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GECCO
2006
Springer
150views Optimization» more  GECCO 2006»
15 years 5 months ago
Nonlinear parametric regression in genetic programming
Genetic programming has been considered a promising approach for function approximation since it is possible to optimize both the functional form and the coefficients. However, it...
Yung-Keun Kwon, Sung-Soon Choi, Byung Ro Moon
GECCO
2000
Springer
142views Optimization» more  GECCO 2000»
15 years 5 months ago
Controlling Effective Introns for Multi-Agent Learning by Genetic Programming
This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the effective cooperative behav...
Hitoshi Iba, Makoto Terao
ICMLA
2007
15 years 3 months ago
Improving gene expression programming performance by using differential evolution
Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and...
Qiongyun Zhang, Chi Zhou, Weimin Xiao, Peter C. Ne...
GECCO
2004
Springer
169views Optimization» more  GECCO 2004»
15 years 7 months ago
Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics
The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and ...
Marylyn D. Ritchie, Christopher S. Coffey, Jason H...
GRAPHITE
2006
ACM
15 years 8 months ago
Methodologies for immersive robot programming in an augmented reality environment
—Advancements in robotics have gained much momentum in recent years. Industrial robotic systems are increasingly being used outside the factory floor, evident by the growing pres...
Soh-Khim Ong, J. W. S. Chong, Andrew Y. C. Nee