Title :

Towards landscape-aware automatic algorithm configuration: preliminary experiments on neutral and rugged landscapes

Author(s) :

Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Scientific editor(s) :

Bin Hu
Manuel López-Ibáñez

Conference title :

EvoCOP 2017 - Conference on Evolutionary Computation in Combinatorial Optimisation

City :

Amsterdam

Country :

Pays-Bas

Start date of the conference :

2017-04-19

Book title :

Evolutionary Computation in Combinatorial Optimization17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings

Journal title :

Lecture Notes in Computer Science (LNCS)

Publisher :

Springer

Publication date :

2017-03

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

The proper setting of algorithm parameters is a well-known issue that gave rise to recent research investigations from the (offline) automatic algorithm configuration perspective. Besides, the characteristics of the target ...
Show more >The proper setting of algorithm parameters is a well-known issue that gave rise to recent research investigations from the (offline) automatic algorithm configuration perspective. Besides, the characteristics of the target optimization problem is also a key aspect to elicit the behavior of a dedicated algorithm, and as often considered from a landscape analysis perspective. In this paper, we show that fitness landscape analysis can open a whole set of new research opportunities for increasing the effectiveness of existing automatic algorithm configuration methods. Specifically, we show that using landscape features in iterated racing both (i) at the training phase, to compute multiple elite configurations explicitly mapped with different feature values, and (ii) at the production phase, to decide which configuration to use on a feature basis, provides significantly better results compared against the standard landscape-oblivious approach. Our first experimental investigations on NK-landscapes, considered as a benchmark family having controllable features in terms of ruggedness and neutrality, and tackled using a memetic algorithm with tunable population size and variation operators, show that a landscape-aware approach is a viable alternative to handle the heterogeneity of (black-box) combinatorial optimization problems.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL