Volume 4, Issue 6 p. 445-469
Overview

Swarm-based metaheuristics in automatic programming: a survey

Juan L. Olmo

Juan L. Olmo

Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

Search for more papers by this author
José R. Romero

José R. Romero

Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

Search for more papers by this author
Sebastián Ventura

Corresponding Author

Sebastián Ventura

Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia

Correspondence to: [email protected]Search for more papers by this author
First published: 27 October 2014
Citations: 10

Conflict of interest: The authors have declared no conflicts of interest for this article.

Abstract

On the one hand, swarm intelligence (SI) is an emerging field of artificial intelligence that takes inspiration in the collective and social behavior of different groups of simple agents. On the other hand, the automatic evolution of programs is an active research area that has attracted a lot of interest and has been mostly promoted by the genetic programming paradigm. The main objective is to find computer programs from a high-level problem statement of what needs to be done, without needing to know the structure of the solution beforehand. This paper looks at the intersection between SI and automatic programming, providing a survey on the state-of-the-art of the automatic programming algorithms that use an SI metaheuristic as the search technique. The expression of swarm programming (SP) has been coined to cover swarm-based automatic programming proposals, since they have been published to date in a disorganized manner. Open issues for future research are listed. Although it is a very recent area, we hope that this work will stimulate the interest of the research community in the development of new SP metaheuristics, algorithms, and applications. WIREs Data Mining Knowl Discov 2014, 4:445–469. doi: 10.1002/widm.1138

This article is categorized under:

  • Algorithmic Development > Association Rules
  • Technologies > Classification
  • Technologies > Computational Intelligence