title: Real-coded memetic algorithms with crossover hill-climbing.
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.
authors.affiliation: Dept. of Computer Science and A.I., University of Granada, 18071 Granada, Spain. lozano@decsai.ugr.es
authors.forename: Manuel
authors.initials: M
authors.lastname: Lozano
authors.forename: Francisco
authors.initials: F
authors.lastname: Herrera
authors.forename: Natalio
authors.initials: N
authors.lastname: Krasnogor
authors.forename: Daniel
authors.initials: D
authors.lastname: Molina
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008432:Mathematical Computing; D008957:Models, Genetic; D015233:Models, Statistical