Title :

Problem Features vs. Algorithm Performance on Rugged Multi-objective Combinatorial Fitness Landscapes

Author(s) :

Daolio, Fabio [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
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]

Journal title :

Evolutionary Computation

Pages :

555–585

Publisher :

Massachusetts Institute of Technology Press (MIT Press)

Publication date :

2017

ISSN :

1063-6560

English keyword(s) :

Evolutionary multi-objective optimization
black-box 0–1 multi-objective problems
feature-based analysis
fitness landscape and problem difficulty
empirical performance modeling
multi-level multi-variate analysis
random-effects mixed models

HAL domain(s) :

Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Optimisation et contrôle [math.OC]

English abstract : [en]

In this paper, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multi-objective combinatorial optimization problems. At first, we ...
Show more >In this paper, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multi-objective combinatorial optimization problems. At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space dimension, and objective correlation (ρMNK-landscapes). Precisely, we investigate the expected runtime required by a global evolutionary optimization algorithm with an er-godic variation operator (GSEMO) and by a neighborhood-based local search heuristic (PLS), to identify a (1 + ε)−approximation of the Pareto set. Then, we define a number of problem features characterizing the fitness landscape, and we study their intercor-relation and their association with algorithm runtime on the benchmark instances. At last, with a mixed-effects multi-linear regression we assess the individual and joint effect of problem features on the performance of both algorithms, within and across the instance classes defined by benchmark parameters. Our analysis reveals further insights into the importance of ruggedness and multi-modality to characterize instance hardness for this family of multi-objective optimization problems and algorithms.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