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Title General-purpose optimization through information maximization
Publication Date
Date Accessioned
Discipline/Department Computer Sciences
University/Publisher University of Texas – Austin
Abstract 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.
Subjects/Keywords Optimization; General-purpose learning; Martingale optimization; Artificial intelligence; Evolutionary computation; Genetic algorithms; Simulated annealing; Evolutionary annealing; Neuroannealing; Neural networks; Neural network controllers; Neuroevolution; Differential evolution; No Free Lunch theorems; NFL Identification Theorem; Population-based stochastic optimization; Iterative optimization; Optimal optimization; Information-maximization principle; Convex control; Algorithm selection
Contributors Miikkulainen, Risto (advisor); Ghosh, Joydeep (committee member); Mooney, Raymond (committee member); Ravikumar, Pradeep (committee member); Zitkovic, Gordan (committee member)
Language en
Country of Publication us
Record ID handle:2152/ETD-UT-2012-05-5459
Repository texas
Date Retrieved
Date Indexed 2018-10-22
Note [] text; [department] Computer Sciences;

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