Artificial bee colony training of neural networks

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Abstract

The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization that has previously been applied successfully to the training of neural networks. This paper explores more carefully the performance of the ABC algorithm for optimizing the connection weights of feed-forward neural networks for classification tasks, and presents a more rigorous comparison with the traditional Back-Propagation (BP) training algorithm. The empirical results show that using the standard \stopping early" approach with optimized learning parameters leads to improved BP performance over the previous comparative study, and that a simple variation of the ABC approach provides improved ABC performance too. With both improvements applied, we conclude that the ABC approach does perform very well on small problems, but the generalization performances achieved are only significantly better than standard BP on one out of six datasets, and the training times increase rapidly as the size of the problem grows.

Details

Original languageEnglish
Title of host publicationNature Inspired Cooperative Strategies for Optimization (NICSO 2013)
Subtitle of host publicationlearning, optimization and interdisciplinary applications
EditorsGerman Terrazas, Fernando E. B. Otero, Antonio D. Masegosa
Publication statusPublished - 2014
EventNature Inspired Cooperative Strategies for Optimization (NICSO 2013) - , United Kingdom
Duration: 2 Sep 2013 → …

Conference

ConferenceNature Inspired Cooperative Strategies for Optimization (NICSO 2013)
Country/TerritoryUnited Kingdom
Period2/09/13 → …