Bansal A, Cannings C, Sheehan N
School of Mathematics and Statistics, University of Sheffield, UK.
IMA J Math Appl Med Biol. 1997 Sep;14(3):161-87.
We consider the problem of ordering detectable genetic loci along a chromosome by minimizing the number of obligatory breaks that can be inferred from radiation hybrid data. The problem bears some resemblance to the travelling-salesman problem, for which genetic algorithms have been used with considerable success. We find that the results from other studies on closely related problems are not directly transferable, and although we did find a genetic algorithm that performed well in this application it would appear that this algorithm is highly sensitive to any changes in the problem. Moreover, a very simple stochastic algorithm performed almost as well as our much more complicated and computer-intensive genetic algorithm and it did so in a fraction of the time. While we do not dispute that genetic algorithms can work on large complicated problems, the various modifications and fine-tuning necessary for good performance tend to be highly problem specific and they are often only arrived at after an exhaustive exploration of possibilities. Thus, we would dispute any claim that genetic algorithms are robust in their form and range of applicability.
我们考虑通过最小化从辐射杂交数据中推断出的强制断点数量来对染色体上可检测的基因座进行排序的问题。该问题与旅行商问题有一些相似之处，遗传算法已成功应用于旅行商问题。我们发现，其他关于密切相关问题的研究结果不能直接转移，虽然我们确实找到了一种在该应用中表现良好的遗传算法，但这种算法似乎对问题的任何变化都高度敏感。此外，一种非常简单的随机算法的表现几乎与我们更为复杂且计算量大的遗传算法一样好，而且它所用时间仅为遗传算法的一小部分。虽然我们并不否认遗传算法可以处理大型复杂问题，但良好性能所需的各种修改和微调往往高度依赖于具体问题，而且通常只有在对各种可能性进行详尽探索之后才能得出。因此，我们会质疑任何声称遗传算法在其形式和适用范围上具有鲁棒性的说法。
