The effective self-guided genetic algorithm (SGGA) which we proposed is based on the characteristics of a hybrid flow shop scheduling problem. A univariate probability model based on workpiece permutation is introduce...
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The effective self-guided genetic algorithm (SGGA) which we proposed is based on the characteristics of a hybrid flow shop scheduling problem. A univariate probability model based on workpiece permutation is introduced together with a bivariate probability model based on a similar workpiece blocks. An approach to updating a probability model parameters is given based on superior individuals. A novel probability calculation function is proposed taking advantages of statistical learning information provided by univariate and bivariate probabilistic model to calculate the probability of workpieces located in different positions. A method for evaluating the quality of individual candidates generated by GA crossover and mutation operators is suggested for selecting promising and excellent individual candidates as offspring. Simulation results show that the SGGA has excellent performance and robustness.
Estimation of Distribution algorithms (EDAs) have recently been recognized as a prominent alternative to traditional evolutionary algorithms due to their increasing popularity. The core of EDAs is a probabilistic mode...
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Estimation of Distribution algorithms (EDAs) have recently been recognized as a prominent alternative to traditional evolutionary algorithms due to their increasing popularity. The core of EDAs is a probabilistic model which directly impacts performance of the algorithm. Previous EDAs have used a univariate, bi-variate, or multi-variable probabilistic model each time. However, application of only one probabilistic model may not represent the parental distribution well. This paper advocates the importance of using ensemble probabilistic models in EDAs. We combine the univariate probabilistic model with the bi-variate probabilistic model which learns different population characteristics. To explain how to employ the two probabilistic models, we proposed the Ensemble self-guided genetic algorithm (eSGGA). The extensive computation results on two NP-hard scheduling problems indicate the advantages of adopting two probabilistic models. Most important of all, eSGGA can avoid the computation effort overhead when compared with other EDAs employing two models. As a result, this paper might point out a next generation approach for EDAs. (c) 2012 Elsevier B.V. All rights reserved.
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