A.Y. HAIKAL, M.S.M. KSASY , S . F . SARAYA, F . F . AREED
Computers & Systems Dept., Faculty of Engineering, Mansoura University
The work proposed in this paper concerns an effective genetic algorithm that works on
two levels, neuron-level and blue prints networks-level. The present algorithm differs from
standard evolutionary algorithms in that it evolves a population of neurons instead of
complete networks. These neurons are combined through different subpopulation to form
hidden layers of feed-forward networks that are then evaluated on a given problem. The
applicability of evolutionary reinforcement learning techniques was investigated through
the proposed algorithm. Bioreactor Process control simulation benchmark is considered,
which exhibit highly nonlinear behaviour and consequently complex regions of operation.
This simulation study include developing optimal neurocontroller for the reactor. The
neurocontroller developed was found to be robust as a result of generalization afforded by
the presented learning algorithm.