Abstract
Ant Colony Optimization (ACO) differs substantially from other meta-heuristics such as Evolutionary Algorithms (EA). Two of its distinctive features are: (i) it is constructive rather than based on iterative improvements, and (ii) it employs problem knowledge in the construction process via the heuristic function, which is essential for its success. In this paper, we introduce the ACO encoding, which is a self-contained algorithmic component that can be readily used to make available these two particular features of ACO to any search algorithm for continuous spaces based on iterative improvements to solve combinatorial optimization problems. © 2010 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Moraglio, A., Otero, F. E. B., & Johnson, C. G. (2010). The ACO encoding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6234 LNCS, pp. 528–535). https://doi.org/10.1007/978-3-642-15461-4_53
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