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