Comparing the performance of the genetic and local search algorithms for solving the satisfiability problems

Abstract

The satisfiability problem (SAT), as one of the six basic core NP-complete problems, has been the deserving object of many studies in the last two decades [1]. Stochastic local search and genetic algorithms are two current state-of-the-art techniques for solving the SATs. GASAT [2,1] and SAT-WAGA [4] are two current state-of-the-art genetic algorithms for solving SATs. Beside, the discrete lagrange-multiplier (DLM) [8–10] and the exponentiated subgradient algorithms (ESG) [12] are the current state-of-the-art local search algorithms for solving SATs. In this paper, we compare DLM and ESG with GASAT and SAT-WAGA. We show that the performance of the local search-based algorithms: DLM and ESG is better than the performance of the genetic-based algorithms: GASAT and SAT-WAGA. We further analyze the results of the comparisons. We hope these comparisons give light to the researchers while researching in genetic and local search algorithms, help understanding of the reasons for these algorithms’ performance and behaviour and to the new researchers who are in the process of choosing research direction.