Combining competent crossover and mutation operators: a probabilistic model building approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Why is parity hard for estimation of distribution algorithms?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Sporadic model building for efficiency enhancement of the hierarchical BOA
Genetic Programming and Evolvable Machines
Using previous models to bias structural learning in the hierarchical BOA
Proceedings of the 10th annual conference on Genetic and evolutionary computation
iBOA: the incremental bayesian optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hierarchical BOA, cluster exact approximation, and ising spin glasses
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
Graph clustering based model building
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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The intrinsic feature of Estimation of Distribution Algorithms lies in their ability to learn and employ probabilistic models over the input spaces. Discovery of the appropriate model usually implies a computationally expensive comprehensive search, where many models are proposed and evaluated in order to find the best value of some model discriminative scoring metric. This paper presents basic results demonstrating how simple variable correlation data can be extended and used to efficiently guide the model search, decreasing the number of model evaluations by several orders of magnitude and without significantly affecting model quality. As a case study, the O(n3) model building of the Extended Compact Genetic Algorithm is successfully replaced by a correlation guided search of linear complexity which infers the perfect problem structures on the test suites.