A comparative study of selective breeding strategies in a multiobjective genetic algorithm

Authors:
Andrew Wildman;Geoff Parks
Affiliations:
Cambridge University Engineering Department, Cambridge, UK;Cambridge University Engineering Department, Cambridge, UK
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

Citing 6
Cited 0

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Genetic Algorithms in Search, Optimization and Machine Learning
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Evolutionary Algorithms for Solving Multi-Objective Problems

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Adaptive Selection Methods for Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Selective Breeding in a Multiobjective Genetic Algorithm

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Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation

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Abstract

The design of Pressurized Water Reactor (PWR) reload cores is a difficult combinatorial optimization problem with multiple competing objectives. This paper describes the use of a Genetic Algorithm (GA) to perform true multiobjective optimization on the PWR reload core design problem and improvements made to its performance in identifying nondominated solutions to represent the trade-off surface between competing objectives. The use of different pairing strategies for combining parents is investigated and found to produce promising results in some cases.