An Adaptive memetic algorithm using a synergy of differential evolution and learning automata

Abhronil Sengupta, Tathagata Chakraborti, Amit Konar, Eunjin Kim, Atulya K. Nagar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In recent years there has been a growing trend in the application of Memetic Algorithms for solving numerical optimization problems. They are population based search heuristics that integrate the benefits of natural and cultural evolution. In this paper, we propose an Adaptive Memetic Algorithm, named LA-DE which employs a competitive variant of Differential Evolution for global search and Learning Automata as the local search technique. During evolution Stochastic Automata Learning helps to balance the exploration and exploitation capabilities of DE resulting in local refinement. The proposed algorithm has been evaluated on a test-suite of 25 benchmark functions provided by CEC 2005 special session on real parameter optimization. Experimental results indicate that LA-DE outperforms several existing DE variants in terms of solution quality.

Original languageEnglish (US)
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
StatePublished - Oct 4 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: Jun 10 2012Jun 15 2012

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Other

Other2012 IEEE Congress on Evolutionary Computation, CEC 2012
CountryAustralia
CityBrisbane, QLD
Period6/10/126/15/12

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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