U
Una-May O'Reilly
Researcher at Massachusetts Institute of Technology
Publications - 236
Citations - 7706
Una-May O'Reilly is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Genetic programming & Evolutionary algorithm. The author has an hindex of 36, co-authored 236 publications receiving 6933 citations. Previous affiliations of Una-May O'Reilly include Santa Fe Institute & Association for Computing Machinery.
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Journal ArticleDOI
Genetic Programming II: Automatic Discovery of Reusable Programs.
TL;DR: This book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities.
BookDOI
Genetic and Evolutionary Computation -- GECCO-2003
Erick Cantú-Paz,James A. Foster,Kalyanmoy Deb,Lawrence Davis,Rajkumar Roy,Una-May O'Reilly,Hans-Georg Beyer,Russell Standish,Graham Kendall,Stewart W. Wilson,Mark Harman,Joachim Wegener,Dipankar Dasgupta,Mitch A. Potter,Alan C. Schultz,Kathryn A. Dowsland,Natasha Jonoska,Julian F. Miller +17 more
TL;DR: This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity, and suggests that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.
Proceedings ArticleDOI
OpenTuner: an extensible framework for program autotuning
Jason Ansel,Shoaib Kamil,Kalyan Veeramachaneni,Jonathan Ragan-Kelley,Jeffrey Bosboom,Una-May O'Reilly,Saman Amarasinghe +6 more
TL;DR: The efficacy and generality of OpenTuner are demonstrated by building autotuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8χ with little programmer effort.
Journal ArticleDOI
Meta optimization: improving compiler heuristics with machine learning
TL;DR: By evolving a compiler's heuristic over several benchmarks, Meta Optimization can create effective, general-purpose heuristics, and demonstrates the efficacy of the techniques on three different optimizations in this paper: hyperblock formation, register allocation, and data prefetching.
Proceedings ArticleDOI
Genetic programming needs better benchmarks
James McDermott,David White,Sean Luke,Luca Manzoni,Mauro Castelli,Leonardo Vanneschi,Wojciech Jaskowski,Krzysztof Krawiec,Robin Harper,Kenneth de Jong,Una-May O'Reilly +10 more
TL;DR: This paper argues that the definition of standard benchmarks is an essential step in the maturation of the field and motivates the development of a benchmark suite and defines its goals.