Abstract
HeuristicLab is a graphical user interface (GUI) based framework for heuristic and evolutionary algorithms, designed for ease of use. It offers a plugin-based architecture (which enables users to add custom extensions without knowing the whole of the source code), a domain-independent model to represent arbitrary search algorithms, support for graphical user interfaces, and the ability to accommodate parallel algorithms. A comfortable and feature-rich graphical user interface reduces the learning effort and enables users without programming skills to use and apply HeuristicLab. Several well-known heuristic algorithms and optimization problems are already implemented in HeuristicLab and can be used off-the-shelf. Users can create and reuse plugins to integrate new features and extend the functionality of HeuristicLab. HeuristicLab supports parallel execution of algorithms on multi-core and cluster systems. It provides interactive charts for quickly and thoroughly analyzing results.
Original language | English |
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Pages (from-to) | 215-218 |
Number of pages | 4 |
Journal | Genetic Programming and Evolvable Machines |
Volume | 15 |
Issue number | 2 |
DOIs | |
State | Published - 1 Jan 2014 |
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Science Applications