A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
The One-Machine Problem with Earliness and Tardiness Penalties
Journal of Scheduling
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
Expert Systems with Applications: An International Journal
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This paper proposed a new algorithm, termed as Self-Guided genetic algorithm, which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM). Previous 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 single machine 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, it could be a break-through in the branch of EAPM.