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Description of CEC2017 benchmark functions.
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创建时间：
2022-10-20
相关数据集
"Benchmark-to-Benchmark Generalisation of Automated Algorithm Selection in Single-objective Black-Box Optimisation"
"The provided repository contains code and data supporting the accompanying manuscript, entitled: \u201cBenchmark-to-Benchmark Generalisation of Automated Algorithm Selection in Single-objective Black
DataCite Commons2025-09-20 更新30
Percentage of evolutionary experiments that achieve maximum fitness in experiments carried out with different values of the Stochasticity parameter.
Data obtained by running 30 replications lasting 6,000 generations for each value of the parameter. The Wtrial, vFaultRate, and MutRate parameters have been set 25%, 5, and 0.02, respectively. The per
Figshare2016-09-28 更新20
Comparison of average comprehensive evaluation of GA, NSGA-II and I-NSGA-II.
Comparison of average comprehensive evaluation of GA, NSGA-II and I-NSGA-II.
NIAID Data Ecosystem20
Average number of evaluations required to reach a fitness equal or greater than 0.9 for the first time.
Average number of evaluations required to reach a fitness equal or greater than 0.9 for the first time.
Figshare2018-07-18 更新30
The calculation results of 72 instances of 0-1 knapsack problem and 80 instances of knapsack problem with single continuous variable by evolutionary algorithms
In the document, the calculation results of 72 instances of 0-1 knapsack problem (0-1KP) obtained by SAHA, GA, GTOA, HBDE, BPSO, BTGA and bitABC are given, the calculation results of 80 instances of k
NIAID Data Ecosystem20
