  This paper considers the two-stage capacitated facility location problem
(TSCFLP) in which products manufactured in plants are delivered to customers
via storage depots. Customer demands are satisfied subject to limited plant
production and limited depot storage capacity. The objective is to determine
the locations of plants and depots in order to minimize the total cost
including the fixed cost and transportation cost. A hybrid evolutionary
algorithm (HEA) with genetic operations and local search is proposed. To avoid
the expensive calculation of fitness of population in terms of computational
time, the HEA uses extreme machine learning to approximate the fitness of most
of the individuals. Moreover, two heuristics based on the characteristic of the
problem is incorporated to generate a good initial population.
  Computational experiments are performed on two sets of test instances from
the recent literature. The performance of the proposed algorithm is evaluated
and analyzed. Compared with the state-of-the-art genetic algorithm, the
proposed algorithm can find the optimal or near-optimal solutions in a
reasonable computational time.
