General-Purpose Optimization Through Information-Maximization (2012)
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.
PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, 2012. Tech Report AI12-11.

Alan J. Lockett Ph.D. Alumni alan lockett [at] gmail com
PyEC Python package containing source code for Evolutionary Annealing along with a number of other evolutionary and stochasti... 2011