Integrating HeuristicLab with Compilers and Interpreters for Non-Functional Code Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@InProceedings{heuristicLab_GCE_2020,

• author = "Daniel Dorfmeister and Oliver Krauss",
• title = "Integrating {HeuristicLab} with Compilers and Interpreters for Non-Functional Code Optimization",
• booktitle = "GECCO 2020 Workshop on Evolutionary Computation Software Systems",
• year = "2020",
• month = jul # " 8-12",
• editor = "Stefan Wagner and Michael Affenzeller",
• publisher = "ACM",
• address = "Internet",
• pages = "1580--1588",
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Optimization, Compiler, Interpreter, Distributed Computing, Architecture, Metaheuristics, HeuristicLab, Truffle, Graal",
• isbn13 = "978-1-4503-7127-8",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp139s2-file1.pdf",
• DOI = "doi:10.1145/3377929.3398103",
• size = "9 pages",
• abstract = "Modern compilers and interpreters provide code optimizations during compile and run time, simplifying the development process for the developer and resulting in optimized software. These optimizations are often based on formal proof, or alternatively stochastic optimizations have recovery paths as backup. The Genetic Compiler Optimization Environment (GCE) uses a novel approach, using genetic improvement to optimize the run-time performance of code with stochastic machine learning techniques.

  we propose an architecture to integrate GCE, which directly integrates with low-level interpreters and compilers, with HeuristicLab, a high-level optimization framework that features a wide range of heuristic and evolutionary algorithms, and a graphical user interface to control and monitor the machine learning process. The defined architecture supports parallel and distributed execution to compensate long run times of the machine learning process caused by abstract syntax tree (AST) transformations. The architecture does not depend on specific operating systems, programming languages, compilers or interpreters.",

• notes = "API of ZeroMQ. Paranoid Pirate pattern. Java JIT JVM AST. Python, Ruby, JavaScript and C. MiniC. future PSO?

  Software Competence Center Hagenberg

  Also known as \cite{Dorfmeister:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398103}

  GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Daniel Dorfmeister Oliver Krauss

Citations