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» Directional self-learning of genetic algorithm
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82
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WSC
2008
15 years 8 hour ago
Simulation optimization for industrial scheduling using hybrid genetic representation
Simulation modeling has the capability to represent complex real-world systems in details and therefore it is suitable to develop simulation models for generating detailed operati...
Marcus Andersson, Amos Ng, Henrik Grimm
GECCO
2005
Springer
122views Optimization» more  GECCO 2005»
15 years 3 months ago
An enhanced GA to improve the search process reliability in tuning of control systems
Evolutionary Algorithms (EAs) have been largely applied to optimisation and synthesis of controllers. In spite of several successful applications and competitive solutions, the st...
Andrea Soltoggio
78
Voted
GECCO
2008
Springer
130views Optimization» more  GECCO 2008»
14 years 10 months ago
Parsimony pressure made easy
The parsimony pressure method is perhaps the simplest and most frequently used method to control bloat in genetic programming. In this paper we first reconsider the size evolutio...
Riccardo Poli, Nicholas Freitag McPhee
PRL
2006
95views more  PRL 2006»
14 years 9 months ago
Evolving visual sonar: Depth from monocular images
To recover depth from images, the human visual system uses many monocular depth cues, which vision research has only begun to explore. Because a given image can have many possible...
Martin C. Martin
EVOW
2001
Springer
15 years 2 months ago
An Evolutionary Annealing Approach to Graph Coloring
This paper presents a new heuristic algorithm for the graph coloring problem based on a combination of genetic algorithms and simulated annealing. Our algorithm exploits a novel cr...
Dimitris Fotakis, Spiridon D. Likothanassis, Stama...