Parametrization and Balancing Local and Global Search

  • Dirk Sudholt
Part of the Studies in Computational Intelligence book series (SCI, volume 379)

Introduction

This chapter is devoted to the parametrization of memetic algorithms and how to find a good balance between global and local search. This is one of the most pressing questions when designing a hybrid algorithm. The idea of hybridization is to combine the advantages of different components. But if one components dominates another one, hybridization may become more hindering than useful and computational effort may be wasted. For the case of memetic algorithms, if the effect of local search is too strong, the algorithm may quickly get stuck in local optima of bad quality.Moreover, the algorithm is likely to rediscover the same local optimum over and over again. Lastly, an excessive local search quickly leads to a loss of diversity within the population.

Keywords

Local Search Polynomial Time Local Optimum Pareto Front State Graph 
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 2012

Authors and Affiliations

  • Dirk Sudholt
    • 1
  1. 1.School of Computer ScienceThe University of Birmingham EdgbastonBirminghamUK

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