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Ant Colony Programming: Application of Ant Colony System to Function Approximation

Ant Colony Programming: Application of Ant Colony System to Function Approximation

Mariusz Boryczka
Copyright: © 2010 |Pages: 25
ISBN13: 9781605667980|ISBN10: 1605667986|ISBN13 Softcover: 9781616924164|EISBN13: 9781605667997
DOI: 10.4018/978-1-60566-798-0.ch011
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MLA

Boryczka, Mariusz. "Ant Colony Programming: Application of Ant Colony System to Function Approximation." Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, edited by Raymond Chiong, IGI Global, 2010, pp. 248-272. https://doi.org/10.4018/978-1-60566-798-0.ch011

APA

Boryczka, M. (2010). Ant Colony Programming: Application of Ant Colony System to Function Approximation. In R. Chiong (Ed.), Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications (pp. 248-272). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-60566-798-0.ch011

Chicago

Boryczka, Mariusz. "Ant Colony Programming: Application of Ant Colony System to Function Approximation." In Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, edited by Raymond Chiong, 248-272. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-798-0.ch011

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Abstract

Automatic programming is the method in which a computer program is constructed automatically based on the specification of goals which are to be realized. This chapter describes one of the methods for automatic function approximation (as a form of automatic programming) – ant colony programming (ACP). It is based on ant colony system (ACS) as a new method for solving approximation problems. While solving these problems by ACP two approaches are used: the expression approach and the program approach. Several improvements of this method are presented, including the elimination of introns, the use of a structure similar to the candidate list introduced in ACS, and parameter-tuning. The chapter first describes ACS and introduces the problem of symbolic regression. Then, ACP is defined. After that, improvements of ACP are presented. The main objective of the chapter is to give an overview of the published results of studies carried out on ACP, while at the same time present a new idea in the process of parameter-tuning.

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