Using automatic programming to generate state-of-the-art algorithms for random 3-SAT
The paper focuses on automatic programming and how to synthesize stochastic local search algorithms using automatic design of algorithms through evolution (ADATE). The main goal is to provide support for the hypothesis that automatic programming can generate competitive heuristic algorithms. A well studied and highly optimized SAT solver, G2WSAT, is used as a case study. The results indicate that automatic programming is an effective design tool for heuristics and that there may be many well studied optimization problems that could benefit from the ADATE system and the specification methodology that we describe.
automatic programming sat local search program induction machine learning