Abstract
In this paper, we present the design of a system for automatically learning and evaluating new heuristic methods that can be used to map a set of communicating processes on a network of computers. Our learning system is based on testing a population of competing heuristic methods within a fixed time constraint. We develop and analyze various resource scheduling strategies based on a statistical model that trades between the number of new heuristic methods considered and the amount of testing performed on each. We implement a prototype learning system (TEACHER 4.1) for learning new heuristic methods used in post-game analysis, a system that iteratively generates and refines mappings of a set of communicating processes on a network of computers. Our performance results show that a significant improvement can be obtained by a systematic exploration of the space of possible heuristic methods.
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