Abstract
In this article we analyze the behavior and scalability of the CHC algorithm over a benchmark of instances of the software project scheduling problem. Our goal is to analyze the performance of the CHC algorithm when solving realistic NP-hard combinatorial problems and test whether its previously reported high performance on similar problems also holds on this one. We perform a preliminary study to obtain a suitable configuration of the parameters in the algorithm. After choosing the configuration, we show the results for the problem instances in the benchmark. To give a reference on how CHC performs and scales, its results are compared against those of a GA. We conclude that CHC outperforms GA in large problem instances. Moreover, CHC produces promising results for the software project scheduling problem domain, and could be used by practitioners.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alba, E., Chicano, F.: Software project management with GAs. Information Sciences 177(11), 2380–2401 (2007) (in press)
Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Computation, 1st edn. IOP Publishing Ltd., Bristol (1997)
Bilbao, M., Alba, E.: CHC and SA applied to wind energy optimization using real data. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Chicano, F., Luna, F., Nebro, A.J., Alba, E.: Using multi-objective metaheuristics to solve the software project scheduling problem. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1915–1922. ACM, New York (2011)
Eshelman, L.: The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins, G. (ed.) Foudations of Genetic Algorithms, pp. 265–283. Morgan Kaufmann (1990)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc. (1989)
Harman, M., Jones, B.F.: Search-based software engineering. Information & Software Technology 43(14), 833–839 (2001)
Holland, J.H.: Adaptation in natural and artificial systems (1975)
Nebro, A.J., Alba, E., Molina, G., Chicano, F., Luna, F., Durillo, J.J.: Optimal antenna placement using a new multi-objective CHC algorithm. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, New York, NY, USA, pp. 876–883 (2007)
Nesmachnow, S., Cancela, H., Alba, E.: Heterogeneous computing scheduling with evolutionary algorithms. Soft Computing, 685–701 (2010)
Nesmachnow, S., Cancela, H., Alba, E.: Evolutionary algorithms applied to reliable communication network design. Engineering Optimization 39(7), 831–855 (2007)
Nesmachnow, S., Cancela, H., Alba, E.: A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling. Appl. Soft Comput. 12(2), 626–639 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matos, J., Alba, E. (2012). Benchmarking CHC on a New Application: The Software Project Scheduling Problem. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-32964-7_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32963-0
Online ISBN: 978-3-642-32964-7
eBook Packages: Computer ScienceComputer Science (R0)