Effective use of memory in iterative deepening search

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Abstract

The Iterative Deepening A (IDA) (R.E. Korf, Artificial Intelligence 27 (1985)) algorithm often reexpands too many nodes while solving certain combinatorial problems. Algorithm IDA_CR (U.K. Sarkar, Artificial Intelligence 50 (1991)) attempted to remedy this drawback. These algorithms require very little memory although much more is available in practice. This paper introduces an algorithm IDA_CRM which shows how the available memory can be advantageously utilized in IDAast;_CR in order to reduce the number of expanded nodes. IDA_CRAM, an approximation scheme derived from IDA_CRM, is also presented. Computational results are shown for the Flow-Shop Scheduling Problem.

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