Constraint solving and optimization using evolutionary techniques

Abstract

Constraint Solving and Optimization is very relevant in many real world applications including scheduling, planning, configuration, resource allocation and timetabling. Solving a constraint optimization problem consists of finding an assignment of values to variables that optimizes some defined objective functions, subject to a set of constraints imposed on the problem variables. Due to their high dimensional and exponential search spaces, classical methods are unpractical to tackle these problems. An appropriate alternative is to rely on metaheuristics. My thesis is concerned with investigating the applicability of the evolutionary algorithms when dealing with constraint optimization problems. In this regard, we propose two new optimization algorithms namely Mushroom Reproduction Optimization algorithm (MRO) and Focus Group Optimization algorithm (FGO) for solving such problems.