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

作者：Salcedo Sanz Sancho^*; Munoz Bulnes Jesus; Vermeij Mark J A

来源：International Journal of Bio-Inspired Computation, 2017, 10(3): 145-158.

DOI：10.1504/IJBIC.2017.10004324

• model type selection problem
• coral reefs optimisation algorithm
• coral species
• reef substrate layer
• energy demand estimation
• macroeconomic variables

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.

• 出版日期2017