An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-03-02

AUTHORS

Krzysztof Socha, Christian Blum

ABSTRACT

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm. More... »

PAGES

235-247

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-007-0084-z

DOI

http://dx.doi.org/10.1007/s00521-007-0084-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046650841


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