Author(s):
Hajek, Petr ; Henriques, Roberto ; Castelli, Mauro ; Vanneschi, Leonardo
Date: 2019
Persistent ID: http://hdl.handle.net/10362/145907
Origin: Repositório Institucional da UNL
Subject(s): Forecasting; Genetic algorithms; Genetic programming; Knowledge based systems; Learning systems; Local search (optimization); Patents and inventions; Optimization; Regional planning; Semantic WebSemantics; Technological forecasting; Computer Science(all); Modelling and Simulation; Management Science and Operations Research; SDG 11 - Sustainable Cities and Communities
Description
Hajek, P., Henriques, R., Castelli, M., & Vanneschi, L. (2019). Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer. Computers and Operations Research, 106(June), 179-190. [advanced online on 7 February 2018]https://doi.org/10.1016/j.cor.2018.02.001 . Doi: https://doi.org/10.1016/j.cor.2018.02.001 ---%ABS3%
Innovation performance of regional innovation systems can serve as an important tool for policymaking to identify best practices and provide aid to regions in need. Accurate forecasting of regional innovation performance plays a critical role in the implementation of policies intended to support innovation because it can be used to simulate the effects of actions and strategies. However, innovation is a complex and dynamic socio-economic phenomenon. Moreover, patterns in regional innovation structures are becoming increasingly diverse and non-linear. Therefore, to develop an accurate forecasting tool for this problem represents a challenge for optimization methods. The main aim of the paper is to develop a model based on a variant of genetic programming to address the regional innovation performance forecasting problem. Using the historical data related to regional knowledge base and competitiveness, the model should accurately and effectively predict a variety of innovation outputs, including patent counts, technological and non-technological innovation activity and economic effects of innovations. We show that the proposed model outperforms state-of-the-art machine learning methods.
