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Analysis of Crossover Operators for Cluster Geometry Optimization

  • Francisco B. PereiraEmail author
  • Jorge M. C. Marques
Chapter
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 46)

Abstract

We study the effectiveness of different crossover operators in the global optimization of atomic clusters. Hybrid approaches combining a steady-state evolutionary algorithm and a local search procedure are state-of-the-art methods for this problem. In this paper we describe several crossover operators usually adopted for cluster geometry optimization tasks. Results show that operators that are sensitive to the phenotypical properties of the solutions help to enhance the performance of the optimization algorithm. They are able to identify and recombine useful building blocks and, therefore, increase the likelihood of performing a meaningful exploration of the search space.

Keywords

Local Search Crossover Operator Local Search Procedure Uniform Crossover Argon Cluster 
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 Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Instituto Superior de Engenharia de CoimbraCoimbraPortugal
  2. 2.Centro de Informática e Sistemas da Universidade de Coimbra (CISUC)CoimbraPortugal
  3. 3.Departamento de QuímicaUniversidade de CoimbraCoimbraPortugal

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