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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
CGO
2005
IEEE
14 years 1 months ago
Optimizing Sorting with Genetic Algorithms
The growing complexity of modern processors has made the generation of highly efficient code increasingly difficult. Manual code generation is very time consuming, but it is oft...
Xiaoming Li, María Jesús Garzar&aacu...
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
13 years 11 months ago
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is...
Garnett Carl Wilson, Malcolm I. Heywood
CISIS
2011
IEEE
12 years 7 months ago
Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations
—Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive ap...
Marcin Bogdanski, Peter R. Lewis, Tobias Becker, X...
GECCO
2003
Springer
100views Optimization» more  GECCO 2003»
14 years 18 days ago
Studying the Advantages of a Messy Evolutionary Algorithm for Natural Language Tagging
The process of labeling each word in a sentence with one of its lexical categories (noun, verb, etc) is called tagging and is a key step in parsing and many other language processi...
Lourdes Araujo