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GECCO
2009
Springer
112views Optimization» more  GECCO 2009»
14 years 3 months ago
Soft memory for stock market analysis using linear and developmental genetic programming
Recently, a form of memory usage was introduced for genetic programming (GP) called “soft memory.” Rather than have a new value completely overwrite the old value in a registe...
Garnett Carl Wilson, Wolfgang Banzhaf
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
14 years 2 months ago
EA models and population fixed-points versus mutation rates for functions of unitation
Using a dynamic systems model for the Simple Genetic Algorithm due to Vose[1], we analyze the fixed point behavior of the model without crossover applied to functions of unitation...
J. Neal Richter, John Paxton, Alden H. Wright
GECCO
2008
Springer
145views Optimization» more  GECCO 2008»
13 years 9 months ago
Threshold selecting: best possible probability distribution for crossover selection in genetic algorithms
The paper considers the problem of selecting individuals in the current population in Genetic Algorithms for crossover to find a solution of high fitness of a given combinatoria...
Jörg Lässig, Karl Heinz Hoffmann, Mihael...
SAC
2006
ACM
14 years 2 months ago
Adaptive mutation with fitness and allele distribution correlation for genetic algorithms
In this paper, a new gene based adaptive mutation scheme is proposed for genetic algorithms (GAs), where the information on gene based fitness statistics and on gene based allele...
Shengxiang Yang, Sima Uyar
GECCO
2003
Springer
123views Optimization» more  GECCO 2003»
14 years 1 months ago
Benefits of Implicit Redundant Genetic Algorithms for Structural Damage Detection in Noisy Environments
A robust structural damage detection method that can handle noisy frequency response function information is discussed. The inherent unstructured nature of damage detection problem...
Anne M. Raich, Tamás Liszkai