Computer Science – Neural and Evolutionary Computing
Scientific paper
2005-05-25
Computer Science
Neural and Evolutionary Computing
Genetic and Evolutionary Computation Conference (GECCO), Part I, 2004: 238-250 (LNCS 3102)
Scientific paper
A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both of the macro rules by simple combination and subsymbolic deploying of multiple rules among them are also studied. Experimental results on benchmark problems are presented, and performance comparison between SWAF and other existing algorithms indicates that it is efficiently.
Xie Xiao-Feng
Zhang Wen-Jun
No associations
LandOfFree
SWAF: Swarm Algorithm Framework for Numerical Optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with SWAF: Swarm Algorithm Framework for Numerical Optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and SWAF: Swarm Algorithm Framework for Numerical Optimization will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-641730