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TCAD
2010
121views more  TCAD 2010»
13 years 2 months ago
Translation Validation of High-Level Synthesis
The growing complexity of systems and their implementation into silicon encourages designers to look for model designs at higher levels of abstraction and then incrementally build ...
Sudipta Kundu, Sorin Lerner, Rajesh K. Gupta
GECCO
2005
Springer
189views Optimization» more  GECCO 2005»
14 years 28 days ago
Molecular programming: evolving genetic programs in a test tube
We present a molecular computing algorithm for evolving DNA-encoded genetic programs in a test tube. The use of synthetic DNA molecules combined with biochemical techniques for va...
Byoung-Tak Zhang, Ha-Young Jang
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
ASPDAC
2006
ACM
148views Hardware» more  ASPDAC 2006»
13 years 11 months ago
TAPHS: thermal-aware unified physical-level and high-level synthesis
Thermal effects are becoming increasingly important during integrated circuit design. Thermal characteristics influence reliability, power consumption, cooling costs, and performan...
Zhenyu (Peter) Gu, Yonghong Yang, Jia Wang, Robert...
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
14 years 28 days ago
Preventing overfitting in GP with canary functions
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...
Nate Foreman, Matthew P. Evett