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» Introduction to genetic algorithms
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EVOW
2009
Springer
16 years 1 months ago
Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming
A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market...
Garnett Carl Wilson, Wolfgang Banzhaf
GECCO
2007
Springer
160views Optimization» more  GECCO 2007»
16 years 26 days ago
An analysis of constructive crossover and selection pressure in genetic programming
A common problem in genetic programming search algorithms is destructive crossover in which the offspring of good parents generally has worse performance than the parents. Design...
Huayang Xie, Mengjie Zhang, Peter Andreae
GECCO
2005
Springer
183views Optimization» more  GECCO 2005»
16 years 7 days ago
802.11 network intrusion detection using genetic programming
Genetic Programming (GP) based Intrusion Detection Systems (IDS) use connection state network data during their training phase. These connection states are recorded as a set of fe...
Patrick LaRoche, A. Nur Zincir-Heywood
GECCO
2004
Springer
16 years 2 days ago
Inducing Sequentiality Using Grammatical Genetic Codes
Abstract. This paper studies the inducement of sequentiality in genetic algorithms (GAs) for uniformly-scaled problems. Sequentiality is a phenomenon in which sub-solutions converg...
Kei Ohnishi, Kumara Sastry, Ying-Ping Chen, David ...
EUROGP
2003
Springer
119views Optimization» more  EUROGP 2003»
15 years 12 months ago
Maximum Homologous Crossover for Linear Genetic Programming
We introduce a new recombination operator, the Maximum Homologous Crossover for Linear Genetic Programming. In contrast to standard crossover, it attempts to preserve similar struc...
Michael Defoin-Platel, Manuel Clergue, Philippe Co...