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Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization

  • Pradyumn Kumar Shukla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems.

Keywords

Mutation Operator Search Operator Binary Indicator Local Search Operator Simultaneous Perturbation 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Pradyumn Kumar Shukla
    • 1
  1. 1.Institute of Numerical Mathematics, Department of Mathematics, Dresden University of Technology, Dresden PIN 01069Germany

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