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- W2099758392 abstract "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." @default.
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- W2099758392 date "2005-06-25" @default.
- W2099758392 modified "2023-09-23" @default.
- W2099758392 title "On the convergence of an estimation of distribution algorithm based on linkage discovery and factorization" @default.
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- W2099758392 doi "https://doi.org/10.1145/1068009.1068126" @default.
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