This paper describes our procedure and a software application for conducting large parameter sweep experiments in genetic and evolutionary computation research. Both procedure and...
Michael E. Samples, Jason M. Daida, Matthew J. Byo...
Finding motifs — patterns of conserved residues — within nucleotide and protein sequences is a key part of understanding function and regulation within biological systems. Thi...
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...
The Army’s push towards developing highly flexible military teams that combine manned and unmanned units requires significant advances in the intelligence of the unmanned units ...
Talib S. Hussain, Daniel Cerys, David J. Montana, ...
We present a novel camera network design methodology based on the Parisian approach to evolutionary computation. The problem is partitioned into a set of homogeneous elements, whos...
— We created a human-robot communication system that can adapt to user preferences that can easily change through communication. Even if any learning algorithms are used, evaluat...
Yuki Suga, Chihiro Endo, Daizo Kobayashi, T. Matsu...
A new method for optimizing complex functions and systems is described that employs Learnable Evolution Model (LEM), a form of non-Darwinian evolutionary computation guided by mac...
Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A....
This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (G...
Modularity is thought to improve the evolvability of biological systems [18, 22]. Recent studies in the field of evolutionary computation show that the use of modularity improves...
—As the application and complexity of microelectromechanical (MEMS) devices increases, there is a corresponding need for automated design and optimization tools to augment engine...
Jason D. Lohn, William F. Kraus, Gregory S. Hornby