1995 IEEE International Conference on Evolutionary Computation, the University of Western Australia, Perth, Western Australia, 29 November-1 December, 1995, Volume 2 |
From inside the book
Results 1-3 of 85
Page 616
... individual produces may exceed the number of alleles of the individual's pivot gene . We will refer to the new hybrid that we have de- veloped for solving the exemplification CSP as AGS ( A Genetic / Systematic search hybrid ) . AGS ...
... individual produces may exceed the number of alleles of the individual's pivot gene . We will refer to the new hybrid that we have de- veloped for solving the exemplification CSP as AGS ( A Genetic / Systematic search hybrid ) . AGS ...
Page 748
... individual player as a board . evaluation function [ 7 ] [ 8 ] . The system can then simulate the play of an individual by constructing the set of moves , applying the represented evalua- tion function to each move in the set , and ...
... individual player as a board . evaluation function [ 7 ] [ 8 ] . The system can then simulate the play of an individual by constructing the set of moves , applying the represented evalua- tion function to each move in the set , and ...
Page 811
... individuals . After all the individuals in a gen- eration have been produced , each individual learns 50 patterns 50 times . Those patterns contain 10 correct patterns ( all are the same ) and 40 incorrect ones . Ratio of correct answer ...
... individuals . After all the individuals in a gen- eration have been produced , each individual learns 50 patterns 50 times . Those patterns contain 10 correct patterns ( all are the same ) and 40 incorrect ones . Ratio of correct answer ...
Contents
A STUDY ON FINDING FUZZY RULES FOR SEMIACTIVE SUSPENSION | 502 |
Volume 1 | 583 |
ENVIRONMENTALLY CONSTRAINED ELECTRIC POWER DISPATCH WITH GENETIC | 624 |
Copyright | |
7 other sections not shown
Common terms and phrases
antibodies antigen applied approach Artificial automata average behavior cells chromosome color constraints convergence crossover operator defined dynamic encoding equation error evaluation evolution strategies evolutionary algorithms evolved example experiments Figure fitness function fitness value function smoothing fuzzy if-then rules fuzzy sets g-fGA gene Genetic Algorithms genetic operators Genetic Programming genotype global optimum hill climbing idiotope IEEE immune system individual initial population input layer linear LOGENPRO logic grammar Machine Learning method mobile robot mutation operator mutation rate neural networks node nonlinear objective function obtained offspring optimization output p-fGA paper parameters parent patterns performance PID controllers problem Proc produced proposed random randomly rectangles represent SAN DIEGO search space Section selection shown shows simulation slime moulds solution solve step stochastic strategy string structure Table technique tion total number travelling salesman problem University variables vector