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» Removing the Genetics from the Standard Genetic Algorithm
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BMCBI
2006
99views more  BMCBI 2006»
13 years 8 months ago
Genetic algorithm learning as a robust approach to RNA editing site prediction
Background: RNA editing is one of several post-transcriptional modifications that may contribute to organismal complexity in the face of limited gene complement in a genome. One f...
James Thompson, Shuba Gopal
CEC
2007
IEEE
14 years 3 months ago
Continuous dynamic problem generators for evolutionary algorithms
— Addressing dynamic optimization problems has attracted a growing interest from the evolutionary algorithm community in recent years due to its importance in the applications of...
Renato Tinós, Shengxiang Yang
GECCO
2009
Springer
156views Optimization» more  GECCO 2009»
14 years 3 months ago
Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster
Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large n...
Kenneth Holladay
GECCO
2003
Springer
14 years 1 months ago
A Kernighan-Lin Local Improvement Heuristic That Solves Some Hard Problems in Genetic Algorithms
We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heur...
William A. Greene
UPP
2004
Springer
14 years 2 months ago
Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm
Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been know...
Thomas Bäck, Ron Breukelaar, Lars Willmes