Conference Proceeding

A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments.

01/1996; DOI:10.1007/3-540-61723-X_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 22-26, 1996, Proceedings
Source: DBLP

ABSTRACT In this paper we examine a modification to the genetic algorithm - a new adaptive operator was developed for two industrial applications using genetic algorithm based on-line control systems. The aim is to enable the control systems to track optima of a time-varying dynamic system whilst not being detrimental to its ability to provide sound results for the stationary environments. When compared with the hypermutation operator, the new operator matched the level of diversity introduced into the population with the "degree" of the environmental changes better because it increases population diversity only gradually. Although the new technique was developed for the control application domain where real variables are mostly used, a possible generalization of the method is also suggested. It is believed that the technique has the potential to be a further contribution in making genetic algorithm based techniques more readily usable in industrial control applications. The genetic algorithm is a proven search/optimisation technique (1) based on an adaptive mechanism of biological systems. The motivating context of Holland's initial work on genetic algorithms (GAs) was the design and implementation of robust adaptive systems in contrast to mere function optimisers (2). Understanding GAs in this broader adaptive system context is a necessary prerequisite for understanding their potential application to any problem domain and for understanding their relevant strengths and limitations as argued in the previously quoted paper. One important limiting factor for the use of the GA in real time applications common to many real world applications, whose models are not stationary, is the need for the repeated initialization of the GA from a random starting point in the search space to enable tracking optima in such changing/dynamic environments. The use of a repetitive learning cycle has obvious implications in terms of the quality of the solutions available which presents limitations on the use of genetic techniques in dynamic environments such as on-line industrial control. In this paper we present preliminary results of our research into techniques for genetic algorithm based robust systems which will continually evolve an optimal solution in

0 0
 · 
0 Bookmarks
 · 
25 Views
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: Several gradient-based 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 back-propagation and back-propagation 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 Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (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 multi-input–multi-output 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; 74:1696-1709.
  • [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 agent-based evolutionary algorithm in solving constrained optimization problems with equality constraints. The technique is basically used as an agent learning process in the agent-based evolutionary algorithm. The performance of the proposed algorithm is tested on a set of well-known benchmark problems including seven new problems. The experimental results confirm the improved performance of the proposed technique.
    Memetic Computing. 01/2011; 3:51-72.
  • [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 agent-based 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 well-known benchmark problems including two new problems. The experimental results confirm the improved performance of the proposed algorithm.
    07/2010: pages 49-76;

Full-text (2 Sources)

View
0 Downloads
Available from

Frank Vavak