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

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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of business research 2004, Vol.57 (1), p.79-85
Main Authors: Fish, Kelly E, Johnson, John D, Dorsey, Robert E, Blodgett, Jeffery G
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural network
Back propagation
Backpropagation
Brands
Business
Choice model
Genetic algorithm
Genetic algorithms
Market shares
Mathematical models
Multinomial logit
Networks
Neural networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0148-2963
1873-7978
DOI:10.1016/S0148-2963(02)00287-4