This paper describes an application of a neural network approach to
the SM (standard model) and the MSSM (minimal supersymetry standard model) Higgs search in the associated production ttH with H —> bb. This decay channel is considered as a discovery channel for Higgs scenarios for Higgs boson masses in the range 80 - 130 GeV. A neural network model with a special type of data flow is used to separate ttjj background from H —> bb events. The neural network used combines together a classical neural network approach and a linear decision tree separation process. The parameters of these neural networks are randomly generated and the population of the predefined size of those networks is learned to get initial generation for the following genetic algorithm optimization process. A genetic algorithm principles are used to tnne parameters of further neural network individuals derived from previous neural networks by GA operations of crossover and mutation. The goal of this GA process is optimization of the final neural network performance.
Our results show that the NN approach is applicable to the problem of Higgs bosou detection. Neural network filters can be used to emphasize the difference of the Mbb distribntion for events accepted by filter from the distribntion for original events under condition that there is no loss of significance. This improvement of the shape of the Mbb, distribntion can be used as a criterion of existence of Higgs bosou decay in the discovery channel considered.