Above are papers related to the following paper: [Paper_ID] Paper #594483. 	[Title] On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control [Abstract] Fuzzy logic and evolutionary computation have proven to be convenient tools for handling real-world uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms for the purpose of developing intelligent control systems. The potential of the genetic programming paradigm (GP) for learning rules for use in fuzzy logic controllers (FLCs) is evaluated by focussing on the problem of discovering a controller for mobile robot path tracking. Performance results of incomplete rule-bases compare favorably to those of a complete FLC designed by the usual trial-and-error approach. A constrained syntactic representation supported by structure-preserving genetic operators is also introduced. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129040. 	[Title] Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-behavior modulation and evolution [Abstract] Realization of autonomous behavior in mobile robots, using fuzzy logic control, requires formulation of rules which are collectively responsible for necessary levels of intelligence. Such a collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This article describes how this can be done using a behavior-based architecture. A behavior hierarchy and mechanisms of control decision-making are described. In addition, an approach to behavior coordination is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavior for sensor-based goal-seeking. The usefulness of the behavior hierarchy, and partial design by GP, is evident in performance results of simulated autonomous navigation. Please summarise all the given information.

Paper #594483 has the title `On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control  
` and the abstract `Fuzzy logic and evolutionary computation have proven to be convenient tools for handling real-world uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms for the purpose of developing intelligent control systems. The potential of the genetic programming paradigm (GP) for learning rules for use in fuzzy logic controllers (FLCs) is evaluated by focussing on the problem of discovering a controller for mobile robot path tracking. Performance results of incomplete rule-bases compare favorably to those of a complete FLC designed by the usual trial-and-error approach. A constrained syntactic representation supported by structure-preserving genetic operators is also introduced. 
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Question: Which category should Paper #594483 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #594483 has the title `On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control  
` and the abstract `Fuzzy logic and evolutionary computation have proven to be convenient tools for handling real-world uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms for the purpose of developing intelligent control systems. The potential of the genetic programming paradigm (GP) for learning rules for use in fuzzy logic controllers (FLCs) is evaluated by focussing on the problem of discovering a controller for mobile robot path tracking. Performance results of incomplete rule-bases compare favorably to those of a complete FLC designed by the usual trial-and-error approach. A constrained syntactic representation supported by structure-preserving genetic operators is also introduced. 
`.
Question: Which category should Paper #594483 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms