Abstract
Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CSPs, however traditional EAs are ‘blind’ to constraints as they do not exploit information from the constraints in search for solutions. In this paper, a variation of EA is proposed where information is extracted from the constraints and exploited in search. The proposed model (ICHEA for Intelligent Constraint Handling Evolutionary Algorithm) improves on efficiency and is independent of problem characteristics. This paper presents ICHEA and its results from solving continuous CSPs. The results are significantly better than results from other existing approaches and the model shows strong potential. The scope is to finding at least one solution that satisfies all the constraints rather than optimizing the solutions.
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
Brailsford, S.: Constraint satisfaction problems: Algorithms and applications. European Journal of Operational Research 119(3), 557–581 (1999)
Coello Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002)
Craenen, B.G.W., et al.: Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 7(5), 424–444 (2003)
Craenen, B.G.W.: Solving constraint satisfaction problems with evolutionary algorithms. Phd Dissertation, Vrije Universiteit (2005)
Craenen, B.G.W., et al.: Solving constraint satisfaction problems with heuristic-based evolutionary algorithms. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1571–1577. IEEE (2000)
Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K.: Kanpur Genetic Algorithms Laboratory, http://www.iitk.ac.in/kangal/codes.shtml
Eiben, A.E.: Evolutionary algorithms and constraint satisfaction: definitions, survey, methodology, and research directions. Presented at the (2001)
Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formu-lation, Discussion and Generalization. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Glover, F., Kochenberger, G.A.: Critical event tabu search for multidimensional knapsack problems. In: Osman, I., Kelly, J. (eds.) Meta Heuristics: Theory and Applications (1996)
Kramer, O.: A Review of Constraint-Handling Techniques for Evolution Strategies. Applied Computational Intelligence and Soft Computing, 1–11 (2010)
Lehman, J., Stanley, K.: Exploiting Open-Endedness to Solve Problems Through the Search for Novelty. In: Proceedings of the Eleventh International Conference on Artificial Life (ALIFE XI). MIT Press (2008)
Liu, H., et al.: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl. Soft Comput., 629–640 (2010)
Mezura-Montes, E., Coello, C.A.C.: A Survey of Constraint-Handling Techniques Based on Evolutionary Multiobjective Optimization, Departamento de Computación, Evolutionary Computation Group at CINVESTAV (2006)
Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4(1), 1–32 (1996)
Müller, T.: Constraint-based Timetabling. PhD Dissertation, Charles University (2005)
Paredis, J.: Co-evolutionary Constraint Satisfaction. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 46–55. Springer, Heidelberg (1994)
Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erl-baum Associates Inc. (1985)
Shang, Y., Fromherz, M.P.J.: Experimental complexity analysis of continuous constraint satisfaction problems. Information Sciences 153, 1–36 (2003)
Sharma, A.: A new optimizing algorithm using reincarnation concept. In: 2010 11th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), pp. 281–288. Springer, Heidelberg (2010)
Shcherbina, O.: The COCONUT Benchmark, http://www.mat.univie.ac.at/~neum/glopt/coconut/Benchmark/Benchmark.html
Tessema, B., Yen, G.G.: A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 246–253. IEEE (2006)
Wang, Y.: Yong Wang CV, http://deptauto.csu.edu.cn/staffmember/YongWang.html
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
Sharma, A., Sharma, D. (2012). ICHEA – A Constraint Guided Search for Improving Evolutionary Algorithms. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-34475-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34474-9
Online ISBN: 978-3-642-34475-6
eBook Packages: Computer ScienceComputer Science (R0)