Abstract
The hybridisation of systematic and stochastic search is an active research area with potential benefits for real-world combinatorial problems. This paper shows that randomising the backtracking component of a systematic backtracker can improve its scalability to equal that of stochastic local search. The hybrid may be viewed as stochastic local search in a constrained space, cleanly combining local search with constraint programming techniques. The approach is applied to two very different problems. Firstly a hybrid of local search and constraint propagation is applied to hard random 3-SAT problems, and is the first constructive search algorithm to solve very large instances. Secondly a hybrid of local search and branch-and-bound is applied to low-autocorrelation binary sequences (a notoriously difficult communications engineering problem), and is the first stochastic search algorithm to find optimal solutions. These results show that the approach is a promising one for both constraint satisfaction and optimisation problems.
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Prestwich, S. (2000). A Hybrid Search Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences. In: Dechter, R. (eds) Principles and Practice of Constraint Programming – CP 2000. CP 2000. Lecture Notes in Computer Science, vol 1894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45349-0_25
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DOI: https://doi.org/10.1007/3-540-45349-0_25
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