Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Model Driven Rapid Prototyping of Heuristic Optimization Algorithms
Computer Aided Systems Theory - EUROCAST 2009
Benefits of plugin-based heuristic optimization software systems
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
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This tutorial demonstrates how to apply and analyze metaheuristic optimization algorithms using the HeuristicLab open source optimization environment. It is shown how to parameterize and execute evolutionary algorithms to solve combinatorial optimization problems (traveling salesman, vehicle routing) as well as data analysis problems (regression, classification). The attendees learn how to assemble different algorithms and parameter settings to large scale optimization experiments and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results are compared using HeuristicLab's interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it is sketched briefly how HeuristicLab can be extended with further optimization problems and how custom optimization algorithms can be modeled using the graphical algorithm designer. Additional details on HeuristicLab can be found at http://dev.heuristiclab.com.