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