Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Parallel Optimization of Evolutionary Algorithms
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Simulated Jumping in Genetic Algorithms for a set of test functions
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
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The goal of this paper is to study if there is a dependency between the probability of crossover with the genetic similarity (in terms of hamming distance) and the fitness difference between two individuals. In order to see the relation between these parameters, we will find a neural network that simulates the behavior of the probability of crossover with these differences as inputs. An evolutionary algorithm will be used, the goodness of every network being determined by a genetic algorithm that optimizes a well-known function.