Treating Some Constraints as Hard Speeds up the ESG Local Search Algorithm
Local search (LS) methods for solving constraint satisfaction problems (CSP) such as GSAT, WalkSAT and DLM starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment, using the penalty function to determine a better neighbour valuations to move to. It repeats this process until it finds a solution that satisfies all constraints. ICM considers some of the constraints as hard constraints that are always satisfied. In this way, the constraints reduce the possible neighbours in each move and hence the overall search space. We choose the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach any solution from any valuation in this space. In this paper, we incorporate ICM into one of the most recent local search algorithm, ESG, and we show the improvement of the new algorithm.
KeywordsLocal Search Constraint Satisfaction Problem Local Search Algorithm Hard Constraint Good Neighbour
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