Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Fine-grained program power behavior is useful in both evaluating power optimizations and observing power optimization opportunities. Detailed power simulation is time consuming and...
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms ha...
Several features such as reconfiguration, voltage and frequency scaling, low-power operating states, duty-cycling, etc. are exploited for latency and energy efficient application ...
After an outline of the history of evolutionary algorithms, a new ( ) variant of the evolution strategies is introduced formally. Though not comprising all degrees of freedom, it i...