Titlebook: Parallel Problem Solving from Nature – PPSN XVII; 17th International C Günter Rudolph,Anna V. Kononova,Tea Tušar Conference proceedings 202

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Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-objective Optimization?optimization problems. However, it is not uncommon that the constraint violation is hardly approachable in real-world black-box optimization scenarios. It is unclear that whether the existing constrained evolutionary multi-objective optimization algorithms, whose environmental selection mechanism ar

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Fair Feature Selection with a Lexicographic Multi-objective Genetic Algorithmf people based e.g. on gender or race. This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Fair Feature Selection (LGAFFS). LGAFFS selects a subset of relevant features which is optimised for a given classification algorithm, by simultaneously optimising one measure of accu

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Greedy Decremental Quick Hypervolume Subset Selection Algorithmsimes faster than the original implementation and according to the presented computational experiment it is at least competitive to other state-of-the-art codes for hypervolume computation. Second, we present a Greedy Decremental Lazy Quick Hypervolume Subset Selection algorithm. Third, we propose a

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Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocationariables, classic domination-based approaches are known to lose selection pressure when approaching the Pareto set. Indicator-based approaches, such as optimizing the uncrowded hypervolume (UHV), can overcome this issue and ensure that individual solutions converge to the Pareto set. Recently, a gra

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Multi-Objective Evolutionary Algorithm Based on the Linear Assignment Problem and the Hypervolume Approximation Using Polar Coordinates (MOEA-LAPCO)lem (LAP). In a LAP, we want to assign . agents to . tasks, where assigning an agent to a task corresponds to a cost. Thus, the aim is to minimize the overall assignment cost. It has been shown that HDE is competitive with respect to state-of-the-art algorithms. However, in this work, we identify tw

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