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TEC
2008
98views more  TEC 2008»
15 years 6 months ago
Opposition-Based Differential Evolution
Evolutionary Algorithms (EAs) are well-known optimization approaches to cope with non-linear, complex problems. These population-based algorithms, however, suffer from a general we...
Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A...
MIDDLEWARE
2007
Springer
16 years 20 days ago
Domain-based context management for dynamic and evolutionary environments
Research in context-aware computing has produced a number of application prototypes, frameworks, middlewares and models for describing context. However, development of ubiquitous ...
Ricardo Couto Antunes da Rocha, Markus Endler
ICAC
2009
IEEE
16 years 1 months ago
Applying genetic algorithms to decision making in autonomic computing systems
Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means...
Andres J. Ramirez, David B. Knoester, Betty H. C. ...
IPPS
2003
IEEE
15 years 11 months ago
Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications
Performance obtained with existing library-based parallelization tools for implementing high performance image processing applications is often sub-optimal. This is because inter-...
Frank J. Seinstra, Dennis Koelma
CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 6 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná