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, Paramétrage'automatisé'd'algorithme'par'instance'pour'l'optimisation'boîte'noire' Mot'clés':'intelligence'artificielle,'optimisation,'apprentissage'statistique
, Cette' thèse' porte' sur' la' configuration' automatisée'des'algorithmes'qui'vise'à'trouver'le'meilleur' paramétrage' à' un' problème' donné' ou' une' catégorie' de' problèmes
, configuration'de'l'algorithme'revient'donc' à' un' problème' de' métaFoptimisation' dans' l'espace' des' paramètres,' dont' le' métaFobjectif' est' la' mesure' de' performance'de'l'algorithme'donné'avec'une'configuration' de'paramètres'donnée
, CAPI' (Configuration' d'Algorithme' Par' Instance)' pour' résoudre' des' problèmes' d'optimisation'de'boîte'noire'continus,'où'seul'un'budget' limité'd'évaluations'de'fonctions'est'disponible.' Nous' étudions' d'abord' les' algorithmes' évolutionnaires' pour'l'optimisation'continue,'en'mettant'l'accent'sur'deux' algorithmes' que' nous' avons' utilisés' comme' algorithme' cible'pour'CAPI,'DE'et'CMAFES
, générale' pour'étudier'empiriquement'le'CAPI'pour'le'domaine' continu,'de'sorte'que'toutes'les'composantes'du'CAPI' puissent'être'explorées'dans'des'conditions'réelles.' À'cette'fin,'nous'introduisons'également'un'nouveau' banc' d'essai' de' boîte' noire' continue,' distinct' du' célèbre' benchmark' BBOB,' qui' est' composé' de' plusieurs'fonctions'de'test'multidimensionnelles'avec' différentes' propriétés' problématiques,' issues' de' la' littérature.' La'méthodologie'proposée'est'finalement'appliquée'à' deux' AEs.' LA' méthodologie' est' ainsi,' validé' empiriquement' sur' le' nouveau' banc' d'essai' d'optimisation'boîte'noire'pour'des'dimensions'allant' jusqu'à'100 Title:'Per'Instance'Algorithm'Configuration'for'Continuous'Black'Box'Optimization' Keywords:'Artificial'intelligence,'Optimization,'Machine'Learning' ' Abstract:' This' PhD' thesis' focuses' on' the' automated' algorithm' configuration' that' aims' at' finding' the' 'best'' parameter' setting' for' a' given' problem' or' a' class' of' problem.' The' Algorithm' Configuration' problem' thus' amounts' to' a' metaFoptimization' problem' in' the' space' of' parameters,' whose'metaFobjective'is'the'performance'measure'of'the' given' algorithm' at' hand' with' a' given' parameter' configuration.' However,' in' the' continuous' domain,' such' method' can' only' be' empirically' assessed' at' the' cost' of' running''the'algorithm'on'some'problem'instances.' More'recent'approaches''rely'on'a'description'of'problems' in' some' features' space,' and' try' to' learn' a' mapping' from' this' feature' space' onto' the' space' of' parameter' configurations'of'the'algorithm'at'hand.'Along'these'lines,' this' PhD' thesis' focuses' on' the' Per' Instance' Algorithm' Configuration' (PIAC)' for' solving' continuous' black' box' optimization' problems,' where' ' only' a' limited' budget' of' function'evaluations''is'available.' 'We' first' survey' Evolutionary' Algorithms' for' continuous' optimization,'with'a'focus'on'two'algorithms'that'we'have' used'as'target'algorithm'for'PIAC,'DE'and'CMAFES.' Next,' we' review' the' state' of' the' art' of' Algorithm' Configuration' approaches,' and' the' different' features' that'have'been'proposed'in'the'literature'to'describe' continuous'black'box'optimization'problems.' We' then' introduce' a' general' methodology' to' empirically'study'PIAC'for'the'continuous'domain,'so' that' all' the' components' of' PIAC' can' be' explored' in' realFworld'conditions.'' To'this'end,''we'also'introduce'a'new'continuous'black' box' test' bench,' distinct' from' the' famous' BBOB' benchmark,' that' is' composed' of' a' several' multiF dimensional' test' functions' with' different' problem' properties,'gathered'from'the'literature.'' The'methodology'is'finally'applied'to'two'EAs.' First'we'use'Differential'Evolution'as'target'algorithm,' and'explore'all'the'components'of'PIAC,'such'that'we' empirically' assess' the' best.' Second,' based' on' the' results' on' DE,' we' empirically' investigate' PIAC' with' Covariance' Matrix' Adaptation' Evolution' Strategy' (CMAFES)' as' target' algorithm.' Both' use' cases' empirically'validate'the'proposed'methodology'on'the' new'black'box'testbench'for'dimensions'up'to'100