General-purpose optimization through information maximization

• Main Author: Lockett, Alan Justin
• Format: Others
• Language: English
• Published: 2012
• Subjects: 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
• Online Access: http://hdl.handle.net/2152/ETD-UT-2012-05-5459

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. === text