Solving satisfiability problems by fluctuations: The dynamics of stochastic local search algorithms

Solving satisfiability problems by fluctuations: The dynamics of stochastic local search algorithms

Stochastic local search algorithms are frequently used to numerically solve hard combinatorial optimization or decision problems. We give numerical and approximate analytical descriptions of the dynamics of such algorithms applied to random satisfiability problems. We find two different dynamical regimes, depending on the number of constraints per variable: For low constraintness, the problems ...

Combining cellular genetic algorithms and local search for solving satisfiability problems

A new parallel hybrid method for solving the satissa-bility problem that combines cellular genetic algorithms and the random walk (W SAT) strategy of GSAT is presented. The method, called CGWSAT, uses a cellular genetic algorithm to perform a global search on a random initial population of candidate solutions and a local selective generation of new strings. Global search is specialized in local...

Solving Satisfiability Problems with Genetic Algorithms

We show how to solve hard 3-SAT problems using genetic algorithms. Furthermore, we explore other genetic operators that may be useful to tackle 3-SAT problems, and discuss their pros and cons.

Stochastic Local Search for Satisfiability Modulo Theories

Satisfiability Modulo Theories (SMT) is essential for many practical applications, e.g., in hardand software verification, and increasingly also in other scientific areas like computational biology. A large number of applications in these areas benefit from bit-precise reasoning over finite-domain variables. Current approaches in this area translate a formula over bit-vectors to an equisatisfia...

Stochastic Systematic Search Algorithms for Satisfiability