Method of binary analytic programming to look for optimal mathematical

被引：11
作者：
Diveev, A. I. [1 ,2 ]
Konyrbaev, N. B. [2 ]
Sofronova, E. A. [2 ]
机构：
[1] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, 44 Vavilova Str, Moscow 119333, Russia
[2] RUDN Univ, 6 Miklukho Maklaya Str, Moscow 117198, Russia
来源：
XII INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2016, (INTELS 2016) | 2017年 / 103卷
关键词：
symbolic regression; genetic programming; analytic programming; genetic algorithm; EVOLUTION;
D O I：
10.1016/j.procs.2017.01.073
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the known methods of symbolical regression by search of the solution with the help of a genetic algorithm, there is a problem of crossover. Genetic programming performs a crossover only in certain points. Grammatical evolution often corrects a code after a crossover. Other methods of symbolical regression use excess elements in a code for elimination of this shortcoming. The work presents a new method of symbolic regression on base of binary computing trees. The method has no problems with a crossover. Method use a coding in the form of a set of integer numbers like analytic programming The work describes the new method and some examples of codding for mathematical expressions. (C) 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
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收藏
页码：597 / 604
页数：8
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