@article {pub3068,
	title = {A new self-adaptation scheme for differential evolution},
	author = {Xiaofen Lu AND Ke Tang AND Bernhard Sendhoff AND Xin Yao},
	year = {2014},
	month = {April},
	abstract = {The performance of Differential Evolution (DE) largely depends on the choice of trial
vector generation strategy and the values of its control parameters. In the past years,
quite a few DE variants have been developed to adaptively adjust the strategy and control
parameters during the search process. However, these variants may not perform
satisfactorily when coping with computationally expensive problems (CEPs), for which
a satisfying solution needs to be obtained with very limited fitness evaluations (FEs).
In this paper, we demonstrate that not only can surrogate models be used to approximate
the fitness function, they can also provide a good alternative method to adapt the
strategy and control parameters of DE, and thus propose a framework called DE with
Surrogate-assisted Self-Adaptation (DESSA). DESSA generates multiple trial vectors
using different trial vector generation strategies and parameter settings, and then employs
a surrogate model to identify the potentially best trial vector to undergo real
fitness evaluation. As each trial vector corresponds to a unique combination of strategy
and parameter setting, the surrogate model acts like a strategy/parameter setting
selector that aims to identify the most suitable strategy and parameter setting for each
target vector. Since DESSA can be easily combined with different DE variants, three
concrete DE variants, namely DESSA-CoDE, DESSA-SaDE, and DESSA-CoDE*, are
proposed. Comprehensive empirical studies demonstrate that DESSA can lead to superior
performance over the compared adaptive DE variants. More important, it is shown
that DESSA has the potential of accommodating more search strategies, which may
lead to novel DE variants with even more competitive performance.},
	publisher = {Elsevier },
	journal = {Neurocomputing},
	volume = {146},
	pages = {2-16}
}