The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjust...
We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum circuits, then ...
This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossov...
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...
Genetic programming has highlighted the problem of bloat, the uncontrolled growth of the average size of an individual in the population. The most common approach to dealing with ...
The error threshold of replication is an important notion of the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an e...
The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent pop...
The frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. Th...