Conference Paper
A Scalability Test for Accelerated DE Using Generalized OppositionBased Learning.
State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China
DOI: 10.1109/ISDA.2009.216 Conference: Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, Pisa, Italy , November 30December 2, 2009 Source: IEEE Xplore

Conference Paper: Adaptive Differential Evolution with variable population size for solving highdimensional problems.
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ABSTRACT: In this paper, we present a novel Differential Evo lution (DE) algorithm to solve highdimensional global opti mization problems effectively. The proposed approach, called DEVP, employs a variable population size mechanism, which adjusts population size adaptively. Experiments are conducted to verify the performance of DEVP on 19 highdimensional global optimization problems with dimensions 50, 100, 200, 500 and 1000. The simulation results show that DEVP out performs classical DE, CHC (Crossgenerational elitist selection, Heterogeneous recombination, and Cataclysmic mutation), G CMAES (Restart Covariant Matrix Evolutionary Strategy) and GODE (Generalized OppositionBased DE) on the majority of test problems. Index Terms—Differential Evolution (DE), variable population size, global optimization, largescale, highdimensional.Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 58 June, 2011; 01/2011 
Conference Paper: A direct optimization approach to the P300 speller.
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ABSTRACT: The P300 component of the brain eventrelatedpotential is one of the most used signals in brain computer interfaces (BCIs). One of the required steps for the application of the P300 paradigm is the identification of this component in the presence of stimuli. In this paper we propose a direct optimization approach to the P300 classification problem. A general formulation of the problem is introduced. Different classes of optimization algorithms are applied to solve the problem and the concepts of kbest and kworst ensembles of solutions are introduced as a way to improve the accuracy of single solutions. The introduced approaches are able to achieve a classification rate over 80% on test data.13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 1216, 2011; 01/2011 
Conference Paper: Large Scale Global Optimization: Experimental Results with MOSbased Hybrid Algorithms
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ABSTRACT: Continuous optimization is one of the most active research Iines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2013 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many realworld problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. In this paper we describe the whole process of creating a competitive hybrid algorithm, from the experimental design to the final statistical validation of the resuIts. We prove that a good experimental design is able to find a combination of algorithms that outperforms any of its composing algorithms by automatically selecting the most appropriate heuristic for each function and search phase. We also show that the proposed algorithm obtains statistically better results than the reference algorithm DECCG.2013 IEEE Congress on Evolutionary Computation, CEC 2013; 01/2013
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