
A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD CountriesDOI: 10.1155/2012/904797 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 wellknown 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 Cooperation and Development) countries. The results of the optimized diffusion models are compared to those of the hGPgenerated models. The comparison indicates that the hGP manages to generate solutions with highperformance 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. 1. Introduction Many methods have been proposed for predicting the penetration of new technology in a community. The subject has been described and analyzed by worldwide literature, extensively [1–6]. Examples of the above methods are the diffusion models for the adoption of new technologies. The diffusion models are mathematical functions that follow an Sshaped curve in time. The diffusion models used in this study are the Gompertz, Logistic, and Bass [4]. The parameters of the models have been estimated by regression analysis [5]. Genetic algorithm (GA) is a probabilistic search method which uses the Darwinian principle of natural selection in finding an appropriate solution of a specific problem [7]. GP is more general than GA, because the produced solution corresponds to a new program [8]. The implementation of genetic programming (GP) in optimization problems has produced some important forecasting tools [7, 8]. Generally, a GP begins with a set of initial randomly chosen functions (solutions) and this set is called population. A chromosome is a program solution of GP. Each solution has a fitness value, and this chromosome’s fitness is evaluated. The next generation is the resultant of the Darwinian selection process. In this process, the best chromosomes, according to their fitness values, are selected for the next generation. Some of the selected chromosomes are randomly combined (crossover) and generate new chromosomes (offspring). The mutation process also occurs, according to which a part of a
