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GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
16 years 21 days ago
Takeover time curves in random and small-world structured populations
We present discrete stochastic mathematical models for the growth curves of synchronous and asynchronous evolutionary algorithms with populations structured according to a random ...
Mario Giacobini, Marco Tomassini, Andrea Tettamanz...
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
16 years 21 days ago
Genetic drift in univariate marginal distribution algorithm
Like Darwinian-type genetic algorithms, there also exists genetic drift in Univariate Marginal Distribution Algorithm (UMDA). Since the universal analysis of genetic drift in UMDA...
Yi Hong, Qingsheng Ren, Jin Zeng
GECCO
2005
Springer
106views Optimization» more  GECCO 2005»
16 years 21 days ago
Fitness uniform deletion: a simple way to preserve diversity
A commonly experienced problem with population based optimisation methods is the gradual decline in population diversity that tends to occur over time. This can slow a system’s ...
Shane Legg, Marcus Hutter
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
16 years 21 days ago
Evolutionary computation and the c-value paradox
The C-value Paradox is the name given in biology to the wide variance in and often very large amount of DNA in eukaryotic genomes and the poor correlation between DNA length and p...
Sean Luke
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
16 years 21 days ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
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