Genetic algorithm optimization for extreme learning machine based microalgal growth forecasting of Chlamydomonas sp

Abstract

Currently, microalgae cultivation is one of the most promising alternative solutions to alleviate the value of CO2 concentration. Microalgae growth rate is convinced to be the indicator to measure the effectiveness in capturing CO2. In this paper, the microalgal growth behavior by means of various pH concentrations is observed. From the observation data, the growth behavior is modeled by regression graphs using single hidden layer feed-forward network (SLFN). To train and test the data, extreme learning machine (ELM) algorithm is applied. Recently, ELM is approved to be the fastest algorithm to learn an SLFN for regression. ELM is also well-known for its high learning accuracy as various activation functions can be applied in hidden layer. Yet the over-fitting in regression is still an issue in ELM. Thus to alleviate this problem cross-validation method is employed. To optimize the algorithm, ELM is also combined with Genetic Algorithm. The result shows that regression using ELM-GA is more accurate than ELM in various numbers of neurons.