Genetic Algorithms in a dynamically changing environment

Genetic Algorithms (GAs) are search methods based on principles of natural selection and genetics. GAs attempt to find optimal solutions to a given problem by manipulating a population of candidate solutions (individuals). In the real world, we always encounter the problems that need to be solved in a changing environment. This means that our algorithm needs to be dynamic or even adaptive to the changing environment. In this paper, we mainly deal with the adaptive GAs that have a new genetic operator called transformation instead of the traditional crossover. We use a dynamic problem generator to create a dynamically changing landscape and study the behavior of the transformation-based GAs in different parameter settings, such as transformation rate, mutation rate and segment replacement rate.