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.

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PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, 2012. Tech Report AI12-11.

@phdthesis{lockett:phd12, title={General-Purpose Optimization Through Information-Maximization}, author={Alan J Lockett}, school={Department of Computer Sciences, The University of Texas at Austin}, institution={Department of Computer Sciences, The University of Texas at Austin}, note={Tech Report AI12-11}, url="http://nn.cs.utexas.edu/?lockett:phd12", year={2012} }