Titlebook: Parallel Problem Solving from Nature – PPSN XVIII; 18th International C Michael Affenzeller,Stephan M. Winkler,Thomas B?ck Conference proce

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Multi-objective Random Bit Climbers with?Weighted Permutation on?Large Scale Binary MNK-Landscapese decreases significantly when solving large scale problems, which can have hundreds or thousands of variables. Although several algorithms have been proposed to tackle this problem in the recent years, most of them are designed for continuous problems, and only a few focus on binary ones. In this p

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Influence Maximization in?Hypergraphs Using Multi-Objective Evolutionary Algorithmswork that spreads influence at most. Among the various methods for solving the IM problem, evolutionary algorithms (EAs) have been shown to be particularly effective. While the literature on the topic is particularly ample, only a few attempts have been made at solving the IM problem over higher-ord

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Biased Pareto Optimization for?Subset Selection with?Dynamic Cost Constraintsudget, which has various applications such as influence maximization and maximum coverage. In real-world scenarios, the budget, representing available resources, may change over time, which requires that algorithms must adapt quickly to new budgets. However, in this dynamic environment, previous alg

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Reaching Pareto Front Shape Invariance with?a?Continuous Multi-objective Ant Colony Optimization Algorithm problem. Many Multi-Objective Evolutionary Algorithms (MOEAs) have been proposed for this aim achieving remarkable results. However, the utilization of Swarm Intelligence algorithms such as Multi-Objective Ant Colony Optimization Algorithms (MOACOs) has been scarcely studied. In this paper, we prop