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» The Evolutionary Control Methodology: An Overview
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HIS
2007
13 years 9 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin
GECCO
2007
Springer
177views Optimization» more  GECCO 2007»
14 years 1 months ago
Evolving problem heuristics with on-line ACGP
Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees. ...
Cezary Z. Janikow
CCE
2008
13 years 7 months ago
Chance constrained programming approach to process optimization under uncertainty
Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging tas...
Pu Li, Harvey Arellano-Garcia, Günter Wozny
VSGAMES
2010
114views Game Theory» more  VSGAMES 2010»
13 years 6 months ago
Privacy Challenges and Methods for Virtual Classrooms in Second Life Grid and OpenSimulator
—Mass adoption of virtual world platforms for education and training implies efficient management of computational resources. In Second Life Grid and OpenSimulator, commonly used...
Andreas Vilela, Marcio Cardoso, Daniel Martins, Ar...
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
13 years 5 months ago
Genetic programming for quantitative stock selection
We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
Ying L. Becker, Una-May O'Reilly