Real-coded Memetic Algorithms with crossover hill-climbing

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Real-coded Memetic Algorithms with crossover hill-climbing (2004)

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by Manuel Lozano , Francisco Herrera , Natalio Krasnogor , Daniel Molina

Venue:	Evolutionary Computation
Citations:	70 - 12 self

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@ARTICLE{Lozano04real-codedmemetic,
    author = {Manuel Lozano and Francisco Herrera and Natalio Krasnogor and Daniel Molina},
    title = {Real-coded Memetic Algorithms with crossover hill-climbing},
    journal = {Evolutionary Computation},
    year = {2004},
    pages = {3--273}
}

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Abstract

This paper presents a real-coded memetic algorithm that applies a crossover hillclimbing 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 selfadaptive 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.

Keyphrases

real-coded memetic algorithm crossover hill-climbing memetic algorithm high level problem instance population diversity selfadaptive capacity global search wide range different local search probability important aspect genetic operator crossover hillclimbing experimental result global local search balance real-parameter crossover operator

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