Adaptive control of the number of crossed genes in many-objective evolutionary optimization

  • Authors:

  • Hiroyuki Sato;Carlos A. Coello Coello;Hernán E. Aguirre;Kiyoshi Tanaka

  • Affiliations:

  • Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan;Departamento de Computación, CINVESTAV-IPN, México, D.F., México;Faculty of Engineering, Shinshu University, Nagano, Japan;Faculty of Engineering, Shinshu University, Nagano, Japan

  • Venue:
  • LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
  • Year:
  • 2012

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Abstract

To realize effective genetic operation in evolutionary many-objective optimization, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using an user-defined parameter α. CCG with small α significantly improves the search performance of multi-objective evolutionary algorithm in many-objective optimization by keeping small the number of crossed genes. However, to achieve high search performance by using CCG, we have to find out an appropriate parameter α by conducting many experiments. To avoid parameter tuning and automatically find out an appropriate α in a single run of the algorithm, in this work we propose an adaptive CCG which adopts the parameter α during the solutions search. Simulation results show that the values of α controlled by the proposed method converges to an appropriate value even when the adaptation is started from any initial values. Also we show the adaptive CCG achieves more than 80% with a single run of the algorithm for the maximum search performance of the static CCG using an optimal α*.