Title :
Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways
Author :
Renders, Jean-Michel ; Bersini, Hugues
Author_Institution :
Lab. d´´Autom., Univ. Libre de Bruxelles, Belgium
Abstract :
Two methods of hybridizing genetic algorithms (GA) with hill-climbing for global optimization are investigated. The first one involves two interwoven levels of optimization-evolution (GA) and individual learning (hill-climbing)-which cooperate in the global optimization process. The second one consists of modifying a GA by the introduction of new genetic operators or by the alteration of traditional ones in such a way that these new operators capture the basic mechanisms of hill-climbing. The simplex-GA is one of the possibilities explained and tested. These two methods are applied and compared for the maximization of complex functions defined in high-dimensional real space
Keywords :
genetic algorithms; learning (artificial intelligence); optimisation; search problems; complex functions maximization; evolution; genetic algorithms; genetic operators; global optimization; high-dimensional real space; hill-climbing methods; individual learning; interwoven optimization levels; simplex; Data mining; Design methodology; Genetic algorithms; Genetic mutations; Optimization methods; Roads; Sampling methods; Testing;
Conference_Titel :
Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1899-4
DOI :
10.1109/ICEC.1994.349948
