برنامه نویسی کلونی زنبور عسل مصنوعی برای رگرسیون نمادین
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7520||2012||16 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Sciences, Volume 209, 20 November 2012, Pages 1–15
Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several fields to solve different problems up to date. In this paper, an artificial bee colony algorithm, called as Artificial Bee Colony Programming (ABCP), is described for the first time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given finite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artificial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression.
نتیجه گیری انگلیسی
In this paper, a new approach to symbolic regression is proposed which is based on the artificial bee colony algorithm. The new approach, named as artificial bee colony programming, allows to evolve expressions and constants in the same representation and form the mathematical functions automatically. The proposed approach is tested on a large set of symbolic regression benchmark problems and remarkable performance is concluded after comparing its performance with a well-known symbolic regression approach, GP. Moreover, the performance of ABCP is also compared with the performance of a recently proposed dynamic ant programming and evaluated on more difficult problems. By observing the good performance of the new paradigm, it is expected that ABCP will be used to solve problems in many areas such as machine learning, data mining and optimization. As a future work, it is planned to study on applying ABCP to solve real-world problems and also on improving the performance of ABCP by introducing new modifications.