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<p>A learning model for designing heuristics automatically under resource constraints is studied. The focus is on improving performance-related heuristic methods (HMs) in knowledge-lean application domains. It is assumed that learning is episodic, that the performance measures of an episode are dependent only on the final state reached in evaluating the corresponding test case, and that the aggregate performance measures of the HMs involved are independent of the order of evaluation of test cases. The learning model is based on testing a population of competing HMs for an application problem, and switches from one to another dynamically, depending on the outcome of previous tests. Its goal is to find a good HM within the resource constraints, with proper tradeoff between cost and quality. It extends existing work on classifier systems by addressing issues related to delays in feedback, scheduling of tests of HMs under limited resources, anomalies in performance evaluation, and scalability of HMs. Experience in applying the learning method is described.</p>
population based learning; feedback delays; test scheduling; learning from examples; resource constraints; performance-related heuristic methods; knowledge-lean application; performance measures; cost; quality; classifier systems; limited resources; performance evaluation; scalability; learning by example

B. Wah, "Population-Based Learning: A Method for Learning from Examples Under Resource Constraints," in IEEE Transactions on Knowledge & Data Engineering, vol. 4, no. , pp. 454-474, 1992.
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