Title :
Coevolutionary GA with schema extraction by machine learning techniques and its application to knapsack problems
Author :
Handa, H. ; Horiuchi, T. ; Katai, O. ; Kaneko, T. ; Konishi, T. ; Baba, M.
Author_Institution :
Fac. of Eng., Okayama Univ., Japan
Abstract :
The authors introduce a novel coevolutionary genetic algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in a natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm the effectiveness of the proposed method
Keywords :
genetic algorithms; knapsack problems; learning (artificial intelligence); search problems; CGA; GA populations; H-GA; P-GA; coevolutionary GA; coevolutionary genetic algorithm; computational simulations; knapsack problems; machine learning techniques; multidimensional knapsack problems; schema extraction; search ability; Computational modeling; Cultural differences; Data mining; Genetic algorithms; Informatics; Machine learning; Machine learning algorithms; Multidimensional systems; Search methods; Stochastic processes;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934329
