Browsing by Subject "Neural network controllers"

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    General-purpose optimization through information maximization
    (2012-05) Lockett, Alan Justin; Miikkulainen, Risto; Ghosh, Joydeep; Mooney, Raymond; Ravikumar, Pradeep; Zitkovic, Gordan
    The primary goal of artificial intelligence research is to develop a machine capable of learning to solve disparate real-world tasks autonomously, without relying on specialized problem-specific inputs. This dissertation suggests that such machines are realistic: If No Free Lunch theorems were to apply to all real-world problems, then the world would be utterly unpredictable. In response, the dissertation proposes the information-maximization principle, which claims that the optimal optimization methods make the best use of the information available to them. This principle results in a new algorithm, evolutionary annealing, which is shown to perform well especially in challenging problems with irregular structure.