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GECCO
2009
Springer
103views Optimization» more  GECCO 2009»
13 years 11 months ago
Why evolution is not a good paradigm for program induction: a critique of genetic programming
We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are ...
John R. Woodward, Ruibin Bai
GECCO
2008
Springer
123views Optimization» more  GECCO 2008»
13 years 5 months ago
MLS security policy evolution with genetic programming
In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is becoming much more complex. S...
Yow Tzu Lim, Pau-Chen Cheng, Pankaj Rohatgi, John ...
GECCO
2006
Springer
205views Optimization» more  GECCO 2006»
13 years 8 months ago
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP
In contrast with the diverse array of genetic algorithms, the Genetic Programming (GP) paradigm is usually applied in a relatively uniform manner. Heuristics have developed over t...
L. Darrell Whitley, Marc D. Richards, J. Ross Beve...
CIG
2005
IEEE
13 years 10 months ago
Nannon: A Nano Backgammon for Machine Learning Research
A newly designed game is introduced, which feels like Backgammon, but has a simplified rule set. Unlike earlier attempts at simplifying the game, Nannon maintains enough features a...
Jordan B. Pollack
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 8 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa