Abstract
The ACO\(\mathbb {_R}\) algorithm is based on the Ant Colony Optimization (ACO) metaphor, and a crossover operator does not naturally within this metaphor. In spite of this, we investigate in this paper whether the performance of ACO\(\mathbb {_R}\) would benefit from the deployment, with a fixed probability, of a crossover operator. Our extensive experimental evaluation uses two applications: (1) training feedforward neural networks for classification using 65 benchmark datasets from the UCI repository; and (2) optimizing several popular synthetic benchmark continuous-domain functions with the number of dimensions varying from 10 up to 10,000. Our experimental results confirm that the use of crossover does improve performance on both applications to a statistically significant extent.
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Partial support of a grant from the Brandon University Research Council (BURC) is gratefully acknowledged.
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Abdelbar, A.M., Salama, K.M. (2018). Does the ACO\(\mathbb {_R}\) Algorithm Benefit from the Use of Crossover?. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_28
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