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New coral reefs-based approaches for the model type selection problem: a novel method to predict a nation's future energy demand

In this paper, we describe two new methods to address the model type selection problem (MTSP) based on modifications of the coral reefs optimisation algorithm (CRO). The effectiveness of these novel approaches is subsequently illustrated in a problem of energy demand estimation in Spain. First, we describe how coral species can be defined in the CRO algorithm, so each specie defines a competing model for the MTSP. Second, we propose another method to solve MTSPs by modifying the original CRO with a substrate layer, so that the different models considered can be encoded similarly. This second method to solve the MTSP simplifies the application of the CRO operators. Finally, we evaluate the performance of the two CRO-based algorithms by solving a MTSP consisting of the prediction of future energy demand from macro-economic data in Spain as a case study.
- University of Amsterdam Netherlands
- University of Alcalá Spain
- University of Alcalá Spain
570, Model type selection problem, Reef substrate layer, Coral species, 510, Macroeconomic variables, Theoretical Computer Science, Energy demand estimation, Coral reefs optimisation algorithm, Computer Science(all)
570, Model type selection problem, Reef substrate layer, Coral species, 510, Macroeconomic variables, Theoretical Computer Science, Energy demand estimation, Coral reefs optimisation algorithm, Computer Science(all)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).26 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
