiosis in a collective of homogeneous populations. It is applied to the automatic generation of disparity surfaces used for depth estimation in stereo vision. The global task of approximating the complete disparity surface is decomposed to a large number of smaller local problems, each solvable by a smaller processing unit. Coevolution is sustained in such a way as to counteract the arbitrary decomposition of the original super-problem, so that the local evolutions of all the subproblems become interlocked. This, in the long run, provides a consistent global solution, and it does so via an asynchronous and massively parallel architecture. The entire surface is partitioned to a set of adjoining patches represented by distinct species or populations, with phenotypes corresponding to different polynomial functionals. The credit assignment functions take into account both self and symbiotic terms in an adaptive and dynamic manner, in order to produce disparity patches that are fit within their own domain and at the same time fit in association with their symbionts. This persistent propagation of local interactions to a global scale throughout evolution generates a unified disparity surface composed of the many smaller patch surfaces. View full abstract»

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  • Self-adaptive fitness formulation for constrained optimization

    Page(s): 445 - 455
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (558 KB) |  | HTML iconHTML  

    A self-adaptive fitness formulation is presented for solving constrained optimization problems. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two-stage penalty that is applied to the infeasible solutions. The performance of the method has been examined by its application to a set of eleven test cases from the specialized literature. The results have been compared with previously published results from the literature. It is shown that the method is able to find the optimum solutions. The proposed method requires no parameter tuning and can be used as a fitness evaluator with any evolutionary algorithm. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution. View full abstract»

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  • Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms

    Page(s): 503 - 515
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (721 KB) |  | HTML iconHTML  

    The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance counting, or identification of the nondominated solutions. The success of these algorithms indicates that this type of fitness is suitable for multiobjective problems, but so far the use of Pareto-based fitness has lead to program run times in O(GMN2), where G is the number of generations, M is the number of objectives, and N is the population size. The N2 factor should be reduced if possible, since it leads to long processing times for large population sizes. This paper presents a new and efficient algorithm for nondominated sorting, which can speed up the processing time of some multiobjective evolutionary algorithms (MOEAs) substantially. The new algorithm is incorporated into the nondominated sorting genetic algorithm II (NSGA-II) and reduces the overall run-time complexity of this algorithm to O(GN logM-1N), much faster than the O(GMN2) complexity published by Deb et al. (2002). Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(MN2) algorithms. View full abstract»

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  • The analysis of a recombinative hill-climber on H-IFF

    Page(s): 417 - 423
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (455 KB) |  | HTML iconHTML  

    Many experiments have proved that crossover is an essential search operator in evolutionary algorithms, at least for certain functions. However, the rigorous analysis of such algorithms on crossover-friendly functions is still in its infancy. Here, a recombinative hill-climber is analyzed on the crossover-friendly function hierarchical-if-and-only-if (H-IFF) introduced by Watson et al. (1998). The dynamics of this algorithm are investigated and it is proved that the expected optimization time equals Θ(n log n). View full abstract»

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  • GENETICA: A computer language that supports general formal expression with evolving data structures

    Page(s): 456 - 481
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1225 KB)  

    This paper presents a general problem-solving method combining the principles of artificial intelligence and evolutionary computation. The problem-solving method is based on the computer language GENETICA, which stands for "Genetic Evolution of Novel Entities Through the Interpretation of Composite Abstractions." GENETICAs programming environment includes a computational system that evolves data abstractions, viewed as genotypes of data generation scenarios for a GENETICA program, with respect to either confirmation or optimization goals. A problem can be formulated as a GENETICA program, while the solution is represented as a data structure resulting from an evolved data generation scenario. This approach to problem solving offers: 1) generality, since it concerns virtually any problem stated in formal logic; 2) effectiveness, since formally expressed problem-solving knowledge can be incorporated in the problem statement; and 3) creativity, since unpredictable solutions can be obtained by evolved data structures. It is shown that domain specific languages, including genetic programming ones, that inherit GENETICAs features can be developed in GENETICA. The language G-CAD, specialized to problem solving in the domain of architectural design, is presented as a case study followed by experimental results. 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