Project Team Tao

Members
Overall Objectives
Scientific Foundations
Application Domains
Contracts and Grants with Industry
Bibliography

Section: New Results

Crossing the Chasm

Participants : Alejandro Arbelaez, Anne Auger, Robert Busa-Fekete, Nikolaus Hansen, Balázs Kégl, Manuel Loth, Nadjib Lazaar, Marc Schoenauer, Michèle Sebag.

Due to the departure of both PhD students funded within the Microsoft-INRIA joint lab after their successful defenses (Alvaro Fialho and Alejandro Arbelaez), some of the activities of this SIG have been slightly redefined this year, with the one-month visit of Prof. Th. Runarsson (University of Iceland) in October, and the arrival in November of two new post-docs, also funded by the joint lab (Nadjib Lazaar and Manuel Loth). A new direction of research has appeared, in line with both Adaptive Operator Selection (Alvaro Fialho's PhD) and Continuous Search (Alejandro Arbelaez' PhD).

Bandit-based choice of heuristics in combinatorial optimization

This new direction of research deals with heuristic choice within an existing combinatorial solver using bandit-like algorithms, and the very first results deal with scheduling problems and will be published in early 2012 [57] .

In line with his PhD work, Alvaro Fialho has succesfully used his Adaptive Operator Selection method to the on-line tuning of Differential Evolution in the multi-objective case [96] .

Learn and Optimize (LaO),

an instance-based parameter-tuning method. Though originally designed for Divide-And-Evolve framework (see Section 6.2 ), LaO is a generic method that learns the relationship between some instance features and the optimal parameters of the optimizer. The current version [49] , [50] , [51] uses Neural Network to directly learn the optimal parameters, and average performance increase compared to the default parameter set (that has won the temporal track in the IPC7 competition) is of more than 10%. On-going work uses rankSVM to learn a partial order on the features $×$ parameter space.