Biology-inspired optimization algorithms applied to intelligent input weights selection of an extreme learning machine in regression problems.

Modern artificial neural network architectures and training algorithms are able to achieve high accuracy in a wide range of problems. However, training multilayer neural networks using backpropagation or evolutionary algorithms might take a large amount of time. Extreme learning machines (ELMs) are aimed to resolve this problem by excluding training from the neural network model creation process by randomly initializing weights between input and hidden layers and computing weights between hidden and output layers. However, random weights initialization might lead to suboptimal results produced by the network. In this paper, we apply biology-inspired algorithms, including genetic algorithm with tournament selection, particle swarm optimization, and chaotic fish school search with exponential step decay, to the selection of input weights in ELM, the obtained ELM configurations are applied to solve regression problems. The results of the study show, that population-based algorithms can improve ELM accuracy. [ABSTRACT FROM AUTHOR]