Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
EA models and population fixed-points versus mutation rates for functions of unitation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Redundant genes and the evolution of robustness
Proceedings of the 8th annual conference on Genetic and evolutionary computation
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
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We introduce quotient graphs for modeling neutrality in evolutionary search. We demonstrate that for a variety of evolutionary computing problems, search can be characterized by grouping genes with similar fitness and search behavior into quotient sets. These sets can potentially reduce the degrees of freedom needed for modeling evolutionary behavior without any loss of accuracy in such models. Quotients sets, which are also shown to be Markov models, aid in understanding the nature of search. We explain how to calculate Fitness Distance Correlation (FDC) through quotient graphs, and why different problems can have the same FDC but have different dynamics. Quotient models also allow visualization of correlated evolutionary drives.