An evolving strategy for a multi-stage placement of charging stations for electrical cars is developed. Both an incremental as well as a decremental placement decomposition are evaluated on this Maximum Covering Location Problem. We show that an incremental Genetic Algorithm benefits from problem decomposition effects of having multiple stages and shows greedy behaviour.
Evolutionary computation and genetic algorithms (GAs) in particular have been applied very successfully to many real world application problems. However, the success or failure of applying Genetic Algorithms is highly dependent on how a problem is represented. Additionally, the number of free parameters makes applying these methods a science of its own, presenting a huge barrier to entry for beginners. This tutorial will give a summary on various representational aspects, discuss parametrization and their influence on the dynamics of GAs.
