Fast probabilistic modeling for combinatorial optimization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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Computational Optimization and Applications
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PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Expert Systems with Applications: An International Journal
A combinatorial particle swarm optimisation for solving permutation flowshop problems
Computers and Industrial Engineering
A discrete differential evolution algorithm for the permutation flowshop scheduling problem
Computers and Industrial Engineering
IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
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This paper proposed Self-Guided genetic algorithm, which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM), to solve strong NP-Hard flowshop scheduling problems with the minimization of makespan. Most EAPM research explicitly used the probabilistic model from the parental distribution, then generated solutions by sampling from the probabilistic model without using genetic operators. Although EAPM is promising in solving different kinds of problems, Self-Guided GA doesn't intend to generate solution by the probabilistic model directly because the time-complexity is high when we solve combinatorial problems, particularly the sequencing ones. As a result, the probabilistic model serves as a fitness surrogate which estimates the fitness of the new solution beforehand in this research. So the probabilistic model is used to guide the evolutionary process of crossover and mutation. This research studied the flowshop scheduling problems and the corresponding experiment were conducted. From the results, it shows that the Self-Guided GA outperformed other algorithms significantly. In addition, Self-Guided GA works more efficiently than previous EAPM. As a result, Self-Guided GA is promising in solving the flowshop scheduling problems.