With this work we tackle, in a preliminary way, how to incorporate specific heuristic knowledge in the sampling phase. We propose two ways to do it and evaluate ...
In this work we make an initial proposal about the use of heuristic knowledge during the sampling phase. This fact is used by other evolutionary algorithms such ...
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We compare our proposed approach named Heuristic Selection based on Estimation of Distribution Algorithm (HSEDA) with three state-of-the-art algorithms for the ...
heuristic sampling, is a generalization of Knuth's original algorithm for estimating the efficiency of backtrack ... nique known as stratified sampling [2], based ...
The main idea of the proposed sampling algorithm is to apply a heuristic strategy to find a subset D0 with a minimum size m based on the defined scoring func-.
Estimation of distribution algorithms [1-5] are evolutionary algorithms that work with a multiset (or population sets) of candidate solutions (points). Figure 1 ...
Estimation of Distribution Algorithms (EDAs) are evolutionary methods that utilize an explicit distribution model instead of traditional genetic operators ...
Missing: Heuristic | Show results with:Heuristic
Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization that represent a more recent alternative to classical approaches.
Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods ...
Missing: Initial | Show results with:Initial
A heuristic sampling algorithm is proposed to generate the required subset by designing two scoring functions: one based on the chi-square test and the other ...
Missing: Initial Approach.