ment euclidiennes des quations dEinstein en dimensions suprieures, cf.2, 5. Performance evaluations show that the additional mechanisms enable XCSF to reliably, accurately, and compactly approximate even seven dimensional functions. 1.1 Rsultat principal et esquisse de la preuve. The resulting compaction mechanism can boil the population size down by 90% on average, while decreasing prediction accuracy only marginally. Additionally, this paper introduces a novel closest classifier matching mechanism for the efficient compaction of XCS's final problem solution. This paper shows that XCSF performance on function approximation problems additively benefits from: 1) improved representations 2) improved genetic operators and 3) improved approximation techniques. In function approximation problems, the XCSF classifier system develops a problem solution in the form of overlapping, piecewise linear approximations. Together, the two components develop a distributed, locally optimized problem solution in the form of a population of expert rules, often called classifiers. The genetic optimization technique is designed to distribute these local approximations efficiently over the problem space. The chosen approximation technique develops locally optimal approximations, such as accurate classification estimates, Q-value predictions, or linear function approximations. Īn important strength of learning classifier systems (LCSs) lies in the combination of genetic optimization techniques with gradient-based approximation techniques. neither ZCS nor XCS) Should be revisited from ensembles perspective Other representations for conditions VL 1 logic as used in GIL First-order logic Decision lists used in GABIL and GAssist Messy encoding Ellipses and hyperellipses Hyperspheres Convex hulls Hyperplane coding Tile coding GP trees GP to define Boolean networks Support vectors Edges of an Augmented Transition Network Other representations for conditions and actions Gene Expression Programming Fuzzy rules (see ) Neural networks Evolved prototypes One of the representations used with GALE Evolve prototypes, use k-nearest-neighbour for classification Prototypes need not be fully specified Also used in GAssist Decision trees Another of GALE's representations Uses GP to evolve trees defining axis-parallel and oblique hyper-rectangles Computed actions Continuous actions. Not much interest since early 1990s (but see ) Not all Michigan LCS support them (e.g.

Preuve hyperplan dimension