Abstract

Abstract. Stochastic search algorithms are often robust, scalable problem solvers. In this paper, we concern ourselves with the class of stochastic search algorithms called stochastic sampling. Randomization in such a search framework can be an effective means of expanding search around a stochastic neighborhood of a strong domain heuristic. Specifically, we show that a value-biased approach can be more effective than the rank-biased approach of the heuristic-biased stochastic sampling algorithm. We also illustrate the effectiveness of value-biasing the starting configurations of a local hill-climber. We use the weighted tardiness scheduling problem to evaluate our approach.

Citations