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» Introduction to genetic algorithms
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CEC
2005
IEEE
15 years 7 months ago
Heterogeneous multiprocessor scheduling with differential evolution
The problem of scheduling a parallel program given by a Directed Acyclic Graph (DAG) of tasks is a well-studied area. We present a new approach which employs Differential Evolution...
Krzysztof Rzadca, Franciszek Seredynski
GECCO
1999
Springer
133views Optimization» more  GECCO 1999»
15 years 10 months ago
Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks
In this work MagnetoEncephaloGram (MEG) recordings of epileptic patients were analyzed using a hybrid neural networks training algorithm. This algorithm combines genetic algorithm...
Adam V. Adamopoulos, Efstratios F. Georgopoulos, S...
FOGA
2011
14 years 9 months ago
Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, ...
Greg Durrett, Frank Neumann, Una-May O'Reilly
GECCO
2005
Springer
186views Optimization» more  GECCO 2005»
15 years 11 months ago
Subproblem optimization by gene correlation with singular value decomposition
Several ways of using singular value decomposition (SVD), a linear algebra technique typically used for information retrieval, to decompose problems into subproblems are investiga...
Jacob G. Martin
GECCO
2005
Springer
150views Optimization» more  GECCO 2005»
15 years 11 months ago
A GA for maximum likelihood phylogenetic inference using neighbour-joining as a genotype to phenotype mapping
Evolutionary relationships among species can be represented by a phylogenetic tree and inferred by optimising some measure of fitness, such as the statistical likelihood of the t...
Leon Poladian