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
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HoriuchiTadashi en-aut-sei=Horiuchi en-aut-mei=Tadashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KanekoTakeshi en-aut-sei=Kaneko en-aut-mei=Takeshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Osaka University affil-num=3 en-affil= kn-affil=Kyoto University affil-num=4 en-affil= kn-affil=Kyoto University affil-num=5 en-affil= kn-affil=Okayama University affil-num=6 en-affil= kn-affil=Okayama University en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=knapsack problems kn-keyword=knapsack problems en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=search problems kn-keyword=search problems END start-ver=1.4 cd-journal=joma no-vol=2 cd-vols= no-issue= article-no= start-page=1184 end-page=1189 dt-received= dt-revised= dt-accepted= dt-pub-year=2000 dt-pub=20007 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A new fitness function for discovering a lot of satisfiable solutions in constraint satisfaction problems en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this paper, we discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Kyoto University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University en-keyword=constraint theory kn-keyword=constraint theory en-keyword=functions kn-keyword=functions en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=operations research kn-keyword=operations research END