Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Niching with derandomized evolution strategies in artificial and real-world landscapes
Natural Computing: an international journal
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
DBSCAN-based multi-objective niching to approximate equivalent pareto-subsets
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Hype: An algorithm for fast hypervolume-based many-objective optimization
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
Variants of Evolutionary Algorithms for Real-World Applications
Variants of Evolutionary Algorithms for Real-World Applications
Omni-optimizer: a procedure for single and multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative non-dominated fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents a new algorithm, the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies distinct sets of non-dominated solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of a Pareto front. MNCA is demonstrated to identify a set of non-dominated fronts with maximum difference in decision vectors for a set of real-valued problems.