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GECCO
2005
Springer
119views Optimization» more  GECCO 2005»
15 years 3 months ago
Improving GA search reliability using maximal hyper-rectangle analysis
In Genetic algorithms it is not easy to evaluate the confidence level in whether a GA run may have missed a complete area of good points, and whether the global optimum was found....
Chongshan Zhang, Khaled Rasheed
GECCO
2010
Springer
154views Optimization» more  GECCO 2010»
15 years 2 months ago
Evolutionary learning in networked multi-agent organizations
This study proposes a simple computational model of evolutionary learning in organizations informed by genetic algorithms. Agents who interact only with neighboring partners seek ...
Jae-Woo Kim
GECCO
2009
Springer
152views Optimization» more  GECCO 2009»
15 years 2 months ago
A data-based coding of candidate strings in the closest string problem
Given a set of strings S of equal lengths over an alphabet Σ, the closest string problem seeks a string over Σ whose maximum Hamming distance to any of the given strings is as s...
Bryant A. Julstrom
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 1 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
GECCO
2008
Springer
261views Optimization» more  GECCO 2008»
14 years 11 months ago
SSNNS -: a suite of tools to explore spiking neural networks
We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural network...
Heike Sichtig, J. David Schaffer, Craig B. Laramee