A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms Giovanni Iacca 1 , Ferrante Neri 2,3 , Fabio Caraffini 2,3 , and Ponnuthurai Nagaratnam Suganthan 4 1 INCAS 3 Dr. Nassaulaan 9, 9401 HJ, Assen, The Netherlands giovanniiacca@incas3.eu 2 Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, England, UK {fneri,fcaraffini}@dmu.ac.uk 3 Department of Mathematical Information Technology, P.O. Box 35 (Agora), 40014 University of Jyv¨ askyl¨ a, Finland {ferrante.neri,fabio.caraffini}@jyu.fi 4 School of Electrical & Electronic Engineering, College of Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 epnsugan@ntu.edu.sg Abstract. The ensemble structure is a computational intelligence su- pervised strategy consisting of a pool of multiple operators that com- pete among each other for being selected, and an adaptation mecha- nism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework co- operatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The re- sulting algorithm, namely Ensemble of Parameters and Strategies Differ- ential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms. Keywords: Differential Evolution, Global Optimization, Ensemble, Pa- rameter Adaptation, Mutation Strategy Adaptation. 1 Introduction Differential Evolution (DE) [24] is a simple, fast and efficient stochastic algorithm with few parameters to tune [4, 21]. After the early DE implementations, impor- tant efforts have been made to improve the performance by introducing different