A new model selection approach for the ELM network using metaheuristic optimization

We propose a novel approach for architecture selection and hidden neurons excitability improvement for the Extreme Learning Machine (ELM). Named Adaptive Number of Hidden Neurons Approach (ANHNA), the proposed approach relies on a new general encoding scheme of the solution vector that automatically estimates the number of hidden neurons and adjust their activation function parameters (slopes and bi- ases). Due to its general nature, ANHNA's encoding scheme can be used by any metaheuristic algorithm for continuous optimization. Computer ex- periments were carried out using Differential Evolution (DE) and Particle Swarm Optimization (PSO) metaheuristics, with promising results being achieved by the proposed method in benchmarking regression problems.