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An evolutionary algorithm for single objective nonlinear constrained optimization problems

  • Hunter T. Albright
  • James P. Ignizio
Problem Structure and Fitness Landscapes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)

Abstract

This paper presents research into an evolutionary algorithm that utilizes the coevolution of feasible and infeasible solutions in solving constrained optimization problems. The evolution of these populations occurs through the use of traditional and specially designed operators that allow for crossover to occur in each of the populations as well as across the two populations. The cross population crossover allows for the information contained in the infeasible solutions to be utilized in the search for the optimal solution.

Keywords

Genetic Algorithm Feasible Solution Penalty Function Feasible Region Constrain Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Hunter T. Albright
    • 1
  • James P. Ignizio
    • 1
  1. 1.Department of Systems EngineeringUniversity of VirginiaCharlottesville

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