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Journal ArticleDOI

Artificial bee colony programming for symbolic regression

TLDR
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.
About
This article is published in Information Sciences.The article was published on 2012-11-01. It has received 187 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Symbolic regression.

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Citations
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Journal ArticleDOI

Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks

TL;DR: This paper uses artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling that was able to achieve the theoretical upper bound in all the experimented cases.
Journal ArticleDOI

Animal migration optimization: an optimization algorithm inspired by animal migration behavior

TL;DR: Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.
Journal ArticleDOI

Multi-strategy ensemble artificial bee colony algorithm

TL;DR: A novel multi-strategy ensemble ABC (MEABC) algorithm, where a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring.
Journal ArticleDOI

Data feature selection based on Artificial Bee Colony algorithm

TL;DR: This paper aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony approach to classification of different data sets using various UCI data sets to demonstrate the effectiveness of the proposed method against other relevant approaches available in the literature.
Journal ArticleDOI

Artificial bee colony algorithm: a survey

TL;DR: A review on Artificial bee colony ABC developments, applications, comparative performance and future research perspectives is presented.
References
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Proceedings ArticleDOI

Particle swarm optimization

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book

Adaptation in natural and artificial systems

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Book

Genetic Algorithms

Book

Genetic Programming: On the Programming of Computers by Means of Natural Selection

TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.