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» Methods for Evolving Robust Programs
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GECCO
2008
Springer
175views Optimization» more  GECCO 2008»
13 years 10 months ago
Using differential evolution for symbolic regression and numerical constant creation
One problem that has plagued Genetic Programming (GP) and its derivatives is numerical constant creation. Given a mathematical formula expressed as a tree structure, the leaf node...
Brian M. Cerny, Peter C. Nelson, Chi Zhou
GECCO
1999
Springer
106views Optimization» more  GECCO 1999»
14 years 1 months ago
Generating Lemmas for Tableau-based Proof Search Using Genetic Programming
Top-down or analytical provers based on the connection tableau calculus are rather powerful, yet have notable shortcomings regarding redundancy control. A well-known and successfu...
Marc Fuchs, Dirk Fuchs, Matthias Fuchs
EPS
1995
Springer
14 years 22 days ago
An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines
Evolutionary programming was first offered as an alternative method for generating artificial intelligence. Experiments were offered in which finite state machines were used to...
Lawrence J. Fogel, Peter J. Angeline, David B. Fog...
GECCO
2009
Springer
103views Optimization» more  GECCO 2009»
14 years 3 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
AIIA
1995
Springer
14 years 23 days ago
Learning Programs in Different Paradigms using Genetic Programming
Genetic Programming (GP) is a method of automatically inducing programs by representing them as parse trees. In theory, programs in any computer languages can be translated to par...
Man Leung Wong, Kwong-Sak Leung