Towards a population-based framework for improving stochastic local search algorithms

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1 Scopus citations

Abstract

In this paper, we introduce a method which goal is to help the search done by a Stochastic Local Search algorithm. Given a set of initial configurations, our algorithm dynamically discriminates the ones that seems to give more promising solutions, discarding at the same time those which did not help. The concept of diversity is managed in our framework in order to both avoid stagnation and to explore the search space. To evaluate our method, we use a well-known local search algorithm. This algorithm has been specially designed for solving instances of the challenging Traveling Tournament Problem. We compare the performance obtained running different configurations of the local search algorithm to the ones using our framework. Our results are very encouraging in terms of both the quality of the solutions and the execution time required.

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation
Pages337-344
Number of pages8
DOIs
StatePublished - 13 Aug 2012
Event14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
Duration: 7 Jul 201211 Jul 2012

Publication series

NameGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation

Conference

Conference14th International Conference on Genetic and Evolutionary Computation, GECCO'12
CountryUnited States
CityPhiladelphia, PA
Period7/07/1211/07/12

Keywords

  • dynamic search
  • parameter control
  • stochastic local search
  • traveling tournament problem

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