Search Results for author:
Found 7 papers, 0 papers with code
Getting higher on rugged landscapes: Inversion mutations open access to fitter adaptive peaks in NK fitness landscapes
We use the well known NK model to instantiate rugged landscapes.
Highlights of Semantics in Multi-objective Genetic Programming
Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed.
GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming
Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation.
Simulating short- and long-term evolutionary dynamics on rugged landscapes
We report the emergence of different time scales: a short-term dynamics mainly driven by point mutations, followed by a long-term (stasis-like) waiting period until a new mutation arises.
Semantics in Multi-objective Genetic Programming
This is then used to compute a distance between the pivot and every individual in the population.
Plotting time: On the usage of CNNs for time series classification
We present a novel approach for time series classification where we represent time series data as plot images and feed them to a simple CNN, outperforming several state-of-the-art methods.
The Milky Way Project: Probing Star Formation with First Results on Yellowballs from DR2
Follow-up work by Kerton et al. (2015) determined that YBs likely trace compact photo-dissociation regions associated with massive and intermediate-mass star formation.
Astrophysics of Galaxies
