HEAL Researchers at LINDI2011 conference

HEAL researchers Andreas Beham and Stefan Vonolfen are heading to Budapest, Hungary, on Wednesday to attend the 3rd IEEE International Symposium on Logistics and Industrial Informatics (LINDI 2011). We have been quite busy in the area of logistics and production optimization, as the titles of our accepted LINDI papers indicate:
  • Solving Large-Scale Vehicle Routing Problem Instances Using an Island-Model Offspring Selection Genetic Algorithm
  • A New Metric to Measure Distances between Solutions to the Quadratic Assignment Problem
  • Re-Warehousing vs. Healing: Strategies for Warehouse Storage Location Assignment
  • Production Fine Planning Using a Solution Archive of Priority Rules
  • Simulation-based Evolution of Municipal Glass-Waste Collection Strategies Utilizing Electric Trucks

HeuristicLab 3.3.5 Released

Right on time for the upcoming GECCO Tutorial we have released HeuristicLab 3.3.5. Let us know what you think! New features include:
  • Random forests (wrapper for ALGLIB implementation)
  • Allele frequency analyzer for symbolic data analysis expression trees
  • Optional caching and parallel execution of external evaluation results
  • New analyzers and operators for the quadratic assignment problem (QAP)
  • New tree view for experiments etc.

For the full feature list, samples and documentation, go to

GECCO 2011: HeuristicLab Tutorial

With just a week to go until the GECCO 2011 conference, we would like to remind you that Stefan Wagner and Gabriel Kronberger will hold a HeuristicLab Tutorial there. In particular, they will show how to
  • parameterize and execute evolutionary algorithms to solve combinatorial optimization problems (traveling salesman, vehicle routing) as well as data analysis problems (regression, classification).
  • model custom optimization algorithms in the graphical algorithm designer.
  • setup a large scale optimization experiment and execute such experiments on multi‐core or cluster systems.
  • inspect executed test runs with HeuristicLab’s interactive charts for visual and statistical
  • extend HeuristicLab with your own problems or algorithms.

If you are attending GECCO it would be great to meet up!

Funded PhD Position: Exact Tracing of Evolutionary Search Trajectories in Complex Hypothesis Spaces

The Heuristic and Evolutionary Algorithms Laboratory at the Upper Austria University of Applied Sciences (Austria) offers a funded PhD position within the scope of the International PhD Program in Informatics Hagenberg. The group conducts scientific research in the field of metaheuristics for solving business, engineering, and biomedical problems.

The focus of the PhD research project should be to gain a better understanding of the evolutionary process by analyzing the search trajectories of genetic programming (GP) algorithms in different hypothesis spaces for academic as well as real-world data mining applications.

Recent algorithmic achievements like offspring selection in combination with linear scaling have enabled GP to achieve high quality results in system identification in less than 50 generations using populations with only several hundred individuals. Therefore the active gene pool of evolutionary search remains manageable and may be subjected to new theoretical investigations closely related to genetic programming schema theories, building block hypotheses and bloat theories.

We are seeking highly motivated and creative students with a master degree in Mathematics/Informatics or a related technical field. Prior knowledge about heuristic optimization and machine learning and programming experience with C# is a plus. Applications (including your CV, degree certificates, list of publications etc.) and informal queries about the lab and research projects should be addressed to Dr. Michael Affenzeller (, phone +43 (0)7236 3888-2031).

Further details about the PhD program can be found on

A longer, printable description of the PhD position as PDF document is also available.

Application Deadline: July 15th, 2011.

HeuristicLab 3.3.4 Released

The HeuristicLab development team is pleased to announce that you can now download HeuristicLab 3.3.4. We have added some exciting new features in this last release, such as:
  • New algorithms: Particle Swarm Optimization, Variable Neighborhood Search, Support Vector Machines for Classification
  • New problems: Quadratic Assignment Problem, Multi-objective Symbolic Regression Problem

For the full feature list, samples and documentation, go to


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