IEEE International Conference on Evolutionary ComputationIEEE, 1998 - Algorithms |
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Page 18
... objective function . But with bicriteria LAN topology design problem , we can only calculate each objective value and can not simply evalu- ate its fitness value when the objective functions conflict with each other in practice . In ...
... objective function . But with bicriteria LAN topology design problem , we can only calculate each objective value and can not simply evalu- ate its fitness value when the objective functions conflict with each other in practice . In ...
Page 450
Best objective function value 10 0 10 6 104 102 100 C 100 200 300 Generation s = 0.5 s = 0.1 s = 0.01 Best objective function value 10 10 102 s = 0.5 s = 0.1 s = 0.01 Best objective function value 104 102 s = 0.5 100 10-2 s = 0.1 10 s ...
Best objective function value 10 0 10 6 104 102 100 C 100 200 300 Generation s = 0.5 s = 0.1 s = 0.01 Best objective function value 10 10 102 s = 0.5 s = 0.1 s = 0.01 Best objective function value 104 102 s = 0.5 100 10-2 s = 0.1 10 s ...
Page 811
function influences the rating of individuals in the popula- tion : better individuals have better chances to survive and reproduce . However , in NLP the ranking of individuals is not straightforward : apart from optimizing the objective ...
function influences the rating of individuals in the popula- tion : better individuals have better chances to survive and reproduce . However , in NLP the ranking of individuals is not straightforward : apart from optimizing the objective ...
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