Federico Cabassi
ABSTRACT
In this paper we make a detailed computational comparison between different variants of memetic DE approaches, including the two variants Greedy MDE (G-MDE) and Distance MDE (D-MDE), recently introduced in [Cabassi & Locatelli, 2015]. The computational comparison reveals that G-MDE is quite effective over single funnel functions, while D-MDE usually outperforms the other approaches over multifunnel landscapes.
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Digital Library
- Federico Cabassi and Marco Locatelli. Computational investigation of simple memetic approaches for continuous global optimization. submitted, 2015. available at http://www.optimization-online.org/DB_HTML/2015/04/4882.html.Google Scholar
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- Andrea Grosso, Marco Locatelli, and Fabio Schoen. A population based approach for hard global optimization problems based on dissimilarity measures. Mathematical Programming, 110(2):373--404, 2007. Google ScholarDigital Library
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Index Terms
- A Computational Comparison of Memetic Differential Evolution Approaches
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