Conference Proceeding
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments.
01/1996; 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

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.  [show abstract] [hide abstract]
ABSTRACT: In addition to inequality constraints, many mathematical models require equality constraints to represent the practical problems appropriately. The existence of equality constraints reduces the size of the feasible space significantly, which makes it difficult to locate feasible and optimal solutions. This paper presents a new equality constraint handling technique which enhances the performance of an agentbased evolutionary algorithm in solving constrained optimization problems with equality constraints. The technique is basically used as an agent learning process in the agentbased evolutionary algorithm. The performance of the proposed algorithm is tested on a set of wellknown benchmark problems including seven new problems. The experimental results confirm the improved performance of the proposed technique.Memetic Computing. 01/2011; 3:5172.  [show abstract] [hide abstract]
ABSTRACT: To represent practical problems appropriately, many mathematical optimization models require equality constraints in addition to inequality constraints. The existence of equality constraints reduces the size of the feasible space, which makes it difficult to locate feasible and optimal solutions. This paper shows the enhanced performance of an agentbased evolutionary algorithm in solving Constrained Optimization Problems (COPs) with equality constraints. In the early generations of the evolutionary process, the agents use a new learning process that is specifically designed for handling equality constraints. In the later generations, the agents improve their performance through other learning processes by exploiting their own potential. The performance of the proposed algorithm is tested on a set of wellknown benchmark problems including two new problems. The experimental results confirm the improved performance of the proposed algorithm.07/2010: pages 4976;
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