Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Evolutionary computation for solving search-based data analytics problems
Automatic extracting of knowledge from massive data samples, ie, big data analytics (BDA),
has emerged as a vital task in almost all scientific research fields. The BDA problems are …
has emerged as a vital task in almost all scientific research fields. The BDA problems are …
The first proven performance guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a combinatorial optimization problem
The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent
algorithms to solve multi-objective optimization problems. Recently, the first mathematical …
algorithms to solve multi-objective optimization problems. Recently, the first mathematical …
Runtime analysis for the NSGA-II: proving, quantifying, and explaining the inefficiency for many objectives
W Zheng, B Doerr - IEEE Transactions on Evolutionary …, 2023 - ieeexplore.ieee.org
The nondominated sorting genetic algorithm II (NSGA-II) is one of the most prominent
algorithms to solve multiobjective optimization problems. Despite numerous successful …
algorithms to solve multiobjective optimization problems. Despite numerous successful …
[HTML][HTML] Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II)
W Zheng, B Doerr - Artificial Intelligence, 2023 - Elsevier
The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
[PDF][PDF] An efficient evolutionary algorithm for subset selection with general cost constraints
In this paper, we study the problem of selecting a subset from a ground set to maximize a
monotone objective function f such that a monotone cost function c is bounded by an upper …
monotone objective function f such that a monotone cost function c is bounded by an upper …
Nearly linear-time, parallelizable algorithms for non-monotone submodular maximization
A Kuhnle - Proceedings of the AAAI Conference on Artificial …, 2021 - ojs.aaai.org
We study combinatorial, parallelizable algorithms for maximization of a submodular function,
not necessarily monotone, with respect to a cardinality constraint k. We improve the best …
not necessarily monotone, with respect to a cardinality constraint k. We improve the best …
Improved evolutionary algorithms for submodular maximization with cost constraints
We present an evolutionary algorithm evo-SMC for the problem of Submodular Maximization
under Cost constraints (SMC). Our algorithm achieves $1/2$-approximation with a high …
under Cost constraints (SMC). Our algorithm achieves $1/2$-approximation with a high …
Faster guarantees of evolutionary algorithms for maximization of monotone submodular functions
VG Crawford - arxiv preprint arxiv:1908.01230, 2019 - arxiv.org
In this paper, the monotone submodular maximization problem (SM) is studied. SM is to find
a subset of size $\kappa $ from a universe of size $ n $ that maximizes a monotone …
a subset of size $\kappa $ from a universe of size $ n $ that maximizes a monotone …
Optimal region search with submodular maximization
Region search is an important problem in location based services due to its wide
applications. In this paper, we study the problem of optimal region search with submodular …
applications. In this paper, we study the problem of optimal region search with submodular …
Efficient Parallel Algorithm for Minimum Cost Submodular Cover Problem with Lower Adaptive Complexity
In this paper, we study the Minimum Cost Submodular Cover (â „³ð'žð'® ð'ž) problem over
the ground set of size n, which aims at finding a subset with the minimal cost required so that …
the ground set of size n, which aims at finding a subset with the minimal cost required so that …