Evolutionary optimization in uncertain environments-a survey

@article{citeulike:2601360, abstract = {Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.}, author = {Jin, Yaochu and Branke, J. }, booktitle = {Evolutionary Computation, IEEE Transactions on}, citeulike-article-id = {2601360}, doi = {http://dx.doi.org/10.1109/TEVC.2005.846356}, journal = {Evolutionary Computation, IEEE Transactions on}, keywords = {evolutionary-computation, metaheuristic, noise, objective-function, problem}, number = {3}, pages = {303--317}, posted-at = {2008-07-03 15:43:40}, priority = {5}, title = {Evolutionary optimization in uncertain environments-a survey}, url = {http://dx.doi.org/10.1109/TEVC.2005.846356}, volume = {9}, year = {2005} }

TY - JOUR ID - citeulike:2601360 L3 - citeulike-article-id:2601360 TI - Evolutionary optimization in uncertain environments-a survey T2 - Evolutionary Computation, IEEE Transactions on JF - Evolutionary Computation, IEEE Transactions on VL - 9 IS - 3 SP - 303 EP - 317 N2 - Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested. KW - evolutionary-computation KW - metaheuristic KW - noise KW - objective-function KW - problem AU - Jin, Yaochu AU - Branke, J PY - 2005/// UR - http://dx.doi.org/10.1109/TEVC.2005.846356 ER -