Abstract
GEP is a biologically motivated machine learning technique used to solve complex multitude problems. Similar to other evolution algorithms, GEP is slow when dealing with a large number of population. Considering that the parallel GEP has great efficiency and the niching method can keep diversity in the process of exploring evolution, a niching GEP algorithm based on parallel model is presented and discussed in this paper. In this algorithm, dividing the population to the niche nodes in sub-populations can solves the same problem in less computation time than it would take on a single process. Experimental results on sequence induction, function finding and sunspot prediction demonstrate its advantages and show that the proposed method takes less computation time but with higher accuracy.
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
Ferreira, C.: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems 13, 87–129 (2001)
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Angra do Heroismo Portugal (2002)
Ferreira, C.: Automatically Defined Functions in Gene Expression Programming. Studies in Computational Intelligence 13, 21–56 (2006)
Jie, Z., Changjie, T., Chuan, L.: Time Series Prediction Based on Gene Expression Programming. In: Proceedings of the Fifth International Conference on Web-Age Information Management, Dalian, China (2004)
Gang, P., Iimura, I., Nakatsuru, T.: Efficiency of Local Genetic Algorithm in Parallel Processing. In: PDCAT 2005. Parallel and Distributed Computing, Applications and Technologies, pp. 620–623 (2005)
Goldberg, D.: Sizing population for serial and parallel genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, California, pp. 70-79 (1989)
Andre, D., Koza, J.R.: Parallel genetic programming: A scalable implementation using the transporter network architecture. In: Angeline, P., Kinnear, K. (eds.) Advances in Genetic Programming 2, Cambridge, MA, pp. 317–337 (1993)
Oussaidkne, M., Chopard, B., Pictet, O.: Parallel genetic programming and its application to trading model induction. Parallel Computing 23, 1183–1198 (1997)
Siwei, J., Zhihua, C., Dang, Z.: Parallel Gene Expression Programming Algorithm Based on Simulated Annealing Method. ACTA Electronic Sinica 33, 2017–2021 (2005)
Goldberg, D., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the 2nd International Conference on Genetic Algorithms, pp. 41–49 (1987)
De Jong, K.: An analysis of the behavior of a class of genetic algorithms. Dissertation Abstracts International 36(10), 5140B (1975)
Mahfoud, S.W.: Crowding and preselection revisited. Parallel Problem Solving from Nature II, 27–36 (1992)
Ferreira, C.: Gene Expression Programming and the Evolution of Computer Programs. In: Recent Developments in Biologically Inspired Computing, pp. 82–103. Idea Group Publishing (2004)
Yang, H., Ch, F., Li, C., Wang, M.: A density clustering based niching genetic algorithm for multimodal optimization. In: Machine Learning and Cybernetics. Proceedings of 2005 International Conference, vol. 3, pp. 1599–1604 (2005)
Ferreira, C.: Function Finding and the Creation of Numerical Constants in Gene Expression Programming. In: Proceedings of the 7th Online World Conference on Soft Computing in Industrial Applications (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, Y., Peng, H., Wei, J. (2007). A Niching Gene Expression Programming Algorithm Based on Parallel Model. In: Xu, M., Zhan, Y., Cao, J., Liu, Y. (eds) Advanced Parallel Processing Technologies. APPT 2007. Lecture Notes in Computer Science, vol 4847. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76837-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-76837-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76836-4
Online ISBN: 978-3-540-76837-1
eBook Packages: Computer ScienceComputer Science (R0)