Evolutionary multi-objective quantum control experiments with the covariance matrix adaptation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary multiobjective optimization in noisy problem environments
Journal of Heuristics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Uncertainty of constraint function in evolutionary multi-objective optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Avoidance of constraint violation for experiment-based evolutionary multi-objective optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An investigation on noise-induced features in robust evolutionary multi-objective optimization
Expert Systems with Applications: An International Journal
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Multiobjective optimization of temporal processes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A territory defining multiobjective evolutionary algorithms and preference incorporation
IEEE Transactions on Evolutionary Computation
Enhancing diversity for average ranking method in evolutionary many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An evolutionary computing approach to robust design in the presence of uncertainties
IEEE Transactions on Evolutionary Computation
Accumulative sampling for noisy evolutionary multi-objective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multi-swarm co-evolutionary paradigm for dynamic multi-objective optimisation problems
International Journal of Intelligent Information and Database Systems
Noise analysis compact genetic algorithm
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
On optimizing a bi-objective flowshop scheduling problem in an uncertain environment
Computers & Mathematics with Applications
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
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In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity