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» Typical properties of winners and losers in discrete optimiz...
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STOC
2004
ACM
150views Algorithms» more  STOC 2004»
16 years 8 hour ago
Typical properties of winners and losers in discrete optimization
We present a probabilistic analysis for a large class of combinatorial optimization problems containing, e.g., all binary optimization problems defined by linear constraints and a...
René Beier, Berthold Vöcking
SASO
2009
IEEE
15 years 6 months ago
Controlling Particle Swarm Optimization with Learned Parameters
—Controlling particle swarm optimization is typically an unintuitive task, involving a process of adjusting low-level parameters of the system that often do not have obvious corr...
Kevin Winner, Don Miner, Marie desJardins
SASO
2009
IEEE
15 years 6 months ago
Self-Organization of Patrolling-Ant Algorithms
—We consider here multi-agent patrolling as the task for a group of agents to repeatedly visit all the cells of a discrete environment. Wagner et al. [1] have introduced patrolli...
Arnaud Glad, Olivier Buffet, Olivier Simonin, Fran...
GECCO
2003
Springer
123views Optimization» more  GECCO 2003»
15 years 5 months ago
Benefits of Implicit Redundant Genetic Algorithms for Structural Damage Detection in Noisy Environments
A robust structural damage detection method that can handle noisy frequency response function information is discussed. The inherent unstructured nature of damage detection problem...
Anne M. Raich, Tamás Liszkai