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GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
14 years 1 months ago
A statistical learning theory approach of bloat
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of sy...
Sylvain Gelly, Olivier Teytaud, Nicolas Bredeche, ...
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
14 years 1 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
GECCO
2005
Springer
146views Optimization» more  GECCO 2005»
14 years 1 months ago
An empirical study of the robustness of two module clustering fitness functions
Two of the attractions of search-based software engineering (SBSE) derive from the nature of the fitness functions used to guide the search. These have proved to be highly robust...
Mark Harman, Stephen Swift, Kiarash Mahdavi
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
14 years 1 months ago
Genetic drift in univariate marginal distribution algorithm
Like Darwinian-type genetic algorithms, there also exists genetic drift in Univariate Marginal Distribution Algorithm (UMDA). Since the universal analysis of genetic drift in UMDA...
Yi Hong, Qingsheng Ren, Jin Zeng
GECCO
2005
Springer
195views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolutionary strategies for multi-scale radial basis function kernels in support vector machines
In support vector machines (SVM), the kernel functions which compute dot product in feature space significantly affect the performance of classifiers. Each kernel function is suit...
Tanasanee Phienthrakul, Boonserm Kijsirikul