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In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.<\/jats:p>","DOI":"10.1142\/s0129065714500087","type":"journal-article","created":{"date-parts":[[2013,11,8]],"date-time":"2013-11-08T11:41:21Z","timestamp":1383910881000},"page":"1450008","source":"Crossref","is-referenced-by-count":65,"title":["MULTI-STRATEGY COEVOLVING AGING PARTICLE OPTIMIZATION"],"prefix":"10.1142","volume":"24","author":[{"given":"GIOVANNI","family":"IACCA","sequence":"first","affiliation":[{"name":"INCAS3 \u2013 Innovation Centre for Advanced Sensors and Sensor Systems, P.O. 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