A Constraint Driven Solution Model for Discrete Domains with a Case Study of Exam Timetabling Problems

• URL: http://arxiv.org/abs/2002.03102v1
• Date: Sat, 8 Feb 2020 06:53:38 GMT
• Title: A Constraint Driven Solution Model for Discrete Domains with a Case Study of Exam Timetabling Problems
• Authors: Anuraganand Sharma
• Abstract summary: A variation of Intelligent constraint handling evolutionary algorithm (ICHEA) has been demonstrated to solve benchmark exam timetabling problems. 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.
• Score: 6.788217433800101
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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