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» Multiobjective optimization for dynamic environments
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CEC
2007
IEEE
15 years 4 months ago
Improving generalization capability of neural networks based on simulated annealing
— This paper presents a single-objective and a multiobjective stochastic optimization algorithms for global training of neural networks based on simulated annealing. The algorith...
Yeejin Lee, Jong-Seok Lee, Sun-Young Lee, Cheol Ho...
ICCAD
2010
IEEE
166views Hardware» more  ICCAD 2010»
14 years 7 months ago
Low-power clock trees for CPUs
Clock networks contribute a significant fraction of dynamic power and can be a limiting factor in high-performance CPUs and SoCs. The need for multi-objective optimization over a l...
Dongjin Lee, Myung-Chul Kim, Igor L. Markov
EUROPAR
2007
Springer
15 years 1 months ago
Optimizing Chip Multiprocessor Work Distribution Using Dynamic Compilation
How can sequential applications benefit from the ubiquitous next generation of chip multiprocessors (CMP)? Part of the answer may be a dynamic execution environment that automatica...
Jisheng Zhao, Matthew Horsnell, Ian Rogers, Andrew...
CONNECTION
2004
94views more  CONNECTION 2004»
14 years 9 months ago
Evolving internal memory for T-maze tasks in noisy environments
In autonomous agent systems, internal memory can be an important element to overcome the limitations of purely reactive agent behaviour. This paper presents an analysis of memory r...
DaeEun Kim
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
15 years 4 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