Using immune-based genetic algorithms for single trader's periodic marketing problem

Authors:
Ta-Cheng Chen;Yi-Chieh Hsieh
Affiliations:
Department of Information Management, National Formosa University, Yunlin, Taiwan;Department of Industrial Management, National Formosa University, Yunlin, Taiwan
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2008

Citing 4
Cited 1

The immune system, adaptation, and machine learning

Physica D
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm

Structural damage identification based on Bayesian theory and improved immune genetic algorithm

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.98

Visualization

Abstract

This study considers a single trader's periodic marketing problem in which both the spatial (where) and the temporal (when) dimensions are decided simultaneously to maximize the total payoff during the trading time horizon. In the past, researchers paid more attention on the periodic marketing problems to figure out why such markets arise and how they are organized in space. Few investigations have been carried out to find the most profitable itinerant route for the traders. In this paper, an immune-based genetic algorithm has been applied to transfer the mathematical model of the natural immune system into computational algorithm. Numerical examples indicate that the proposed approach can perform well for solving the single trader's periodic marketing problem in terms of obtaining the most profitable itinerant strategies.