摘要
The only metric that had existed so far to determine the best algorithm for solving an general Asymmetric Traveling Salesman Problem (ATSP) instance is based on the number of cities; nevertheless, it is not sufficiently adequate for discriminating the best algorithm for solving an ATSP instance, thus the necessity for devising a new metric through the use of data-mining techniques. In this paper we propose: (1) the use of a genetic distance metric for improving the selection of the algorithms that best solve a given instance of the ATSP and (2) the use of discriminant analysis as a means for predictive learning (data-mining techniques) aiming at selecting meta-heuristic algorithms.
- 出版日期2010