Abstract
The introduction of a hybrid genetic programming method (hGP) in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development) countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.
Highlights
Many methods have been proposed for predicting the penetration of new technology in a community
The initial population is generated by the fusion of a randomly produced number of chromosomes and the diffusion models which are optimized by regression analysis
This paper introduces a new GP method that produces fitting and forecasting solutions models with well enough performance
Summary
Many methods have been proposed for predicting the penetration of new technology in a community. Examples of the above methods are the diffusion models for the adoption of new technologies. GP is more general than GA, because the produced solution corresponds to a new program 8. The generation is the resultant of the Darwinian selection process. In this process, the best chromosomes, according to their fitness values, are selected for the generation. The mutation process occurs, according to which a part of a randomly selected chromosome is changing. The chromosomes with better fitness values have better probability of being selected. The chromosomes of the new generation have better overall fitness value than those of the past generations. The basic structure of the new modified GP hGP and its analysis follow. In the Appendices A, B, and C, the syntax of some produced models and the statistic indicators as well as the estimation formulas of the modified GA are provided
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