669 search results - page 126 / 134 » An evolutionary method for complex-process optimization |
105
Voted
GECCO
2007
Springer
15 years 6 months ago
2007
Springer
A significant problem for evolving artificial neural networks is that the physical arrangement of sensors and effectors is invisible to the evolutionary algorithm. For example,...
GECCO
2007
Springer
15 years 6 months ago
2007
Springer
Markov Networks (also known as Markov Random Fields) have been proposed as a new approach to probabilistic modelling in Estimation of Distribution Algorithms (EDAs). An EDA employ...
98
Voted
GECCO
2007
Springer
15 years 6 months ago
2007
Springer
Thirteen years have passed since Karl Sims published his work on evolving virtual creatures. Since then, several novel approaches to neural network evolution and genetic algorithm...
108
click to vote
CIBCB
2005
IEEE
15 years 6 months ago
2005
IEEE
— In this paper we address the problem of predicting gene activities by finding gene regulatory dependencies in experimental DNA microarray data. Only few approaches to infer th...
115
click to vote
GECCO
2005
Springer
15 years 6 months ago
2005
Springer
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...