# Introduction

EGO {cite}`jones98EGO` makes a global search. As a genetic algorithm, it needs an adequate numbers 
of initial evaluated items to initiate its search: in our case, to be able to construct a sufficiently 
pertinent model. After this first phase, it builds a surrogate model, and then loops on updating the 
model with new evaluations, and on searching a new promising solution to evaluate using this model.

For its surrogate model, EGO uses kriging models which provide, for estimated points, a prediction 
value and an associated variance. EGO defines an objective, the expected improvement, which takes 
both of them into account and provides a trade-off between a good estimation value and a large 
uncertainty.

Because of its efficiency in term of evaluation number, this kind of algorithm is well suited when 
evaluations are expensive to compute. EGO algorithm is expensive: both construction of the {{surmod}} 
and search of the next attractive point are complex optimization problems, and are done many times,
slowing down the problem resolution.

Extensions to constraints and/or to multi objective should come later in {{uranie}}.

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introduction/parallelism
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