- author = "Shuyue Stella Li and Hannah Peeler and Andrew N. Sloss and Kenneth N. Reid and Wolfgang Banzhaf",
- title = "Genetic Improvement in the {Shackleton} Framework for Optimizing {LLVM} Pass Sequences",
- booktitle = "GI @ GECCO 2022",
- year = "2022",
- editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke",
- pages = "1938--1939",
- address = "Boston, USA",
- publisher_address = "New York, NY, USA",
- month = "9 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- note = "{Winner Best Presentation}",
- keywords = "genetic algorithms, genetic programming, genetic improvement, linear genetic programming, Compilers, Evolutionary Algorithms, Compiler Optimization, Parameter Tuning, Metaheuristics, Shackleton-GI",
- isbn13 = "978-1-4503-9268-6/22/07",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Li_2022_GI.pdf",
-
URL = "
https://arxiv.org/abs/2204.13261",
-
DOI = "
doi:10.1145/3520304.3534000",
-
slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/li-genetic-improvement-in-the-shackleton-gi-gecco-22.pdf",
-
code_url = "https://github.com/ARM-software/Shackleton-Framework",
-
video_url = "https://www.youtube.com/watch?v=20l_d3UuDPU&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=3",
- size = "2 pages",
- abstract = "Genetic Improvement is a search technique that aims to improve a given acceptable solution to a problem. we present the novel use of genetic improvement to find problem-specific optimized LLVM Pass sequences. We develop a Pass-level edit representation in the linear genetic programming framework, Shackleton, to evolve the modifications to be applied to the default optimization Pass sequences. Our GI-evolved solution has a mean of 3.7percent runtime improvement compared to the default LLVM optimisation level -O3 which targets runtime. The proposed GI method provides an automatic way to find a problem-specific optimization sequence that improves upon a general solution without any expert domain knowledge. we discuss the advantages and limitations of the GI feature in the Shackleton Framework and present our results.",
-
notes = "http://geneticimprovementofsoftware.com/events/gecco2022
Backtrack Algorithm for the Subset Sum Problem (SSP)
GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",
