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Genetic Algorithms for Nonlinear Programming

  • Masatoshi Sakawa
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 14)

Abstract

In this chapter, after introducing genetic algorithms for nonlinear programming including the original GEnetic algorithm for Numerical Optimization of COnstrained Problems (GENOCOP) system for linear constraints, the coevolutionary genetic algorithm, called GENOCOP III, proposed by Michalewicz et al. is discussed in detail. Realizing some drawbacks of GENOCOP III, the coevolutionary genetic algorithm, called the revised GENOCOP III, is presented through the introduction of a generating method of an initial reference point by minimizing the sum of squares of violated nonlinear constraints and a bisection method for generating a new feasible point on the line segment between a search point and a reference point efficiently. Illustrative numerical examples are provided to demonstrate the feasibility and efficiency of the revised GENOCOP III.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Masatoshi Sakawa
    • 1
  1. 1.Department of Artificial Complex Systems Engineering, Graduate School of EngineeringHiroshima UniversityHigashi-HiroshimaJapan

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