IEEE, 1997 - Computers - 724 pages
This collection of articles from the IEEE International Conference covers a wide range of aspects of evolutionary computing. This includes principles of evolutionary computation such as adaption and self-adaption, variation operators, representational issues and theoretical investigations.
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Towards a Theory of PopulationBased Incremental Learning
The Gamblers Ruin Problem Genetic Algorithms and the Sizing of Populations
The Mixing Evolutionary Algorithm independent selection and allocation of trials
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adaptive applied approach average binary bits building blocks cell chromosome circuit clusters color Computer Science Conference on Genetic constraints convergence crossover crossover operator defined distance DNA computing domain dynamic edges encoding evaluation evolution evolutionary algorithm Evolutionary Computation Evolutionary Programming evolved example experiments Figure fitness function fitness landscape fitness value fuzzy genes Genetic Algorithms genetic operators genetic programming genotype global graph heuristic hexapod robot hybrid IEEE implementation improve individual initial population input International Conference iterations learning Machine Learning method molecules mutation operator mutation rate neural networks node obtained offspring optimisation optimization problems optimum paper parameters parents path performance probability Proc proposed random randomly recombination represent representation rithm robot runs search space selection sequence simulation solution solve splicing rules step strategy string structure Table techniques test tube tion Traveling Salesman Problem variables vector