Titre :

Inferring Future Landscapes: Sampling the Local Optima Level

Auteur(s) :

Thomson, Sarah [Auteur]
University of Stirling
Ochoa, Gabriela [Auteur]
University of Stirling
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Veerapen, Nadarajen [Auteur]
Operational Research, Knowledge And Data [ORKAD]

Titre de la revue :

Evolutionary Computation

Pagination :

621-641

Éditeur :

Massachusetts Institute of Technology Press (MIT Press)

Date de publication :

2020-12

ISSN :

1063-6560

Mot(s)-clé(s) en anglais :

Combinatorial Optimisation
Fitness Landscapes
Local Optima Networks
Funnel Landscapes

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Optimisation et contrôle [math.OC]

Résumé en anglais : [en]

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to ...
Lire la suite >Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this paper, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this 'super-sampling': a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL