Real-coded memetic algorithms with crossover hill-climbing

  • Authors:

  • Manuel Lozano;Francisco Herrera;Natalio Krasnogor;Daniel Molina

  • Affiliations:

  • Dept. of Computer Science and A.I., University of Granada, 18071 - Granada, Spain;Dept. of Computer Science and A.I., University of Granada, 18071 - Granada, Spain;Automatic Scheduling, Optimisation and Planning Group, School of Computer Science and IT, Jubilee Campus, University of Nottingham, Nottingham, NG8 1BB, United Kingdom;Dept. of Computer Science and A.I., University of Granada, 18071 - Granada, Spain

  • Venue:
  • Evolutionary Computation - Special issue on magnetic algorithms
  • Year:
  • 2004

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Abstract

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.