Friday, May 05, 2006


Statistics and genetic algorithms

Classes ended Wednesday, and I am just getting my head screwed on straight again. They say you can't teach an old dog new tricks, but I just taught GE 331/IE 300 for the first time. The course is 1/3 probability and 2/3 statistics for engineers. Enjoyed the material, but doing a first-time pass at the material in front of 130+ students was a bit like doing a high wire act without a net after 2 weeks of practice.

In going through a fairly standard engineering probability and statistics curriculum, I have three comments. First, I agree (to some extent) with some critics of our field that we could as a group do a better job in using fairly standard statistical methods to present our findings. Fair enough. Second, it seems to me that the similarities and differences between GA/EC practice and the practice of design of experiments, ANOVA, and model building deserve closer scrutiny and articulation. The practice of estimation of distribution algorithms (EDAs) might benefit from paying attention to modern statistics and vice versa. Third, it seems to me that statistics (like most fields derived from mathematics) is concerned with rigor to the exclusion of economy. An engineering discipline of statistics would explicitly account for and permit relaxation of inferential rigor on economic grounds. Regular readers of this blog will recognize this as the economy of thought (see here) argument made in The Design of Innovation.

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