Stefan Wagner, Andreas Beham, Michael Affenzeller,
"Analysis and Visualization of the Impact of Different Parameter Configurations on the Behavior of Evolutionary Algorithms"
: Lecture Notes in Computer Science, 2017
Original Titel:
Analysis and Visualization of the Impact of Different Parameter Configurations on the Behavior of Evolutionary Algorithms
Sprache des Titels:
Englisch
Original Buchtitel:
Lecture Notes in Computer Science
Original Kurzfassung:
Evolutionary algorithms are generic and flexible optimization
algorithms which can be applied to many optimization problems
in different domains. Depending on the specific type of evolutionary algorithm,
they offer several parameters such as population size, mutation
probability, crossover and mutation operators, or number of elite
solutions. How these parameters are set has a crucial impact on the algorithm's
search behavior and thus affects its performance. Therefore,
parameter tuning is an important and challenging task in each application
of evolutionary algorithms in order to retrieve satisfying results.
In this paper, we show how software frameworks for evolutionary algorithms
can support this task. As an example of such a framework,
we describe how HeuristicLab enables automated execution of extensive
parameter tests as well as its capabilities to analyze and visualize the obtained
results. We also introduce a new chart of HeuristicLab, which can
be used to compare the performance of many different parameter configurations
and to drill down on different configurations in an interactive
way. By this means this new chart helps users to visualize the influence
of different parameter values as well as their interdependencies and is
therefore a powerful feature in order to gain a deeper understanding of
the behavior of evolutionary algorithms.