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» Evolutionary Algorithms for the Satisfiability Problem
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94
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EMO
2005
Springer
68views Optimization» more  EMO 2005»
15 years 7 months ago
Multi-objective Optimization of Problems with Epistemic Uncertainty
Abstract. Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-w...
Philipp Limbourg
NC
2002
210views Neural Networks» more  NC 2002»
15 years 1 months ago
Recent approaches to global optimization problems through Particle Swarm Optimization
This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detectin...
Konstantinos E. Parsopoulos, Michael N. Vrahatis
119
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PPSN
2010
Springer
15 years 6 days ago
An Analysis of the XOR Dynamic Problem Generator Based on the Dynamical System
In this paper, we use the exact model (or dynamical system approach) to describe the standard evolutionary algorithm (EA) as a discrete dynamical system for dynamic optimization pr...
Renato Tinós, Shengxiang Yang
AISB
1997
Springer
15 years 6 months ago
Modelling Bounded Rationality Using Evolutionary Techniques
A technique for the credible modelling of economic agents with bounded rationality based on the evolutionary techniques is described. The genetic programming paradigm is most suite...
Bruce Edmonds, Scott Moss
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 5 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone