Optimal answer generation by equivalent transformation incorporating multi-objective genetic algorithm

This paper proposes a framework for time-efficiently finding multiple answers that meet logical constraints and are Pareto-optimal objective functions. The proposed determines set of optimal by iteratively replacing definite clauses. Clause replacement is performed using SAT solver consisting equivalent transformation rules (ETRs). An ETR replaces clause with one or more clauses while preserving the declarative meaning union original problem To efficiently find answers, in this paper, we define new class ETRs generated based on evaluation results multi-objective genetic algorithm (MOGA) propose method generating belong to class. belonging help replace according user objectives such as cost–benefit performance, reliability, financial constraints. Thus, uses addition extant classes can preferentially produce answer objectives. Experimental indicate significantly reduce computation time memory usage necessary determine