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GECCO
2005
Springer
110views Optimization» more  GECCO 2005»
13 years 10 months ago
Towards identifying populations that increase the likelihood of success in genetic programming
This paper presents a comprehensive, multivariate account of how initial population material is used over the course of a genetic programming run as while various factors influenc...
Jason M. Daida
AAAI
2006
13 years 6 months ago
On the Difficulty of Modular Reinforcement Learning for Real-World Partial Programming
In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
SOQUA
2007
13 years 6 months ago
ConTest listeners: a concurrency-oriented infrastructure for Java test and heal tools
With the proliferation of the new multi-core personal computers, and the explosion of the usage of highly concurrent machine configuration, concurrent code moves from being writt...
Yarden Nir-Buchbinder, Shmuel Ur
UM
2007
Springer
13 years 11 months ago
Eliciting Adaptation Knowledge from On-Line Tutors to Increase Motivation
In the classroom, teachers know how to motivate their students and how to exploit this knowledge to adapt or optimize their instruction when a student shows signs of demotivation. ...
Teresa Hurley, Stephan Weibelzahl
GECCO
2006
Springer
139views Optimization» more  GECCO 2006»
13 years 9 months ago
Genetic programming: optimal population sizes for varying complexity problems
The population size in evolutionary computation is a significant parameter affecting computational effort and the ability to successfully evolve solutions. We find that population...
Alan Piszcz, Terence Soule