Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A new method for linkage learning in the ECGA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Genetic and Evolutionary Computation Conference
Optimal mixing evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Advanced neighborhoods and problem difficulty measures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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We define the linkage model evolvability and the evolvability-based fitness distance correlation. These measures give an insight in the search characteristics of linkage model building genetic algorithms. We apply them on the linkage tree genetic algorithm for deceptive trap functions and the nearest-neighbor NK-landscape problem. Comparisons are made between linkage trees, based on mutual information, and random trees which ignore similarity in the population. On a deceptive trap function, the measures clearly show that by learning the linkage tree the problem becomes easy for the LTGA. On the nearest-neighbor NK-landscape the evolvability analysis shows that the LTGA does capture enough of the structure of the problem to solve it reliably and efficiently even though the linkage tree cannot represent the overlapping epistatic information in the NK-problem. The linkage model evolvability measure and the evolvability-based fitness distance correlation prove to be useful tools to get an insight into the search properties of linkage model building genetic algorithms.