Boundary Evolution Algorithm for SAT-NP

TL;DR

The paper introduces a Boundary Evolution Algorithm (BEA) that enhances hierarchical genetic algorithms for SAT problems by integrating boundary-aware crossover and mutation, leading to improved success rates and convergence speed.

Contribution

It proposes a novel boundary evolution approach with annealing, improving hierarchical genetic algorithms for SAT by maintaining boundary elasticity and optimal individuals.

Findings

BEA outperforms traditional genetic algorithms in success rate.

BEA achieves faster convergence in SAT problem solving.

Boundary elasticity enhances evolutionary algorithm performance.

Abstract

A boundary evolution Algorithm (BEA) is proposed by simultaneously taking into account the bottom and the high-level crossover and mutation, ie., the boundary of the hierarchical genetic algorithm. Operators and optimal individuals based on optional annealing are designed. Based on the numerous versions of genetic algorithm, the boundary evolution approach with crossover and mutation has been tested on the SAT problem and compared with two competing methods: a traditional genetic algorithm and another traditional hierarchical genetic algorithm, and among some others. The results of the comparative experiments in solving SAT problem have proved that the new hierarchical genetic algorithm based on simulated annealing and optimal individuals (BEA) can improve the success rate and convergence speed considerably for SAT problem due to its avoidance of both divergence and loss of optimal individuals, and by coronary, conducive to NP problem. Though more extensive comparisons are to be made on more algorithms, the consideration of the boundary elasticity of hierarchical genetic algorithm is an implication of evolutionary algorithm.