Summary
In this chapter, we present new results on the automated generalization of performance-related heuristics learned for knowledge-lean applications. By first applying genetics-based learning to learn new heuristics for some small subsets of test cases in a problem space, we study methods to generalize these heuristics to unlearned subdomains of test cases. Our method uses a new statistical metric called probability of win. By assessing the performance of heuristics in a range-independent and distribution-independent manner, we can compare heuristics across problem subdomains in a consistent manner. To illustrate our approach, we show experimental results on generalizing heuristics learned for sequential circuit testing, VLSI cell placement and routing, branch-and-bound search, and blind equalization. We show that generalization can lead to new and robust heuristics that perform better than the original heuristics across test cases of different characteristics.
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© 1997 Springer-Verlag Berlin Heidelberg
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Ieumwananonthachai, A., Wah, B.W. (1997). Statistical Generalization of Performance-Related Heuristics for Knowledge-Lean Applications. In: Dasgupta, D., Michalewicz, Z. (eds) Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03423-1_17
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DOI: https://doi.org/10.1007/978-3-662-03423-1_17
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