By Paola Festa, Meinolf Sellmann, Joaquin Vanschoren
This publication constitutes the completely refereed post-conference court cases of the tenth overseas convention on studying and Optimization, LION 10, which used to be hung on Ischia, Italy, in May/June 2016.
The 14 complete papers offered including nine brief papers and a pair of GENOPT papers have been conscientiously reviewed and chosen from forty seven submissions. The papers handle all fields among computing device studying, synthetic intelligence, mathematical programming and algorithms for demanding optimization difficulties. certain concentration is given to new rules and techniques; demanding situations and possibilities in a variety of software components; normal traits, and particular developments.
Read or Download Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, Revised Selected Papers PDF
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Extra info for Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, Revised Selected Papers
Let X s , s = 1, . . , S be some sample sets from the domain of F 4 . Features Φ(F s ) (vector of IRF if F is the number of features) are computed for F from sample set (X s , Y s ) (with yi = F(xi ) for all (xi , yi ) ∈ (X s , Y s ), see Sect. 2). Let us denote by Φ(F ∗ ) the features computed from the largest sample set (of size 2000). Each sample set X s is also used to learn some surrogate models Fts using diﬀerent surrogate modelling techniques t = T1 , . . , TT . , X s ⊂ Xts,s and, for all (xi , yi ) ∈ (Xts,s , Yts,s ), yi = F(xi ) if xi ∈ X s and yi = Fts (xi ) otherwise).
33, 565–606 (2008) MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework Aymeric Blot1,2,3(B) , Holger H. de Abstract. Automated algorithm conﬁguration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these conﬁguration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being conﬁgured.
5 Experimental Protocol and Validation Experimental Protocol and Notations For a given objective function F (one trial of one instance of one d−dimensional function from BBOB), the basic experiment goes as follows: one sample sets of given size is drawn from the deﬁnition domain of F; the features are computed on this sample set, and a surrogate model using one of the chosen modelling techniques. The sample set is then completed with more samples, using the surrogate model in lieu of the original function.