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Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
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Evolution and Optimum Seeking: The Sixth Generation
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Genetic Algorithms in Search, Optimization and Machine Learning
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Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
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Evolutionary Computation
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Multi-objective diversity maintenance
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Evolutionary Computation
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ISTASC'06 Proceedings of the 6th WSEAS International Conference on Systems Theory & Scientific Computation
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Expert Systems with Applications: An International Journal
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ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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Evolutionary Computation
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Engineering Applications of Artificial Intelligence
Exploration and exploitation in evolutionary algorithms: A survey
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International Journal of Robotics Research
Journal of Global Optimization
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A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is toplevel.