SAT-decoding in evolutionary algorithms for discrete constrained optimization problems

— For complex optimization problems, several population-based heuristics like Multi-Objective Evolutionary Algorithms have been developed. These algorithms are aiming to deliver sufficiently good solutions in an acceptable time. However, for discrete problems that are restricted by several constraints it is mostly a hard problem to even find a single feasible solution. In these cases, the optimization heuristics typically perform poorly as they mainly focus on searching feasible solutions rather than optimizing the objectives. In this paper, we propose a novel methodology to obtain feasible solutions from constrained discrete problems in populationbased optimization heuristics. At this juncture, the constraints have to be converted into the Propositional Satisfiability Problem (SAT). Obtaining a feasible solution is done by the DPLL algorithm which is the core of most modern SAT solvers. It is shown in detail how this methodology is implemented in Multiobjective Evolutionary Algor...

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Martin Lukasiewycz, Michael Glaß, Christian Haubelt, Jürgen Teich
• Keywords: Artificial Intelligence, CEC 2007, Feasible Solutions, Optimization Heuristics, Sat Solver