IEEE International Conference on Evolutionary ComputationIEEE, 1998 - Algorithms |
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Page 13
... proposed method and those of our former method in [ 12 ] . From Table 2 , we can see that a 100 % classification rate was obtained by the proposed method with less fuzzy if - then rules than the former method in [ 12 ] . In Figure 7 ...
... proposed method and those of our former method in [ 12 ] . From Table 2 , we can see that a 100 % classification rate was obtained by the proposed method with less fuzzy if - then rules than the former method in [ 12 ] . In Figure 7 ...
Page 587
... proposed approach with that of the initial rule sets . Results show that the rule set derived by the proposed approach is much more accurate than each initial rule set . 1. Introduction The objective of knowledge integration is to ...
... proposed approach with that of the initial rule sets . Results show that the rule set derived by the proposed approach is much more accurate than each initial rule set . 1. Introduction The objective of knowledge integration is to ...
Page 822
... proposed . The crossover technique works together the conven- tional crossovers arranged for the TSP such as partially mapped ( PMX ) , order ( OX ) , and cycle ( CX ) crossovers . Because a new child is sure to be located between two ...
... proposed . The crossover technique works together the conven- tional crossovers arranged for the TSP such as partially mapped ( PMX ) , order ( OX ) , and cycle ( CX ) crossovers . Because a new child is sure to be located between two ...
Contents
A Society Model 157 | 57 |
Extracting Rules from Fuzzy Neural Network by Particle Swarm Optimisation I74 | 74 |
APPLICATIONS | 90 |
Copyright | |
44 other sections not shown
Common terms and phrases
adaptive agents applied approach Artificial average behavior binary bits bond graph building blocks cell chromosome circuit cluster complexity components connections constraints convergence cost crossover crossover operator defined described distance distribution dynamic encoding environment error evaluation evolution evolution strategies evolutionary algorithms Evolutionary Computation evolutionary programming evolved experiments Figure fitness function fitness landscape fitness value fuzzy logic gene Genetic Algorithms genetic operators genetic programming genotype global optimum graph heuristic IEEE individuals initial input iterations learning Machine Learning matrix method mobile robot modules mutation operator mutation rate neural networks neurons niche node objective function obtained offspring optimal optimisation optimum output paper parameters parents path performance population problem proposed protein random randomly represent representation rithm rule scheduling search space selection shown shows simulation solution solve step strategy string subprograms Table target techniques tion tree variables vector weights