In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice, the child solutions generated by crossover and mutation are largely random and therefore cannot ensure the fast convergence of the algorithm. To tackle the weakness of traditional GA, the ELM is introduced to estimate the nonlinear functional relationships between the parent population and child p...
