Change of DNA sequence that fuels evolution is, to a certain extent, a deterministic process because mutagenesis does not occur in an absolutely random manner. So far, it has not ...
Ch. Mizas, Georgios Ch. Sirakoulis, Vasilios A. Ma...
We show that there are unimodal fitness functions and genetic algorithm (GA) parameter settings where the GA, when initialized with a random population, will not move close to the...
In genetic programming, the reproductive operators of crossover and mutation both require the selection of nodes from the reproducing individuals. Both unbiased random selection a...
We propose a multi-agent genetic algorithm to accomplish belief revision. The algorithm implements a new evolutionary strategy resulting from a combination of Darwinian and Lamarck...
Abstract. We present the Acyclic Bayesian Net Generator, a new approach to learn the structure of a Bayesian network using genetic algorithms. Due to the encoding mechanism, acycli...