Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
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This paper proposes a new non-generational genetic algorithm for multiobjective optimization. The novelty in this approach is the use of a moving average to evaluate two things: the number of dominated individuals and the sharing function. Moreover, the proposed solution does less number of comparisons when compared with similar techniques and approaches. This solution has been evaluated on three different problems that are found in the literature and compared to other approaches to tackle them. The results that one sees point to the idea that the proposed algorithm is a feasible and simpler way to deal with multiobjective problems.