Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Fuzzy Sets and Systems - Featured Issue: Selected papers from ACIDCA 2000
Multi-objective Co-operative Co-evolutionary Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Searching in the Presence of Noise
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Design of combinational logic circuits through an evolutionary multiobjective optimization approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization
Artificial Intelligence Review
A multi-objective genetic algorithm for robust design optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparing a coevolutionary genetic algorithm for multiobjective optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
New methods for competitive coevolution
Evolutionary Computation
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Reliability-based multi-objective optimization using evolutionary algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Multi-objective optimization of problems with epistemic uncertainty
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
On the robustness of population-based versus point-basedoptimization in the presence of noise
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A general noise model and its effects on evolution strategy performance
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
A distributed Cooperative coevolutionary algorithm for multiobjective optimization
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks
IEEE Transactions on Neural Networks
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Pareto-dominance in noisy environments
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
Evolutionary algorithms are stochastic search methods that are efficient and effective for solving sophisticated multi-objective (MO) problems. Advances made in the field of evolutionary multi-objective optimization (EMO) are the results of two decades worth of intense research, studying various topics that are unique to MO optimization. However many of these studies assume that the problem is deterministic and static, and the EMO performance generally deteriorates in the presence of uncertainties. In certain situations, the solutions found may not even be implementable in practice. In this chapter, the challenges faced in handling three different forms of uncertainties in EMO will be discussed, including 1) noisy objective functions, 2) dynamic MO fitness landscape, and 3) robust MO optimization. Specifically, the impact of these uncertainties on MO optimization will be described and the approaches/modifications to basic algorithm design for better and robust EMO performance will be presented.