A New GA-Based Method for Temporal Constraint Problems

Abstract

Managing numeric and symbolic temporal information is very relevant for a wide variety of applications including scheduling, planning, temporal databases, manufacturing and natural language processing. Often these applications are represented and managed with the well known constraint-based formalism called the Constraint Satisfaction Problem (CSP). We then talk about temporal CSPs where constraints represent qualitative or quantitative temporal information. Like CSPs, temporal CSPs are NP-hard problems and are traditionally solved with a backtrack search algorithm together with constraint propagation techniques. This method has however some limitations especially for large size problems. In order to overcome this difficulty in practice, we investigate the possibility of solving these problems using Genetic Algorithms (GAs). We propose a novel crossover specifically designed for solving TCSPs using GAs. In order to assess the performance of our proposed crossover over the well known heuristic based GAs, we conducted several experiments on randomly generated temporal CSP instances. In addition, we evaluated the performance of an integration of our crossover within a Parallel GA (PGA) approach. The test results clearly show that the proposed crossover outperforms the known GA methods for all the tests in terms of success rate and time needed to reach the solution. Moreover, when integrated within the PGA, our crossover is very efficient for solving very large size hard temporal CSPs.