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AAAI
1998
15 years 1 months ago
Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems.STAGElearns an evaluation function which predicts the outcom...
Justin A. Boyan, Andrew W. Moore
87
Voted
GECCO
2010
Springer
158views Optimization» more  GECCO 2010»
15 years 4 months ago
A genetic algorithm to improve linux kernel performance on resource-constrained devices
As computers become increasingly mobile, users demand more functionality, longer battery-life, and better performance from mobile devices. In response, chipset fabricators are foc...
James Kukunas, Robert D. Cupper, Gregory M. Kapfha...
GECCO
2005
Springer
160views Optimization» more  GECCO 2005»
15 years 5 months ago
Designing resilient networks using a hybrid genetic algorithm approach
As high-speed networks have proliferated across the globe, their topologies have become sparser due to the increased capacity of communication media and cost considerations. Relia...
Abdullah Konak, Alice E. Smith
EUROGP
2001
Springer
124views Optimization» more  EUROGP 2001»
15 years 4 months ago
An Evolutionary Approach to Automatic Generation of VHDL Code for Low-Power Digital Filters
An evolutionary algorithm is used to design a finite impulse response digital filter with reduced power consumption. The proposed design approach combines genetic optimization an...
Massimiliano Erba, Roberto Rossi, Valentino Libera...
GECCO
2006
Springer
150views Optimization» more  GECCO 2006»
15 years 3 months ago
Nonlinear parametric regression in genetic programming
Genetic programming has been considered a promising approach for function approximation since it is possible to optimize both the functional form and the coefficients. However, it...
Yung-Keun Kwon, Sung-Soon Choi, Byung Ro Moon