on the characteristics of sequential decision problems and their impact on evolutionary computation
In this paper we argue that the performance of evolutionary computation on sequential decision problems strongly depends on the characteristics of the task at hand. On "error-avoidance" tasks, in which the decision process is interrupted every time a bad decision is made, evolutionary methods usually perform well. However, the same is not true for "goal-seeking" tasks, in which the objective is to find one or more target locations. In this case, it is not clear how to evaluate the unsuccessful candidate solutions, and the performance of evolutionary computation may depend on prior knowledge about the problem. Even though the hypothesis of this paper is essentially a conceptual one, we support our ideas with a computational experiment.
sequential decision problems evolutionary computation reinforcement learning