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|a Ansel, Jason Andrew
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Amarasinghe, Saman P.
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|a Ansel, Jason Andrew
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|a Pacula, Maciej
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|a Amarasinghe, Saman P.
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|a O'Reilly, Una-May
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|a Pacula, Maciej
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|a Amarasinghe, Saman P.
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|a O'Reilly, Una-May
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|a An efficient evolutionary algorithm for solving incrementally structured problems
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|b Association for Computing Machinery (ACM),
|c 2012-09-24T19:29:04Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/73133
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|a Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn't appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.
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|a United States. Dept. of Energy (award DESC0005288)
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|a en_US
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|a Article
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|t Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO' 11)
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