Learning for Evolutionary Computation
HELIX operates at the intersection of Evolutionary Computation and Machine Learning. We focus on developing methodologies for solving high-dimensional global optimization problems where conventional methods fail.
Leading Unit
AGH University of Krakow
Faculty of Computer Science
Krakow, Poland
Evolutionary Computation
Population-based search dynamics and robust metaheuristic design.
Landscape Analysis
Structural characterization of search spaces to explain performance.
Machine Learning
Surrogate-assisted optimization and data-driven modeling.
Control Systems
Optimization of Predictive Control Problems.
Our Libraries
lonpy
A Python library for constructing, analyzing, and visualizing Local Optima Networks for continuous optimization problems. LONs provide a powerful way to understand the structure of fitness landscapes, revealing how local optima are connected and how difficult it may be to find global optima.
github.com/helix-agh/lonpypyHMS
A Python implementation of Hierarchic Memetic Strategy: a population-based metaheuristic that organizes search processes into a tree-like hierarchy, enabling adaptive exploration at multiple resolution levels.
github.com/helix-agh/pyhmsTeam
Projects
Our Research Output
Browse our full list of publications on AGH's BaDAP repository.
VIEW PUBLICATIONS ON BADAPOpen for Collaborative Research
We welcome collaboration with academic and industry researchers interested in rigorous algorithmic evaluation and optimization. Please contact us regarding potential research collaborations or student thesis opportunities.
- • Tomas Bata University, Zlín
- • Czech Technical University, Prague