Abstract
It is known that different classes of permutation problems are more easily solved by selecting a suitable representation. In particular, permutation representations suitable for Estimation of Distribution algorithms (EDAs) are known to present several challenges. Therefore, it is of interest to investigate novel representations and their properties. In this paper, we present a study of the factoradic representation which offers new modelling insights through the use of three algorithmic frameworks, a Genetic Algorithm (GA) and two EDAs. Four classic permutation benchmark problems are used to evaluate the factoradic-based algorithms in comparison with published work with other representations. Our experiments demonstrate that the factoradic representation is a competitive approach to apply to permutation problems. EDAs and more specifically, univariate EDAs show the most robust performance on the benchmarks studied. The factoradic representation also leads to better performance than adaptations of EDAs for continuous spaces, overall similar performance to integer-based EDAs and occasionally matches results of specialised EDAs, justifying further study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ceberio, J., Irurozki, E., Mendiburu, A., Lozano, J.A.: A distance-based ranking model estimation of distribution algorithm for the flowshop scheduling problem. IEEE Transactions on Evolutionary Computation (2013)
Schnier, T., Yao, X.: Using multiple representations in evolutionary algorithms. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 479–486. IEEE (2000)
Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6(2), 154–160 (1994)
Ashlock, D.: Evolutionary computation for modeling and optimization. Springer (2006)
Mehdi, M.: Parallel hybrid optimization methods for permutation based problems. PhD thesis, Université des Sciences et Technologie de Lille (2011)
Samarghandi, H., ElMekkawy, T.Y.: A meta-heuristic approach for solving the no-wait flow-shop problem. International Journal of Production Research 50(24), 7313–7326 (2012)
Regnier-Coudert, O., McCall, J., Ayodele, M.: Geometric-based sampling for permutation optimization. In: Proceeding of the 2013 Annual Conference on Genetic and Evolutionary Computation Conference, pp. 399–406. ACM (2013)
Baluja, S.: Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Technical report, Carnegie Mellon University (1994)
Mühlenbein, H.: The equation for response to selection and its use for prediction. Evolutionary Computation 5(3), 303–346 (1997)
Ruiz, R., Maroto, C.: A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research 165(2), 479–494 (2005)
Lawler, E.L.: The quadratic assignment problem. Management science 9(4), 586–599 (1963)
Ceberio, J., Irurozki, E., Mendiburu, A., Lozano, J.A.: A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems. Progress in Artificial Intelligence 1, 103–117 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Regnier-Coudert, O., McCall, J. (2014). Factoradic Representation for Permutation Optimisation. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-10762-2_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
Online ISBN: 978-3-319-10762-2
eBook Packages: Computer ScienceComputer Science (R0)