Ensemble strategies for population-based optimization algorithms - A survey.

In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constraint handling method, local search method, surrogate model, niching method, etc. In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, tournament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on. In POA literature, No Free Lunch (NFL) theorem has been well-documented and therefore, to effectively solve a given optimization problem, an appropriate configuration is necessary. But, the trial and error approach for the appropriate configuration may be impractical because at different stages of evolution, the most appropriate configurations could be different depending on the characteristics of the current search region for a given problem.