Abstract
This work describes an approach for the computation of function features out of optimization functions to train a decision tree. This decision tree is used to identify adequate parameter settings for Particle Swarm Optimization (PSO). The function features describe different characteristics of the fitness landscape of the underlying function. We distinguish between three types of features: The first type provides a short overview of the whole search space, the second describes a more detailed view on a specific range of the search space and the remaining features test an artificial PSO behavior on the function. With these features it is possible to classify fitness functions and to identify a parameter set which leads to an equal or better optimization process compared to the standard parameter set for Particle Swarm Optimization.
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Bogon, T., Poursanidis, G., Lattner, A.D., Timm, I.J. (2013). Setting Up Particle Swarm Optimization by Decision Tree Learning Out of Function Features. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2011. Communications in Computer and Information Science, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29966-7_5
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DOI: https://doi.org/10.1007/978-3-642-29966-7_5
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