Constraint Optimization for Timetabling Problems using a Contraint Driven Solution Model

Many science and engineering applications require finding solutions to planning and optimization problems by satisfying a set of constraints. These constraint problems (CPs) are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs) or constraint optimization problems (COPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. A variation of EA - Intelligent constraint handling evolutionary algorithm (ICHEA) has been demonstrated to be a versatile constraints-guided EA for all forms of continuous constrained problems in our earlier works. In this paper we investigate an incremental approach through ICHEA in solving benchmark exam timetabling problems which is a classic discrete COP and compare its performance with other well-known EAs. Incremental and exploratory search in constraint solving has shown improvement in the quality of solutions.