- author = "Ruihua Zeng and Zhixing Huang and Yongliang Chen and Jinghui Zhong and Liang Feng",
- title = "Comparison of Different Computing Platforms for Implementing Parallel Genetic Programming",
- booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020",
- year = "2020",
- editor = "Yaochu Jin",
- pages = "paper id24270",
- address = "internet",
- month = "19-24 " # jul,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Gene Expression Programming, SL-GEP, parallel platform, MPI, GPU, CUDA, Spark, Parallel Computing",
- isbn13 = "978-1-7281-6929-3",
-
DOI = "
doi:10.1109/CEC48606.2020.9185510",
- size = "8 pages",
- abstract = "Genetic programming (GP) is a powerful tool for knowledge discovery and data mining. Over the past decades, GP has been implemented in various parallel computing platforms to reduce its search time. However, these parallel GPs have different design principles and performance characteristics, which makes it difficult for users to choose the proper parallel GP in practice. To address this issue, this paper focuses on comparing and analyzing the characteristics of parallel GPs implemented in different computing platforms, in terms of running time, the speedup ratio, and the scalability. Based on the empirical results, the guidance of selecting different parallel GPs is concluded.",
-
notes = "SparkGP in Java. F1 = x**5 + x**4 + x**3 + x**2 + x
(-1..+1)
'60 blocks with 256 threads in every block in GPUGP' nvidia GTX 1070
'The speedup ratio of GPs with Spark in large dataset can even be superior to that of MPI' https://wcci2020.org/
South China University of Technology, China; Chongqing University, China.
Also known as \cite{9185510}",
