Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A new design optimization framework based on immune algorithm and Taguchi's method
Computers in Industry
IEEE Transactions on Evolutionary Computation
Capabilities of EMOA to detect and preserve equivalent pareto subsets
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Test problems based on Lamé superspheres
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Integrating decision space diversity into hypervolume-based multiobjective search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Pareto set and EMOA behavior for simple multimodal multiobjective functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Omni-optimizer: a procedure for single and multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Until recently, the main focus of researchers that develop algorithms for evolutionary multi-objective optimization has been the creation of mechanisms capable of obtaining sets of solutions that are as close as possible to the true Pareto front of the problem and also as diverse as possible in the objective space, to properly cover such front. However, an adequate maintenance of diversity in the decision space is also important, to efficiently solve several classes of problems and even to facilitate the post-optimization decision making process. This aspect has been widely studied in evolutionary single-objective optimization, what led to the development of several diversity maintenance techniques. Among them, the recently proposed concentration-based artificial immune network (cob-aiNet), which is capable of self-regulating the population size, presented promising results in multimodal problems. So, it is extended here to deal with multi-objective problems that require a proper maintenance of diversity in the decision space.