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GECCO
2005
Springer
150views Optimization» more  GECCO 2005»
15 years 5 months ago
Population-based incremental learning with memory scheme for changing environments
In recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several a...
Shengxiang Yang
GECCO
2005
Springer
220views Optimization» more  GECCO 2005»
15 years 5 months ago
Scale invariant pareto optimality: a meta--formalism for characterizing and modeling cooperativity in evolutionary systems
This article describes a mathematical framework for characterizing cooperativity in complex systems subject to evolutionary pressures. This framework uses three foundational compo...
Mark Fleischer
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
15 years 5 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
GECCO
2005
Springer
15 years 5 months ago
Optimizing parameters of a mobile ad hoc network protocol with a genetic algorithm
Mobile ad hoc networks are typically designed and evaluated in generic simulation environments. However the real conditions in which these networks are deployed can be quite diff...
David J. Montana, Jason Redi
GECCO
2005
Springer
142views Optimization» more  GECCO 2005»
15 years 5 months ago
Genetic programming: parametric analysis of structure altering mutation techniques
We hypothesize that the relationship between parameter settings, speci cally parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programmin...
Alan Piszcz, Terence Soule