Binary-coded Genetic Algorithm Research Articles

Comparison of Optimization Methods for Ground-Water Bioremediation

This paper compares computational performance of eight optimization algorithms used to identify the most cost-effective policy for \Iin situ\N bioremediation of contaminated ground water. Six of these methods have not previously been applied to optimization of groundwater remediation. Numerical results were obtained for bioremediation of three problems based on two aquifers with time-invariant or time-varying pumping rates. Three major classes of algorithms are considered in the comparison: \Ievolutionary algorithms\N [binary-coded genetic algorithm (BIGA), real-coded genetic algorithm, and derandomized evolution strategy (DES)], \Idirect search methods\N (Nelder-Mead simplex, modified simplex, and parallel directive search), and \Iderivative-based optimization methods\N (implicit filtering for constrained optimization and successive approximation linear quadratic regulator). Based on the three problems considered, the successive approximation linear quadratic regulator is the fastest algorithm. No one algorithm was consistently the most accurate on all three problems. The DES displayed an impressive combination of speed and accuracy. The DES has the advantage that it does not require derivative information. The BIGA was much shower and less accurate than all the other algorithms on the two problems BIGA solved.

Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management

Genetic algorithms (GAs) have been fairly successful in a diverse range of optimization problems, providing an efficient and robust way for guiding a search even in a complex system and in the absence of domain knowledge. In this paper, two types of genetic algorithms, real-coded and binary-coded, are examined for function optimization and applied to the optimization of a flood control reservoir model. The results show that both genetic algorithms are more efficient and robust than the random search method, with the real-coded GA performing better in terms of efficiency and precision than the binary-coded GA.

Automatic design of frequency sampling filters by hybrid genetic algorithm techniques

A new method of designing recursive and nonrecursive frequency sampling filters is presented. We investigate the use of a hybrid real-coded genetic algorithm (GA) for optimising transition sample values to give the maximum stopband attenuation. A modification allows the coefficient wordlength to be optimized concurrently, thereby reducing the overall number of design steps and simplifying the design process. The technique is able to consistently optimize filters with up to six transition samples. Designing digital filters is a complex process involving optimization at several discrete design steps. The techniques presented could form the basis for integrating several of the optimizations. Investigations into increasing this integration by using a binary-coded GA to optimize nonlinear phase, quantized coefficient FIR filters are introduced, with an analysis of the difficulty of the problem from a GA perspective.

Genetic algorithms for the optimization of piecewise linear discriminants

The application of genetic algorithms (GAs) to the optimization of piecewise linear discriminants is described. Piecewise linear discriminant analysis (PLDA) is a supervised pattern recognition technique employed in this work for the automated classification of Fourier transform infrared (FTIR) remote sensing data. PLDA employs multiple linear discriminants to approximate a nonlinear separating surface between data categories defined in a vector space. The key to the successful implementation of PLDA is the positioning of the individual discriminants that comprise the piecewise linear discriminant. For the remote sensing application, the discriminant optimization is challenging due to the large number of input variables required and the corresponding tendency for local optima to occur on the response surface of the optimization. In this work, three implementations of GAs are configured and evaluated: a binary-coded GA (GAB), a real-coded GA (GAR), and a Simplex-GA hybrid (SGA). GA configurations are developed by use of experimental design studies, and piecewise linear discriminants for acetone, methanol, and sulfur hexafluoride are optimized (trained). The training and prediction classification results indicate that GAs area viable approach for discriminant optimization. On average, the best piecewise linear discriminant optimized by a GA is observed to classify 11% more analyte-active patterns correctly in prediction than an unoptimized piecewise linear discriminant. Discriminant optimization problems not used in the experimental design study are employed to test the stability of the GA configurations. For these cases, the best piecewise linear discriminant optimized by SGA is shown to classify 19% more analyte-active patterns correctly in prediction than an unoptimized discriminant. These results also demonstrate that the two real number coded GAs (GAR and SGA) perform better than the GAB. Real number coded GAs are also observed to execute faster and are simpler to implement.