Finding the Right Hybrid Algorithm - A Combinatorial Meta-Problem

Benchmark comparisons tend to overlook the most important challenge in solving combinatorial problems: how to design an appropriate algorithm. For example an early version of Localizer incurred a factor 3 performance penalty when bench-marked against a ʹC implementation of GSAT, but I would recommend implementing a new local search algorithm in Localizer rather than ʹC every time. LiPSe CLP language supports the experimental process of seeking the right hybrid algorithm for the problem at hand. It offers high-level modelling and control features, extensibility and a wide range of constraint solvers which can cooperate in the solving of a problem. ently sought a new hybrid algorithm for a very unpromising class (SAT problems), and using ECLiPSe we were able to develop an algorithm which showed good performance on some very hard instances. We describe the process of exploring the space of hybrid algorithms for the problem class, and indicate the features of ECLiPSe that enabled us to find previously undiscovered algorithms. How to benchmark the solving of this “meta-problem” remains a topic of future research. clude by pointing out the advantages of an extensible platform, such as ECLiPSe, for developing sophisticated hybrid algorithms for large scale industrial combinatorial optimisation problems.