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» Dynamic Control of Genetic Algorithms in a Noisy Environment
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77
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GECCO
2008
Springer
133views Optimization» more  GECCO 2008»
14 years 10 months ago
Using feature-based fitness evaluation in symbolic regression with added noise
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the depe...
Janine H. Imada, Brian J. Ross
90
Voted
SBRN
2008
IEEE
15 years 3 months ago
Imitation Learning of an Intelligent Navigation System for Mobile Robots Using Reservoir Computing
The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which mus...
Eric A. Antonelo, Benjamin Schrauwen, Dirk Strooba...
GECCO
2008
Springer
116views Optimization» more  GECCO 2008»
14 years 10 months ago
Stock trading strategies by genetic network programming with flag nodes
Genetic Network Programming (GNP) has been proposed as a graph-based evolutionary algorithm. GNP works well especially in dynamic environments due to its graph structures. In addi...
Shingo Mabu, Yan Chen, Etsushi Ohkawa, Kotaro Hira...
161
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FAST
2011
14 years 1 months ago
The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements
Elasticity of cloud computing environments provides an economic incentive for automatic resource allocation of stateful systems running in the cloud. However, these systems have t...
Beth Trushkowsky, Peter Bodík, Armando Fox,...
CDC
2008
IEEE
107views Control Systems» more  CDC 2008»
15 years 4 months ago
Locally optimal decomposition for autonomous obstacle avoidance with the Tunnel-MILP algorithm
— The Tunnel-MILP algorithm is a three stage path planning method for 2-D environments that relies on the identification of a sequence of convex polygons to form an obstacle fre...
Michael P. Vitus, Steven Lake Waslander, Claire J....