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FOGA
1990
13 years 5 months ago
A Hierarchical Approach to Learning the Boolean Multiplexer Function
This paper describes the recently developed genetic programming paradigm which genetically breeds populations of computer programs to solve problems. In genetic programming, the i...
John R. Koza
GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
13 years 8 months ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
PROPERTYTESTING
2010
13 years 2 months ago
Testing by Implicit Learning: A Brief Survey
We give a high-level survey of the "testing by implicit learning" paradigm, and explain some of the property testing results for various Boolean function classes that ha...
Rocco A. Servedio
ICML
2005
IEEE
14 years 5 months ago
Why skewing works: learning difficult Boolean functions with greedy tree learners
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
GECCO
2006
Springer
140views Optimization» more  GECCO 2006»
13 years 8 months ago
A representational ecology for learning classifier systems
The representation used by a learning algorithm introduces a bias which is more or less well-suited to any given learning problem. It is well known that, across all possible probl...
James A. R. Marshall, Tim Kovacs