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CIA
2006
Springer
15 years 10 months ago
Learning to Negotiate Optimally in Non-stationary Environments
Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
Vidya Narayanan, Nicholas R. Jennings
EVOW
2008
Springer
15 years 8 months ago
Compound Particle Swarm Optimization in Dynamic Environments
Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compoun...
Lili Liu, Dingwei Wang, Shengxiang Yang
GECCO
2008
Springer
145views Optimization» more  GECCO 2008»
15 years 7 months ago
Towards incremental social learning in optimization and multiagent systems
Social learning is a mechanism that allows individuals to acquire knowledge from others without incurring the costs of acquiring it individually. Individuals that learn socially c...
Marco Antonio Montes de Oca, Thomas Stützle
ANOR
2010
102views more  ANOR 2010»
15 years 6 months ago
Ejection chain and filter-and-fan methods in combinatorial optimization
The design of effective neighborhood structures is fundamentally important for creating better local search and metaheuristic algorithms for combinatorial optimization. Significant...
César Rego, Fred Glover
TSP
2008
89views more  TSP 2008»
15 years 6 months ago
Optimal Filters on the Sphere
We derive optimal filters on the sphere in the context of detecting compact objects embedded in a stochastic background process. The matched filter and the scale adaptive filter ar...
Jason D. McEwen, Michael P. Hobson, Anthony N. Las...