Designing Evolutionary Algorithms for Dynamic Optimization Problems

Most research in evolutionary computation focuses on optimization of static, non-changing problems. Many real-world optimization problems, however, are dynamic, and optimization methods are needed that are capable of continuously adapting the solution to a changing environment. If the optimization problem is dynamic, the goal is no longer to find the extrema, but to track their progression through the space as closely as possible. In this chapter, we suggest a classification of dynamic optimization problems, and survey and classify a number of the most widespread techniques that have been published in the literature so far to make evolutionary algorithms suitable for changing optimization problems. After this introduction to the basics, we will discuss in more detail two specific approaches, pointing out their deficiencies and potential. The first approach is based on memorization, the other one uses a novel multi-population structure.