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Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization.

S Koziel1, Z Michalewicz

  • 1Department of Electronics, Telecommunication and Informatics, Technical University of Gdańsk, Narutowicza 11/12, 80-952, Gdańsk, Poland. koziel@ue.eti.pg.gda.pl

Evolutionary Computation
|April 13, 1999
PubMed
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This study introduces a novel decoder-based method for handling nonlinear constraints in evolutionary algorithms (EAs) for numerical optimization. The approach maps search spaces effectively, offering a new technique for constrained optimization problems.

Area of Science:

  • Numerical Optimization
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Several methods for handling nonlinear constraints in evolutionary algorithms (EAs) exist.
  • Existing techniques are categorized into four main groups: preservation of feasibility, penalty functions, searching for feasibility, and hybrid approaches.
  • A need exists for novel and effective constraint-handling techniques in numerical optimization.

Purpose of the Study:

  • To introduce and investigate a new decoder-based approach for solving constrained numerical optimization problems.
  • To demonstrate the efficacy of this novel approach using various test cases.
  • To explore the potential and applicability of this new constraint-handling technique.

Main Methods:

  • A novel approach incorporating a homomorphous mapping between an n-dimensional cube and a feasible search space is proposed.

Related Experiment Videos

  • This method is classified as a fifth category: decoder-based constraint handling techniques.
  • The approach is tested on several benchmark numerical optimization problems with nonlinear constraints.

    • Main Results:

    • The proposed decoder-based method demonstrates effectiveness in handling nonlinear constraints.
    • The approach shows promise and power when applied to various test cases.
    • The investigation highlights the successful application of homomorphous mapping for constraint handling.

    Conclusions:

    • The novel decoder-based approach offers a new and effective strategy for tackling constrained numerical optimization problems.
    • The homomorphous mapping technique provides a powerful tool for evolutionary algorithms in constrained optimization.
    • Further research into this fifth category of constraint handling techniques is warranted and promising.