We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are ...
Recent work has provided functions that can be used to prove a principled distinction between the capabilities of mutation-based and crossover-based algorithms. However, prior fun...
We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graphbased) chromosomal representation. We designed CrossNet with the...
Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are speci...
Martin V. Butz, Martin Pelikan, Xavier Llorà...
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...