Genetic Algorithm Sequential Monte Carlo Methods For Stochastic Volatility And Parameter Estimation

In this thesis, an approach that combines the Box-Jenkins methodology for ARIMA model and Genetic Algorithm (GA) had been introduced as a new approach in estimating the parameter

The performance of genetic algorithm (GA) in nonlinear kinetic parameter estimation of tapioca starch hydrolysis was studied and compared with the Gauss-Newton method.. Both

A comparison of four types of genetic algorithms, namely simple, modified, multi- population and modified multi-population is presented for parameter identification of a

Self Organizing Maps (SOM) and Genetic Algorithms (GA) are used here to first cluster the network, selection of suitable Cluster Head and then to optimize the results

Genetic algorithm (GA) is among the most popular and most widely used metaheuristic algorithms used for opti- mization. This algorithm has shown to be particularly

Evolutionary methods used for optimization of the fuzzy c-means algorithm are genetic algorithms (GA) [7][8] and particle swarm optimization (PSO) [9][10],

When Genetic Algorithms (GA) are used to solve layout problems, the solution quality may be influenced by the population size, number of generations, the rate of crossover, the rate

Genetic Algorithms [7] belong to the class of evolutionary algorithms that are based on principles of natural selection and genetics. It is a search technique used in