Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
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This paper deals with the analysis of genetic operators for a multi-objective flow-shop problem. The analysis is based on the influence of parental distance on the number of non-dominated solutions that are generated. The results of the analysis allow us to select the best combination of operators to deal with a specific problem. Simulation results show that using our design approach we can easily improve specific results recently available in the literature.