Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization
This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.
KeywordsObjective Space Objective Vector Iterate Local Search Stochastic Local Search Perl Program
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This work was supported by the META-X project, an Action de Recherche Concertée funded by the Scientific Research Directorate of the French Community of Belgium. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Associate. The authors also acknowledge Carlos M. Fonseca for providing us the code for computing the EAF as well as for helpful discussions on the main topic of this chapter.
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