  The Boolean satisfiability problem (SAT) is a famous NP-complete problem in
computer science. An effective way for solving a satisfiable SAT problem is the
stochastic local search (SLS). However, in this method, the initialization is
assigned in a random manner, which impacts the effectiveness of SLS solvers. To
address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a
solution prediction model, which boosts SLS by changing initialization
assignments with a neural network. We evaluated NLocalSAT on five SLS solvers
(CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random
track of SAT Competition 2018. The experimental results show that solvers with
NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.
