Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer
Created by W.Langdon from
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- @Article{HAJEK:2018:COR,
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author = "Petr Hajek and Roberto Henriques and
Mauro Castelli and Leonardo Vanneschi",
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title = "Forecasting performance of regional innovation systems
using semantic-based genetic programming with local
search optimizer",
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journal = "Computer \& Operations Research",
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year = "2018",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0305-0548",
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DOI = "doi:10.1016/j.cor.2018.02.001",
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URL = "http://www.sciencedirect.com/science/article/pii/S0305054818300327",
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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",
- }
Genetic Programming entries for
Petr Hajek
Roberto Henriques
Mauro Castelli
Leonardo Vanneschi
Citations