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» Evolutionary multiobjective optimization
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PLDI
2003
ACM
15 years 6 months ago
Meta optimization: improving compiler heuristics with machine learning
Compiler writers have crafted many heuristics over the years to approximately solve NP-hard problems efficiently. Finding a heuristic that performs well on a broad range of applic...
Mark Stephenson, Saman P. Amarasinghe, Martin C. M...
CEC
2009
IEEE
15 years 6 months ago
Memetic algorithm with Local search chaining for large scale continuous optimization problems
Abstract— Memetic algorithms arise as very effective algorithms to obtain reliable and high accurate solutions for complex continuous optimization problems. Nowadays, high dimens...
Daniel Molina, Manuel Lozano, Francisco Herrera
GECCO
2008
Springer
183views Optimization» more  GECCO 2008»
15 years 2 months ago
UMDAs for dynamic optimization problems
This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designe...
Carlos M. Fernandes, Cláudio F. Lima, Agost...
102
Voted
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
15 years 7 months ago
Experimental analysis of binary differential evolution in dynamic environments
Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems h...
Alp Emre Kanlikilicer, Ali Keles, Sima Uyar
ICML
1998
IEEE
16 years 2 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...