Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
A Tabu-Based Exploratory Evolutionary Algorithmfor Multiobjective Optimization
Artificial Intelligence Review
Tabu-Based Exploratory Evolutionary Algorithm for Effective Multi-objective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Virtual design and testing of protective packaging buffers
Computers in Industry
A dual-population genetic algorithm for adaptive diversity control
IEEE Transactions on Evolutionary Computation
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
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It is not clear that the current distinction between crossover and mutation is necessary. In this paper, we show that it is possible to implement one and only one general operator which can specialize crossover or mutation operator. In this work we intend to investigate this alternative. Our approach consists in inserting doubles in the population of chromosomes. This article argues that explicit mutations are unnecessary. Indeed, in dGAs, without mutation operator, chromosomes undergo the mutation effect. The dual genetic search provides a source of power for searching in changing environment. Within this paper, a first effort is presented towards incorporating the feature of self-adaptation into GAs by using adaptive mutation rates. Finally, we study the effects of explicit mutation on a dual search space. We show that a contraction of the Hamming distance is induced from mutation. As a consequence, a dGA allows to increase the capabilities of evolution on rugged fitness landscapes.