Soft Computing Home Page  Approximation in
Evolutionary Computation
Fitness Approximation in Evolutionary Computation
Bibliography
Fitness evaluations in evolutionary computation are not
trivial in many realworld applications. In the following cases,
a computationally efficient approximation of the original fitness
function is necessary:
 The fitness function is computationally very expensive,
 No explicit mathematical fitness function is available,
 The original fitness function is noisy or multimodal.
Approximate models are also known as metamodels or surrogates in
optimization. Polynomials (response surface methodologies),
neural networks, such as multilayer perceptrons (MLPs), radialbasisfunction
(RBF) networks, support vector machines (SVMs), Gaussian processes,
and Kriging models (often polynomials plus Gaussian processes) can
be used for constructing metamodels.
In evolutionary optimization, ad hoc methods, such as fitness
inheritance, fitness imitation and fitness assignment can also be employed.
References on handling noisy fitness functions and search for robust
optimal solutions can be found here.
A survey of the research can be found here.
Please send me an email to include your papers (in bibtex format)
in this bibliography. Thanks.
The bibtex file of the references can be downloaded
here.
References sorted by year can be found here.
Last updated on July 12, 2005 by Yaochu Jin.
All publications sorted by name


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