Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization

  • Pradyumn Kumar Shukla
Conference paper

DOI: 10.1007/978-3-540-71618-1_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)
Cite this paper as:
Shukla P.K. (2007) Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization. In: Beliczynski B., Dzielinski A., Iwanowski M., Ribeiro B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg

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.

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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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