Presentation 1999/5/28
Genetic Programming for Acquisition of Maximum Values of Non-Linear Discrete Function in Two-Dimensional Euclidean Space
Akira Nomura, Takao Terano,
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Abstract(in English) This article describes a method to acquire maximum values of non-linear discrete functions in a two-dimensional Euclidean space using genetic programming. Generally it is difficult to acquire maximum values of discrete function in two-dimensional nonmed space, while those of differentiable function can be easily acquired using the analytical method with derivatives. And also it is difficult to acquire maximum values when there are some obstacles in the search space. We propose an answer to solve the problems mentioned above with a modified technique of genetic programming which was developed in Lisp by John Koza et al.
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Keyword(in English) non-linear discrete function / Euclidean space / maximum value / genetic programming
Paper # AI99-20
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Committee AI
Conference Date 1999/5/28(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Genetic Programming for Acquisition of Maximum Values of Non-Linear Discrete Function in Two-Dimensional Euclidean Space
Sub Title (in English)
Keyword(1) non-linear discrete function
Keyword(2) Euclidean space
Keyword(3) maximum value
Keyword(4) genetic programming
1st Author's Name Akira Nomura
1st Author's Affiliation Asahigin Systems Co.,Ltd.()
2nd Author's Name Takao Terano
2nd Author's Affiliation University of Tsukuba, Tokyo
Date 1999/5/28
Paper # AI99-20
Volume (vol) vol.99
Number (no) 96
Page pp.pp.-
#Pages 8
Date of Issue