Title of article :
Parameter estimation in mathematical models using the real coded genetic algorithms
Author/Authors :
Tutkun، نويسنده , , Nedim، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
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.
Keywords :
Dynamic system identification , Real coded genetic algorithms , Binary coded genetic algorithms , Parameter estimation , nonlinear curve fitting , ordinary differential equations
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications