Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Searching customer patterns of mobile service using clustering and quantitative association rule
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Trading strategy design in financial investment through a turning points prediction scheme
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A genetic encoding approach for learning methods for combining classifiers
Expert Systems with Applications: An International Journal
An evolutionary computation approach for designing mobile ad hoc networks
Expert Systems with Applications: An International Journal
An evolutionary factor analysis computation for mining website structures
Expert Systems with Applications: An International Journal
A genetic search of patterns of behaviour in OSS communities
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper proposes to identify strategic groups among franchisors from a big set of franchisor variables. Genetic evolutionary computation was used to reduce a set of variables efficiently, and factor analysis was used to make up the strategic groups. Franchise 500 was used as database. The results suggest both that the general map of franchisor has changed since Carney and Gedajlovic's study, and that genetic evolutionary computation is a valid way to extract knowledge from a huge set of data. This paper proposes useful information for those retail firms considering internationalization via franchising. The originality of this paper is in the use of Genetic Algorithm to discriminate the final set of variables to be used for the identification of strategic groups instead of evaluating one by one the adequacy of each variable theoretically. The ability of evolutionary computation to create new knowledge is good to produce new insights into this topic.