Heuristic Selection for Stochastic Search Optimization: Modeling Solution Quality by Extreme Value Theory

The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristic-biased stochastic sampling (HBSS) and value-biased stochastic sampling (VBSS), wherein a heuristic is used to bias a stochastic policy for choosing among alternative branches in the search tree. One complication in getting the most out of algorithms like HBSS and VBSS in a given problem domain is the need to identify the most effective search heuristic. In many domains, the relative performance of various heuristics tends to vary across different problem instances and no single heuristic dominates. In such cases, the choice of any given heuristic will be limiting and it would be advantageous to gain the collective power of several heuristics. Toward this goal, this paper describes a framework for integrating multiple heuristics within a stochastic sampling search algorithm. In its...

• Type: Conference
• Year: 2004
• Where: CP
• Authors: Vincent A. Cicirello, Stephen F. Smith
• Keywords: CP 2004, Heuristic, Programming Languages, Stochastic Sampling, Stochastic Sampling Algorithm