Conference Paper
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments.
DOI: 10.1007/354061723X_1002 In proceeding of: Parallel Problem Solving from Nature  PPSN IV, International Conference on Evolutionary Computation. The 4th International Conference on Parallel Problem Solving from Nature, Berlin, Germany, September 2226, 1996, Proceedings
Source: DBLP

Conference Paper: Genetic algorithms with adaptive immigrants for dynamic environments
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ABSTRACT: One approach integrated with genetic algorithms (GAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. Many immigrants schemes have been proposed that differ on the way new individuals are generated, e.g., mutating the best individual of the previous environment to generate elitismbased immigrants. This paper examines the performance of elitismbased immigrants GA (EIGA) with different immigrant mutation probabilities and proposes an adaptive mechanism that tends to improve the performance in DOPs. Our experimental study shows that the proposed adaptive immigrants GA outperforms EIGA in almost all dynamic test cases and avoids the tedious work of finetuning the immigrant mutation probability parameter.Evolutionary Computation (CEC), 2013 IEEE Congress on; 01/2013 
Article: Nonlinear system identification using memetic differential evolution trained neural networks.
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ABSTRACT: Several gradientbased approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as backpropagation and backpropagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Backpropagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Backpropagation (GABP), Particle Swarm Optimization combined with Backpropagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution backpropagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multiinput–multioutput system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis.Neurocomputing. 01/2011; 74:16961709.  05/2013: pages 3964; , ISBN: 9783642384158
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