  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, however traditional operators
for EAs are 'blind' to constraints or generally use problem dependent objective
functions; as they do not exploit information from the constraints in search
for solutions. A variation of EA, Intelligent constraint handling evolutionary
algorithm (ICHEA), has been demonstrated to be a versatile constraints-guided
EA for continuous constrained problems in our earlier works in (Sharma and
Sharma, 2012) where it extracts information from constraints and exploits it in
the evolutionary search to make the search more efficient. In this paper ICHEA
has been demonstrated to solve benchmark exam timetabling problems, a classic
COP. The presented approach demonstrates competitive results with other
state-of-the-art approaches in EAs in terms of quality of solutions. ICHEA
first uses its inter-marriage crossover operator to satisfy all the given
constraints incrementally and then uses combination of traditional and enhanced
operators to optimize the solution. Generally CPs solved by EAs are problem
dependent penalty based fitness functions. We also proposed a generic
preference based solution model that does not require a problem dependent
fitness function, however currently it only works for mutually exclusive
constraints.
