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
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2012-05-5459 |
| Date | 05 July 2012 |
| Creators | Lockett, Alan Justin |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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