Techniques and Tools for Local Search Landscape Visualization and Analysis
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Grapheur: a software architecture for reactive and interactive optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Visualizing 4D approximation sets of multiobjective optimizers with prosections
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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In this paper, a method for the visualization of the population of an evolutionary multi-objective optimization (EMO) algorithm is presented. The main characteristic of this approach is the preservation of Paretodominance relations among the individuals as good as possible. It will be shown that in general, a Paretodominance preserving mapping from higher- to lowerdimensional spaces does not exist. Thus, the demand is to find a mapping with as few wrongly indicated dominance relations as possible, which gives one more objective in addition to other mapping objectives like preserving nearest neighbor relations. Therefore, such a mapping poses a multi-objective optimization problem by itself, which is also handled by an EMO algorithm (NSGA-II in this case). The resulting mappings are shown for the run of a NSGA-II version on the 15 objective DTLZ2 problem as an example. From such plots, some insights into evolutionary dynamics can be obtained.