TY - JOUR
T1 - Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer
AU - Hajek, Petr
AU - Henriques, Roberto
AU - Castelli, Mauro
AU - Vanneschi, Leonardo
N1 - 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%
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Forecasting
KW - Genetic algorithms
KW - Genetic programming
KW - Knowledge based systems
KW - Learning systems
KW - Local search (optimization)
KW - Patents and inventions
KW - Optimization
KW - Regional planning
KW - Semantic WebSemantics
KW - Technological forecasting
UR - http://www.scopus.com/inward/record.url?scp=85041908079&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000466620800016
U2 - 10.1016/j.cor.2018.02.001
DO - 10.1016/j.cor.2018.02.001
M3 - Article
AN - SCOPUS:85041908079
SN - 0305-0548
VL - 106
SP - 179
EP - 190
JO - Computers and Operations Research
JF - Computers and Operations Research
IS - June
ER -