Let a biogeography-based optimizer train your Multi-Layer Perceptron

By Seyedali Mirjalili, Andrew Lewis. Information Sciences, 2014

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Optimizing connection weights in neural networks using the whale optimization algorithm (2016, methodology): “In this work, we follow the same method proposed and used in Wdaa (2008), Mirjalili (2014) where the number of neurons in the hidden layer is selected based on the following formula: 2 × N + 1, where N is number of dataset features.”
Biogeography-based optimisation with chaos (2014, unknown): “In other words, habitats can be evolved based on their HSI as follows [31]: These rules assist habitats to improve the HSI of each other and consequently evolving the initial random solutions for a given problem.”
Elephant Herding Optimization (2015, methodology): “By comparing with BBO [53-58], DE [59-64] and GA [65], the performance of EHO is investigated by several experiments implemented on fifteen test cases.”