2018/04/17 by Shengcai Liu, Ke Tang, Liu, Shengcai +3 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Model-Driven Software Engineering Techniques #cs.AI
paper · pdf · doi:10.48550/arxiv.1804.06088
arxiv created 2018/04/17 · openalex publication_date 2018/04/17 · arxiv updated 2018/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems. However, manually building such solver portfolios typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich design space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle each of them.This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping.The experimental results showed that the parallel portfolios constructed by the proposed method could achieve consistently superior performances to the ones constructed by the state-of-the-art ACPP methods,and could even rival sophisticated hand-designed parallel solvers.