Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Efficient linkage discovery by limited probing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Detecting the epistatic structure of generalized embedded landscape
Genetic Programming and Evolvable Machines
Lower and upper bounds for linkage discovery
IEEE Transactions on Evolutionary Computation
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Estimation of distribution algorithms construct an explicit model of the problem to be solved, and then use this model to guide the search for good solutions. For an important class of fitness functions, namely those with k-bounded epistasis, it is possible to construct a complete explicit representation of the fitness function by sampling the fitness function. A very natural model of the problem to be solved is the Boltzmann distribution of the fitness function, which is an exponential of the fitness normalized to a probability distribution. As the exponentiation factor (inverse temperature) of the Boltzmann distribution is increased, probability is increasingly concentrated on the set of optimal points. We show that for fitness functions of k-bounded epistasis that satisfy an additional property called the running intersection property, an explicit computable exact factorization of the Boltzmann distribution with an arbitrary exponentiation factor can be constructed. This factorization allows the Boltzmann distribution to be efficiently sampled, which leads to an algorithm which finds the optimum with high probability.