The field of evolutionary dynamic optimization is concerned with the study and application of evolutionary algorithms to dynamic optimization problems: a significant number of new algorithms have been proposed in recent years that are designed specifically to overcome the limitations faced by traditional algorithms in the dynamic domain. Subsequently, a wealth of empirical studies have been published that evaluate the performance of these algorithms on a variety of benchmark problems. However, very few theoretical results have been obtained during this time. This relative lack of theoretical findings makes it difficult to fully assess the strengths and weaknesses of the individual algorithms. In this chapter we provide a review of theoretical advances in evolutionary dynamic optimization. In particular, we argue the importance of theoretical results, highlight the challenges faced by theoreticians and summarise the work that has been done to date. We subsequently identify relevant directions for future research. © 2013 Springer-Verlag Berlin Heidelberg.
|Title of host publication
|Evolutionary Computation for Dynamic Optimization Problems
|Shengxiang YANG, Xin YAO
|Number of pages
|Published - 2013
|Studies in Computational Intelligence