Parameter estimation in mathematical models using the real coded genetic algorithms

Authors:
Nedim Tutkun
Affiliations:
Department of Electrical and Electronic Engineering, University of Zonguldak, University Park, 67100 Zonguldak, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 5
Cited 2

A method for solving the parameter identification problem for ordinary differential equations of the second order

Applied Mathematics and Computation
Parameter estimation of an enzyme kinetic system using computer algebra techniques

Applied Mathematics and Computation
An Introduction to Genetic Algorithms for Scientists and Engineers

An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Parameter estimation of nonlinear models in biochemistry: a comparative study on optimization methods

Applied Mathematics and Computation

A modified Pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly

Advances in Engineering Software
Optimizing time limits for maximum sales response in Internet shopping promotions

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this study, parameter estimation in mathematical models using the real coded genetic algorithms (RCGA) approach is presented. Although the RCGA is similar with the binary coded genetic algorithms (BCGA) in terms of genetic process, it has few advantages such as high precision, non-existence of Hamming's cliff etc., over the BCGA. In this approach, creating initial population and selection procedure are almost the same with the BCGA, but crossover and mutation operations. The proposed approach is implemented on the second order ordinary differential equations modeling the enzyme effusion problem and it is compared with previous approaches. The results indicate that the proposed approach produced better estimated results with respect to previous findings.