Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)179-190
Number of pages11
JournalComputers and Operations Research
Volume106
Issue numberJune
Early online date1 Jan 2018
DOIs
Publication statusPublished - Jun 2019

Fingerprint

Genetic programming
Genetic Programming
Local Search
Forecasting
Innovation
Semantics
Local search
Forecasting performance
Regional systems of innovation
Economics
Economic and social effects
Patents
Best Practice
Competitiveness
Historical Data
Innovation performance
Regional innovation
Knowledge Base
Optimization Methods
Learning systems

Keywords

  • Forecasting
  • Genetic algorithms
  • Genetic programming
  • Knowledge based systems
  • Learning systems
  • Local search (optimization)
  • Patents and inventions
  • Optimization
  • Regional planning
  • Semantic WebSemantics
  • Technological forecasting

Cite this

@article{6aa2515823bd48308f0d41e88f963222,
title = "Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer",
abstract = "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.",
keywords = "Forecasting, Genetic algorithms, Genetic programming, Knowledge based systems, Learning systems, Local search (optimization), Patents and inventions, Optimization, Regional planning, Semantic WebSemantics, Technological forecasting",
author = "Petr Hajek and Roberto Henriques and Mauro Castelli and Leonardo Vanneschi",
note = "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",
year = "2019",
month = "6",
doi = "10.1016/j.cor.2018.02.001",
language = "English",
volume = "106",
pages = "179--190",
journal = "Computers & Operations Research",
issn = "0305-0548",
publisher = "PERGAMON-ELSEVIER SCIENCE LTD",
number = "June",

}

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

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

VL - 106

SP - 179

EP - 190

JO - Computers & Operations Research

JF - Computers & Operations Research

SN - 0305-0548

IS - June

ER -