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GECCO
2005
Springer
110views Optimization» more  GECCO 2005»
14 years 4 months ago
Towards identifying populations that increase the likelihood of success in genetic programming
This paper presents a comprehensive, multivariate account of how initial population material is used over the course of a genetic programming run as while various factors influenc...
Jason M. Daida
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
14 years 4 months ago
Preventing overfitting in GP with canary functions
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...
Nate Foreman, Matthew P. Evett
GECCO
2005
Springer
127views Optimization» more  GECCO 2005»
14 years 4 months ago
Investigating the performance of module acquisition in cartesian genetic programming
Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this pap...
James Alfred Walker, Julian Francis Miller
SAMOS
2005
Springer
14 years 4 months ago
Automatic FIR Filter Generation for FPGAs
This paper presents a new tool for the automatic generation of highly parallelized Finite Impulse Response (FIR) filters. In this approach we follow our PARO design methodology. P...
Holger Ruckdeschel, Hritam Dutta, Frank Hannig, J&...
GECCO
2005
Springer
14 years 4 months ago
Probing for limits to building block mixing with a tunably-difficult problem for genetic programming
This paper describes a tunably-difficult problem for genetic programming (GP) that probes for limits to building block mixing and assembly. The existence of such a problem can be ...
Jason M. Daida, Michael E. Samples, Matthew J. Byo...