An efficient evolutionary algorithm for solving incrementally structured problems
Name
Amarasinghe-An Efficient Evolutionary.pdf
Size
435.21 KB
Format
Adobe PDF
Checksum (MD5)
530a47cec29e8977083d162beec3964e
Author(s)
Ansel, Jason Andrew
Pacula, Maciej
Amarasinghe, Saman P.
O'Reilly, Una-May
Date Issued
July 2011
Journal
Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO' 11)
Publisher
Association for Computing Machinery (ACM)
Citation
Ansel, Jason et al. “An efficient evolutionary algorithm for solving incrementally structured problems.” Proceedings of the 13th annual conference on Genetic and evolutionary computation . ACM Press, 2011. 1699. © 2011 ACM
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2001576.2001805
