This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Batty, Michael, Yichun **e, and Zhanli Sun. 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems 23 (3): 205–233.
Belew, Richard K, John McInerney, and Nicol N Schraudolph. 1990. Evolving networks: Using the genetic algorithm with connectionist learning.
Bergstra, James, Dan Yamins, and David D Cox. 2013. Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th python in science conference.
Bergstra, James, Brent Komer, Chris Eliasmith, Dan Yamins, and David D Cox. 2015. Hyperopt: A python library for model selection and hyperparameter optimization. Computational Science & Discovery 8 (1): 014008.
Casas, Noe. 2015. Genetic algorithms for multimodal optimization: A review. ar**v preprint ar**v:1508.05342.
Ding, Shifei, Hui Li, Chunyang Su, Junzhao Yu, and Fengxiang **. 2013. Evolutionary artificial neural networks: A review. Artificial Intelligence Review 39 (3): 251–260.
Erhan, Dumitru, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11 : 625–660.
Falkner, Stefan, Aaron Klein, and Frank Hutter. 2018. BOHB: Robust and efficient hyperparameter optimization at scale. ar**v preprint ar**v.
Klesk, Przemyslaw. 2008. Construction of a neurofuzzy network capable of extrapolating (and interpolating) with respect to the convex hull of a set of input samples in Rn. IEEE Transactions on Fuzzy Systems 16 (5): 1161–1179.
Krige, Danie G. 1978. Lognormal-de Wijsian geostatistics for ore evaluation. South African Institute of mining and metallurgy Johannesburg.
Lam, Nina Siu-Ngan. 1983. Spatial interpolation methods: A review. The American Cartographer 10 (2): 129–150.
Le, Nhu D, and James V Zidek. 2006. Statistical analysis of environmental space-time processes. Springer Science & Business Media.
Li, **a, and Anthony Gar-On Yeh. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science 16 (4): 323–343.
Li, **, Andrew D Heap, Anna Potter, James J, and Daniell. 2011. Application of machine learning methods to spatial interpolation of environmental variables. Environmental Modelling and Software 26 (12): 1647–1659.
Li, **, and Andrew D. Heap. 2014. Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software 53: 173–189.
Lorenzo, Pablo Ribalta, Jakub Nalepa, Luciano Sanchez Ramos, and José Ranilla Pastor. 2017. Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. In: Proceedings of the genetic and evolutionary computation conference companion.
Matheron, Georges. 1963. Principles of geostatistics. Economic Geology 58 (8): 1246–1266.
Mitchell, Tom M. 1997. Machine learning. 1997. Burr Ridge, IL: Mcgraw Hill 45 (37): 870–877.
Pijanowski, Bryan C., Daniel G. Brown, Bradley A. Shellito, and Gaurav A. Manik. 2002. Using neural networks and GIS to forecast land use changes: A land transformation model. Computers, Environment and Urban Systems 26 (6): 553–575.
Pijanowski, Bryan C, Amin Tayyebi, Jarrod Doucette, Burak K Pekin, David Braun, and James Plourde. 2014. A big data urban growth simulation at a national scale: Configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling & Software 51: 250–268.
Pitiot, Paul, Thierry Coudert, Laurent Geneste, and Claude Baron. 2009. A priori knowledge integration in evolutionary optimization. In: International conference on artificial evolution (evolution artificielle).
Ratle, Alain. 2001. Kriging as a surrogate fitness landscape in evolutionary optimization. AI EDAM 15 (1): 37–49.
Sareni, Bruno, and Laurent Krahenbuhl. 1998. Fitness sharing and niching methods revisited. IEEE Transactions on Evolutionary Computation 2 (3): 97–106.
Schwarz, Josef, and Jifi Ocenasek. 2000. A problem knowledge-based evolutionary algorithm KBOA for hypergraph bisectioning. In: Proceedings of the 4th joint conference on knowledge-based software engineering. IOS Press.
Sui, D.Z., and R.C. Maggio. 1999. Integrating GIS with hydrological modeling: Practices, problems, and prospects. Computers, Environment and Urban Systems 23 (1): 33–51.
Wackernagel, H.L., Bertino, J.P., Sierra, and J.G.D. Río. 2002. Multivariate kriging for interpolating with data from different sources. In Quantitative methods for current environmental issues, 57–75. Springer.
Yao, **n. 1994. The evolution of connectionist networks. In Artificial intelligence and creativity, 233–243. Springer.
Young, Steven R, Derek C Rose, Thomas P Karnowski, Seung-Hwan Lim, and Robert M Patton. 2015. Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of the workshop on machine learning in high-Performance computing environments.
Yu, Jianbo, Shi** Wang, and Lifeng **. 2008. Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing 71 (4–6): 1054–1060.
Zhang, Chunkai, Huihe Shao, and Yu Li. 2000. Particle swarm optimisation for evolving artificial neural network. In: Systems, man, and cybernetics, 2000 IEEE international conference on.
Zheng, Minrui, Wenwu Tang, and **ang Zhao. 2019. Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2018.1530355.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zheng, M. (2021). Methodology. In: Spatially Explicit Hyperparameter Optimization for Neural Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-5399-5_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-5399-5_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5398-8
Online ISBN: 978-981-16-5399-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)