General-purpose optimization through information maximization

General-purpose optimization through information maximization

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...

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Bibliographic Details
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

Internet

http://hdl.handle.net/2152/ETD-UT-2012-05-5459

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