Intelligent process control utilising symbiotic memetic neuro-evolution

Author

Conradie, A. V E ; Miikkulainen, R. ; Aldrich, C.

Author_Institution

Dept. of Chem. Eng., Stellenbosch Univ., South Africa

Volume

1

fYear

2002

Firstpage

623

Lastpage

628

Abstract

A novel reinforcement learning algorithm, called symbiotic memetic neuro-evolution (SMNE), is presented for neurocontroller development in nonlinear processes. A highly nonlinear bioreactor process is used in a learning efficiency case study. The use of implicit fitness sharing maintains genetic diversity and induces niching pressure, which enhances the synergetic effect between the global search (symbiotic evolutionary algorithm) and the local search (particle swarm optimisation). SMNE´s synergetic effect accelerates learning, which translates to greater economic return for the process industries

Keywords

biotechnology; evolutionary computation; intelligent control; learning (artificial intelligence); learning systems; neurocontrollers; nonlinear control systems; optimal control; process control; search problems; bioreactor process; case study; economic return; genetic diversity; global search; implicit fitness sharing; intelligent process control; learning efficiency; local search; neurocontroller development; niching pressure; nonlinear processes; particle swarm optimisation; process industries; reinforcement learning algorithm; symbiotic evolutionary algorithm; symbiotic memetic neuro-evolution; synergetic effect; Acceleration; Bioreactors; Evolutionary computation; Genetics; Intelligent control; Learning; Neurocontrollers; Particle swarm optimization; Process control; Symbiosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1006998

Filename

1006998