Using an artificial neural network trained with a genetic algorithm to model brand share

Abstract

We introduce a new architectural approach to artificial neural network (ANN) choice modeling. The standard ANN design with a polychotomous situation requires an output variable for each alternative. We reconfigure our feedforward network to contain only one output node for a six-level choice problem and network performance improves considerably. We conclude that a simpler ANN architecture leads to better generalization in the case of multilevel choice. We then use a feedforward ANN trained with a genetic algorithm to model individual consumer choices and brand share in a retail coffee market. A well-known choice model is replicated while the computer-processing technique is altered from multinomial logit (MNL) to feedforward ANNs trained with the standard backpropagation algorithm and a genetic algorithm. The ANN trained with our genetic algorithm outperforms both MNL and the backpropagation trained ANN.