Abstract
Intensification and diversification are two driving forces in genetic algorithms and are frequently the subject of research. While it seemed for decades that a genetic operator can be classified as either the one or the other, it has been shown in the last few years that this assumption is an oversimplified view and most operators exhibit both, diversification and intensification, to some degree. Most papers in this field focus on a certain operator or algorithm configuration as theoretical and generalizable foundations are hard to obtain. In this paper we therefore use a wide range of different configurations and behavior measurements to study the intensification and diversification behavior of genetic algorithms and their operators.
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References
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Numerical Insights. CRC Press (2009)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)
Crepinsek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: A survey. To be pubished in ACM Computing Surveys 45(3) (2013)
Eiben, A.E., Schippers, C.A.: On Evolutionary Exploration and Exploitation. Fundamenta Informaticae 35(1-4), 35–50 (1998)
Hansheng, L., Lishan, K.: Balance between exploration and exploitation in genetic search. Wuhan University Journal of Natural Sciences 4(1), 28–32 (1999)
Kötzing, T., Sudholt, D., Theile, M.: How crossover helps in pseudo-boolean optimization. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 989–996. ACM (2011)
Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I., Dizdarevic, D.: Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review 13, 129–170 (1999)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1998)
Reeves, C.R., Rowe, J.E.: Genetic algorithms: principles and perspectives; a guide to GA theory. Kluwer Academic Publishers (2004)
Scheibenpflug, A., Wagner, S., Pitzer, E., Burlacu, B., Affenzeller, M.: On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems. In: Proceedings of the 24th European Modeling and Simulation Symposium, EMSS 2012 (2012)
Stützle, T.: Local search algorithms for combinatorial problems: analysis, algorithms, and new applications. Ph.D. thesis, TU Darmstadt (1999)
Wagner, S.: Heuristic Optimization Software Systems: Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. Ph.D. thesis, Johannes Kepler Universität Linz (2009)
Whitley, L.D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesmen: The genetic edge recombination operator. In: Proceedings of the 3rd International Conference on Genetic Algorithms. pp. 133–140. Morgan Kaufmann Publishers Inc. (1989)
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Scheibenpflug, A., Wagner, S. (2013). An Analysis of the Intensification and Diversification Behavior of Different Operators for Genetic Algorithms. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53856-8_46
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DOI: https://doi.org/10.1007/978-3-642-53856-8_46
Publisher Name: Springer, Berlin, Heidelberg
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