Abstract
As in multiobjective optimization, multimodal optimization generates solution sets that must be measured in order to compare different optimization algorithms. We discuss similarities and differences in the requirements for measures in both domains and suggest a property-based taxonomy. The process of measuring actually consists of two subsequent steps, a subset selection that only considers ‘suitable’ points (or just takes all available points of a solution set) and the actual measuring. Known quality indicators often rely on problem knowledge (objective values and/or locations of optima and basins) which makes them unsuitable for real-world applications. Hence, we propose a new subset selection heuristic without such demands, which thereby enables measuring solution sets of single-objective problems, provided a distance metric exists.
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Preuss, M., Wessing, S. (2013). Measuring Multimodal Optimization Solution Sets with a View to Multiobjective Techniques. In: Emmerich, M., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in Intelligent Systems and Computing, vol 227. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01128-8_9
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DOI: https://doi.org/10.1007/978-3-319-01128-8_9
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