—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...
The success of evolutionary algorithms (EAs) depends crucially on finding suitable parameter settings. Doing this by hand is a very time consuming job without the guarantee to ...
In this paper, we take a holistic approach to the protocol architecture design in multihop wireless networks. Our goal is to integrate various protocol layers into a rigorous fram...
A genome rearrangement scenario describes a series of chromosome fusion, fission, and translocation operations that suffice to rewrite one genome into another. Exact algorithmic ...
—Topology control problems are concerned with the assignment of power levels to the nodes of an ad-hoc network so as to maintain a specified network topology while minimizing th...