Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

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Article ID: iaor20002999
Country: United States
Volume: 7
Issue: 1
Start Page Number: 19
End Page Number: 44
Publication Date: Mar 1999
Journal: Evolutionary Computation
Authors: ,
Keywords: genetic algorithms
Abstract:

During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids. In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

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