Effect of Local Search on Edge Histogram Based Sampling Algorithms for Permutation Problems

One of the most promising research directions that focus on eliminating the drawbacks of fixed, problem-independent genetic algorithms, is to look at the generation of new candidate solutions as a learning problem, and use a probabilistic model of selected solutions to generate the new ones [5,9,10]. The algorithms based on learning and sampling a probabilistic model of promising solutions to generate new candidate solutions are called probabilistic model-building genetic algorithms (PMBGAs) [9,10], estimation of distribution algorithms (EDAs) [7], or iterated density estimation algorithms (IDEAs) [1].

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