An automatic selection method and its application to key search algorithms

Abstract

This paper proposes an automatic selection method for key search algorithms. The proposed methodology has been implemented in the KEALSE (KEy-search ALgorithm SElection) system. Key search algorithms are selected according to user's requirements through interaction with KEALSE, which bases its inferences on an evaluation table. The evaluation table contains values to estimate the performance of each key search algorithm for different searching properties. A selection algorithm for inferencing is based on a step-by-step reduction of key search algorithms and searching properties. This paper also proposes assistance facilities that consist of both a support function and a program synthesis function. Experimental results show that appropriate key search algorithms are effectively selected, and the number of questions asked to select the appropriate algorithm is reduced to less than half of the total number of possible questions. The support function is useful for the user during selection, and the program synthesis function fully translates a selected key search algorithm into high-level language in an average time of less than 1 hour.