Abstract
We propose in this paper a modification of one of the modern state-of-the-art genetic programming algorithms used for data-driven modeling, namely the Bi-objective Genetic Programming (BioGP). The original method is based on a concurrent minimization of both the training error and complexity of multiple candidate models encoded as Genetic Programming trees. Also, BioGP is empowered by a predator-prey co-evolutionary model where virtual predators are used to suppress solutions (preys) characterized by a poor trade-off error vs complexity. In this work, we incorporate in the original BioGP an adaptive mechanism that automatically tunes the mutation rate, based on a characterization of the current population (in terms of entropy) and on the information that can be extracted from it. We show through numerical experiments on two different datasets from the energy domain that the proposed method, named BioAGP (where “A” stands for “Adaptive”), performs better than the original BioGP, allowing the search to maintain a good diversity level in the population, without affecting the convergence rate.
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Notes
- 1.
We should observe, however, that the main drawback of data-driven modeling methods is that they depend entirely on experimental data. Therefore, such methods can only be applied after the actual building is built and measurements are available.
- 2.
In the rest of the paper, we will consider problems with p = 1 output variable. Nonetheless multiple output variables can be approximated by multiple GP trees, one per variable. An extension of the analysis for p > 1 is also possible and will be considered in future studies.
- 3.
- 4.
We should remark that among all the Pareto-optimal solutions returned by BioAGP, we report here the ones characterized by the lowest training error, following the approach suggested in [11]. However other choices e.g. based on information criteria can also be made.
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Bevilacqua, V., Nuzzolese, N., Mininno, E., Iacca, G. (2016). Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_24
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