Proceedings of the ... IEEE Conference on Evolutionary Computation, Volume 2IEEE, 1995 - Algorithms |
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Page 734
... ( Logenpro ) The problem of inducing programs can be reformulated as a search for a highly fit program in the space of all possible programs in the language specified by the logic grammar . In LOGENPRO , populations of programs are ...
... ( Logenpro ) The problem of inducing programs can be reformulated as a search for a highly fit program in the space of all possible programs in the language specified by the logic grammar . In LOGENPRO , populations of programs are ...
Page 735
... LOGENPRO to learn logic programs from noisy and imperfect training examples . An empirical comparison of LOGENPRO with FOIL ( the publicly available version of FOIL , version 6.0 , is used in the experiment ) and mFOIL [ 1 ] in the ...
... LOGENPRO to learn logic programs from noisy and imperfect training examples . An empirical comparison of LOGENPRO with FOIL ( the publicly available version of FOIL , version 6.0 , is used in the experiment ) and mFOIL [ 1 ] in the ...
Page 739
... LOGENPRO . We evaluate the performance of LOGENPRO and Koza's ADF using populations of 100 and 1000 programs respectively . 140 120 M A GP with ADF : Population = 1000 O Logenpro : Population = 100 minimum value of I ( M , i , z ) for ...
... LOGENPRO . We evaluate the performance of LOGENPRO and Koza's ADF using populations of 100 and 1000 programs respectively . 140 120 M A GP with ADF : Population = 1000 O Logenpro : Population = 100 minimum value of I ( M , i , z ) for ...
Contents
Volume 1 | 583 |
ENVIRONMENTALLY CONSTRAINED ELECTRIC POWER DISPATCH WITH GENETIC | 624 |
GENETIC OPERATORS USING VIRAL MODELS | 652 |
Copyright | |
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action adaptive antibodies applied approach Artificial average behavior cells characteristics chromosome classifier compared consists constraints convergence crossover defined described determine developed distance distribution dynamic effect encoding energy equation error estimation evaluation evolution evolutionary evolved example experiments field Figure fitness function fuzzy gene Genetic Algorithms given global individual initial input interaction introduced learning logic machine means mechanism method move mutation natural neural networks objective obtained offspring operator optimization organisms output parameters parent patterns performance population position presented probability problem produced programming proposed random randomly range References represent respectively robot rules runs selection shown shows simple simulation smoothing solution solve space step strategy string structure Table technique tion unit University variables weight