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» On the Convergence of Bound Optimization Algorithms
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CEC
2011
IEEE
12 years 7 months ago
Accelerating convergence towards the optimal pareto front
—Evolutionary algorithms have been very popular optimization methods for a wide variety of applications. However, in spite of their advantages, their computational cost is still ...
Mohsen Davarynejad, Jafar Rezaei, Jos L. M. Vranck...
MP
2008
126views more  MP 2008»
13 years 7 months ago
Geometry of interpolation sets in derivative free optimization
We consider derivative free methods based on sampling approaches for nonlinear optimization problems where derivatives of the objective function are not available and cannot be dir...
Andrew R. Conn, Katya Scheinberg, Luís N. V...
ICML
2003
IEEE
14 years 8 months ago
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Oppon
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...
Vincent Conitzer, Tuomas Sandholm
COR
2008
122views more  COR 2008»
13 years 7 months ago
First steps to the runtime complexity analysis of ant colony optimization
: The paper presents results on the runtime complexity of two ant colony optimization (ACO) algorithms: Ant System, the oldest ACO variant, and GBAS, the first ACO variant for whic...
Walter J. Gutjahr
WSC
2001
13 years 9 months ago
Global random optimization by simultaneous perturbation stochastic approximation
We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
John L. Maryak, Daniel C. Chin