Forecasting tourists’ characteristics by a genetic algorithm with a transition matrix

Abstract The number of tourists that visit a mature tourism destination becomes stable. If recommendations that could adjust the supply structure towards demand needs in this scenario are established as administration goals, then forecasting the total number of tourists is important, but of particular interest is the internal composition of the demand in terms of specific characteristics of the future groups. This view type of forecasts can be achieved by a genetic algorithm (GA). In this paper, a GA with a transition probability matrix is developed and, as an illustration of the methodology, one run of such an algorithm has achieved better forecasting performance than a simple GA.

[1]  Amit Mitra,et al.  Forecasting daily foreign exchange rates using genetically optimized neural networks , 2002 .

[2]  Maurizio Bielli,et al.  Genetic algorithms in bus network optimization , 2002 .

[3]  Ganesh Mani,et al.  Financial Forecasting Using Genetic Algorithms , 1996, Appl. Artif. Intell..

[4]  Luiz Moutinho,et al.  Genetic algorithms for tourism marketing , 1998 .

[5]  Montserrat Hernández-López.  Future Tourists' Characteristics and Decisions: The Use of Genetic Algorithms as a Forecasting Method , 2004 .

[6]  M. Gómez,et al.  Modelización semiparamétrica y validación teórica del método de valoración contingente. Aplicación de un algoritmo genético , 2003 .

[7]  Herbert Dawid,et al.  Adaptive Learning by Genetic Algorithms, Analytical Results and Applications to Economic Models, 2nd extended and revised edition , 1999 .

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Enrique Bonsón Ponte,et al.  Algoritmos genéticos: aplicaciones financieras y de gestión , 1995 .

[10]  J. P. Bonrostro,et al.  Estudio comparativo de diferentes estrategias metaheurísticas para la resolución del labor scheduling problem , 2003 .

[11]  Jasmina Arifovic.  Genetic algorithm learning and the cobweb model , 1994 .

[12]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  V. Cho.  A comparison of three different approaches to tourist arrival forecasting , 2003 .

[15]  Rajkumar Venkatesan,et al.  A genetic algorithms approach to growth phase forecasting of wireless subscribers , 2002 .

[16]  J. Duffy,et al.  Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs , 1999 .

[17]  Jasmina Arifovic,et al.  Genetic algorithms and inflationary economies , 1995 .

[18]  Goh Bee-Hua,et al.  Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector , 2000 .