Reducing bias and inefficiency in the selection algorithm
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Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
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GENITOR II.: a distributed genetic algorithm
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Proceedings of the 1st International Conference on Genetic Algorithms
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Proceedings of the 1st International Conference on Genetic Algorithms
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Proceedings of the 5th International Conference on Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
A Parallel Genetic Algorithm for Multiobjective Microprocessor Design
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Analysis of Genetic Algorithms Evolution under Pure Selection
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Selective Breeding in a Multiobjective Genetic Algorithm
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On Solving Travelling Salesman Problems by Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization
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Convergence Models of Genetic Algorithm Selection Schemes
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Genetic drift in genetic algorithm selection schemes
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Theoretical Computer Science
Multiobjective network design for realistic traffic models
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A Comparison of three evolutionary strategies for multiobjective genetic programming
Artificial Intelligence Review
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Evolutionary Computation
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Expert Systems with Applications: An International Journal
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A dominance tree and its application in evolutionary multi-objective optimization
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Applied Soft Computing
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CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Evolutionary multimodal optimization revisited
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Multicriteria network design using evolutionary algorithm
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Cooperative problem decomposition in Pareto competitive classifier models of coevolution
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
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ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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Applied Soft Computing
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HiPC'04 Proceedings of the 11th international conference on High Performance Computing
Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems
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Self-adaptation techniques applied to multi-objective evolutionary algorithms
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Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems. We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort.