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Letters to the editor

Use the Scientific Method in Computer Science

By CACM Staff
Communications of the ACM, February 2017, Vol. 60 No. 2, Pages 8-9
10.1145/3032965
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In "Sex as an Algorithm: The Theory of Evolution Under the Lens of Computation" (Nov. 2016), Adi Livnat and Christos Papadimitriou argued eloquently that the extraordinary success of sexual evolution has not been adequately explained. Somewhat paradoxically, they concluded that sex is not particularly well suited to the task of generating "outstanding individuals." They also said that genetic algorithms are similarly ill suited to this task.

It should be noted that this critique of genetic algorithms—widely used derivative free optimization heuristics modeled on recombinative evolution—stands in counterpoint to a voluminous empirical record of practical successes. It also speaks to the long-standing absence of consensus among evolutionary computation theorists regarding the abstract workings of genetic algorithms and the general conditions under which genetic algorithms outperform local search. A consensus on these matters promises to shed light on the question the authors originally aimed to answer: Why does recombinative evolution generate populations with outstanding individuals?

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