Abstract
In this paper, we present a Frequent Schemas Analysis (FSA) approach as an instance of Optinformatics for extracting knowledge on the search dynamics of Binary GA using the optimization data generated during the search. The proposed frequent pattern mining algorithm labeled here as LoFIA in FSA effectively mines for interesting implicit frequent schemas. Subsequently these schemas may be visualized to provide new insights into the workings of the search algorithm. A case study using the Royal Road problem is used to explain the search performance of Genetic Algorithm (GA) based on FSA in action.
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
De Jong, K.: Evolutionary Computation: A Unified Approach. MIT Press, Cambridge (2006)
Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
Le, M.N., Ong, Y.S., Nguyen, Q.H.: Optinformatics for Schema Analysis of Binary Genetic Algorithms. In: Genetic and Evolutionary Computation Conference (GECCO) (2008)
Tan, P.-N., Michael Steinbach, V.K.: Introduction to data mining. Pearson Addison Wesley, London (2006)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Westey, Reading (1989)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns Without Candidate Generation: A Frequent-pattern Tree Approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Mitchell, M., Forrest, S., Holland, J.: The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance. In: Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, vol. 1001, p. 48109 (1992)
Forrest, S., Mitchell, M.: Relative Building-block Fitness and the Building-block Hypothesis. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, pp. 109–126. Morgan Kaufmann, San Francisco (1993)
Mitchell, M., Holland, J., Forrest, S.: When Will a Genetic Algorithm Outperform Hill Climbing. Advances in Neural Information Processing Systems 6, 51–58 (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Le, M.N., Ong, Y.S. (2008). A Frequent Pattern Mining Algorithm for Understanding Genetic Algorithms. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-85984-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
eBook Packages: Computer ScienceComputer Science (R0)