Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 10th European Confer Mario Giacobini,Leonardo Vanneschi,Willi

使服水土

https://doi.org/10.1007/978-3-642-29066-4artificial immune system; evolutionary algorithm; genetic programming; neural networks; swarm intelligen

集合

Megatrends of Socioeconomic Changes,s requiring large or intricate neural network architectures. The modular feed forward neural network (MFFN) architecture decomposes a problem among a number of independent task specific neural networks, and is suggested here as a means of managing neuroevolution for complex problems. We present an a

phase-2-enzyme

stress-test

Jubilation

Explanation of Methodological Choices,istribution. This implies that the vast majority of the strides will be short. However, on rare occasions, the stride are gigantic. We use this technique to self-adapt the mutation rate used in Linear Genetic Programming. We apply this original approach to three different classes of problems: Boolea

无弹性

植物群

Federalism, Sovereignty and Modernitytargeted metabolomics data from plasma samples of insulin-sensitive and -resistant subjects both suffering from non-alcoholic fatty liver disease. In addition to improving the classification accuracy by selecting relevant features, we extract the information that led to their selection and reconstru

Anecdote

使混合

A History of European Monetary Integration,es often bears no relation to the agent processes it stems from. Up to now, linking the global networked dynamics to such agent mechanisms has remained elusive. Here we define an observable dynamic and use it to track the self-organization of cooperators when co-evolving with defectors in networked

hemoglobin