An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Three interconnected parameters for genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
A method for identifying values for a genetic algorithm's probability of crossover, mutation rate, and selection pressure that promote the evolution of better results in fewer generations has recently been proposed. This approach, termed the Triple Parameter Hypothesis (TPH), derives these values from schema theory. However, the experiments previously used to test the hypothesis used schema distances that were the extreme ends of the spectrum. In the work presented here, we evaluate the parameters predicted by the hypothesis in a series of maintenance scheduling experiments which use schema distances in between these extremes. Results show that evolutionary runs which use parameters that satisfy the hypothesis statistically significantly outperform runs that use parameters that do not satisfy the hypothesis.