On Stochastic Fitness Landscapes: Local ...
Type de document :
Communication dans un congrès avec actes
Titre :
On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators
Auteur(s) :
Aboutaib, Brahim [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Fonlupt, Cyril [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Japanese French Laboratory for Informatics [JFLI]
Ahiod, Belaïd [Auteur]
Laboratoire de Recherche Informatique et Télécommunications [LRIT]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Fonlupt, Cyril [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Japanese French Laboratory for Informatics [JFLI]
Ahiod, Belaïd [Auteur]
Laboratoire de Recherche Informatique et Télécommunications [LRIT]
Titre de la manifestation scientifique :
PPSN 2020 - The 16th International Conference on Parallel Problem Solving from Nature
Ville :
Leiden
Pays :
Pays-Bas
Date de début de la manifestation scientifique :
2020-09-05
Titre de l’ouvrage :
Parallel problem Solving fron Nature - PPSN XVI : 16th International Conference, PPSN 2020 Leiden, The Netherlands, September 5-9, 2020. Proccedings, Part II
Titre de la revue :
Lecture Notes in Computer Science
Éditeur :
Springer
Date de publication :
2020-09-02
Mot(s)-clé(s) en anglais :
Combinatorial optimization
Local optimality
Fitness lanscape
Stochastic search operators
Local optimality
Fitness lanscape
Stochastic search operators
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
Fitness landscape analysis is a well-established tool for gaining insights about optimization problems and informing about the behavior of local and evolutionary search algorithms. In the conventional definition of a fitness ...
Lire la suite >Fitness landscape analysis is a well-established tool for gaining insights about optimization problems and informing about the behavior of local and evolutionary search algorithms. In the conventional definition of a fitness landscape, the neighborhood of a given solution is a set containing nearby solutions whose distance is below a threshold, or that are reachable using a deterministic local search operator. In this paper, we generalize this definition in order to analyze the induced fitness landscape for stochastic search operators, that is when neighboring solutions are reachable under different probabilities. More particularly, we give the definition of a stochastic local optimum under this setting, in terms of a probability to reach strictly improving solutions. We illustrate the relevance of stochastic fitness landscapes for enumerable combinatorial benchmark problems, and we empirically analyze their properties for different stochastic operators, neighborhood sample sizes, and local optimality thresholds. We also portray their differences through stochastic local optima networks, intending to gather a better understanding of fitness landscapes under stochastic search operators.Lire moins >
Lire la suite >Fitness landscape analysis is a well-established tool for gaining insights about optimization problems and informing about the behavior of local and evolutionary search algorithms. In the conventional definition of a fitness landscape, the neighborhood of a given solution is a set containing nearby solutions whose distance is below a threshold, or that are reachable using a deterministic local search operator. In this paper, we generalize this definition in order to analyze the induced fitness landscape for stochastic search operators, that is when neighboring solutions are reachable under different probabilities. More particularly, we give the definition of a stochastic local optimum under this setting, in terms of a probability to reach strictly improving solutions. We illustrate the relevance of stochastic fitness landscapes for enumerable combinatorial benchmark problems, and we empirically analyze their properties for different stochastic operators, neighborhood sample sizes, and local optimality thresholds. We also portray their differences through stochastic local optima networks, intending to gather a better understanding of fitness landscapes under stochastic search operators.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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