IEEE International Conference on Evolutionary ComputationIEEE, 1998 - Algorithms |
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Page 181
... Random - start local search The initial solutions for random - start local search were obtained as follows : randomly choose one of the nodes to visit on the map ( from those which are not yet assigned ) , and randomly assign it to a ...
... Random - start local search The initial solutions for random - start local search were obtained as follows : randomly choose one of the nodes to visit on the map ( from those which are not yet assigned ) , and randomly assign it to a ...
Page 501
... random initialization flips at least once . Hence , the average time until each of these bits has tried to flip at least once is a lower bound on the considered average time . If a random variable X takes only positive integers , its ...
... random initialization flips at least once . Hence , the average time until each of these bits has tried to flip at least once is a lower bound on the considered average time . If a random variable X takes only positive integers , its ...
Page 556
... Random Walk Seln . Prop . Seln . Rank Seln . 80 Bin . Tourn . Seln . Random Walk Seln . 70 Allele Loss 60 50 40 30 100 20 10 10-1 0 10 20 30 40 50 60 Number of Generations 0 70 80 90 100 0 10 20 30 40 50 60 70 Number of Generations 80 ...
... Random Walk Seln . Prop . Seln . Rank Seln . 80 Bin . Tourn . Seln . Random Walk Seln . 70 Allele Loss 60 50 40 30 100 20 10 10-1 0 10 20 30 40 50 60 Number of Generations 0 70 80 90 100 0 10 20 30 40 50 60 70 Number of Generations 80 ...
Contents
A Society Model 157 | 57 |
Extracting Rules from Fuzzy Neural Network by Particle Swarm Optimisation I74 | 74 |
APPLICATIONS | 90 |
Copyright | |
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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