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Swarm‐based metaheuristics in automatic programming: a survey

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Juan L. Olmo, José R. Romero, Sebastián Ventura

Published Online: Oct 27 2014

DOI: 10.1002/widm.1138

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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

Swarm‐based automatic programming publications per year.

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Two sample artificial fish individuals with the same structure.

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Artificial bee colony programming (ABCP) sharing mechanism.

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An example of the genotype–phenotype mapping in grammatical swarm (GS) from a linear chromosome. The integer values are used to select production rules of the context‐free grammar (CFG), producing a derivation sequence that can be kept as a derivation tree, which is further decoded to an expression tree.

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Part of the derivation tree explored by ants to generate an expression in generalized ant programming (GAP).

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Tree structure generated from a graph in the ant colony programming (ACP) expression approach.

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Prototype tree with a pheromone table associated with each node (it is shown only for the first two nodes).

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Percentage of swarm‐based automatic programming publications by metaheuristic.

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