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In the same 1950 article in which Alan Turing described his “imitation game” test for artificial intelligence, he also described ways in which ideas from evolutionary biology might help us to develop AI. It took time for these ideas to be refined, and it took advances in computing infrastructure for them to bear fruit, but now “evolutionary computation” methods are solving scientific and engineering problems that are beyond the reach of other forms of AI.

In this talk, Spector will introduce the general concepts of evolutionary computation and illustrate some of its applications. He will also describe a contribution to the field that he and his students have recently made, demonstrating that the speed and success of adaptation can be boosted by using random sequences of challenges, rather than overall performance, as the basis for parent selection in evolving populations. This approach increases the problem-solving power of evolutionary computation, and it also raises broader questions about the role of specialists in communities and in evolution.

Bio: Lee Spector is a visiting professor of computer science at Amherst College, a professor of computer science at Hampshire College, and an adjunct professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He received a B.A. in philosophy from Oberlin College, a Ph.D. in computer science from the University of Maryland, College Park, and the highest honor bestowed by the National Science Foundation for excellence in both teaching and research, the NSF Director’s Award for Distinguished Teaching Scholars. His areas of teaching and research include evolutionary computation; quantum computation; and intersections of computer science, cognitive science and the arts. He is the editor-in-chief of the journal Genetic Programming and Evolvable Machines (published by Springer).

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