Created by W.Langdon from gp-bibliography.bib Revision:1.7546
We propose MAGPIE, a unified software improvement framework. It provides a common edit sequence based representation that isolates the search process from the specific improvement technique, enabling a much simplified synergistic workflow. We provide a case study using a basic local search to compare compiler optimisation, algorithm configuration, and genetic improvement. We chose running time as our efficiency measure and evaluated our approach on four real-world software, written in C, C++, and Java.
Our results show that, used independently, all techniques find significant running time improvements: up to 25percent for compiler optimisation, 97percent for algorithm configuration, and 61percent for evolving source code using genetic improvement. We also show that up to 10percent further increase in performance can be obtained with partial combinations of the variants found by the different techniques. Furthermore, the common representation also enables simultaneous exploration of all techniques, providing a competitive alternative to using each technique individually.",
'Software is never done' 'Machine Automated General Performance Improvement through Evolution of software' 'Automated combination of the patches' (mutations) 'compiler and interpreter flag optimisation'
Figure 3: Cross-validation with k-folding and holdout.
p7 'only 10 training instances are actually used due to the very high evaluation cost'
StmtDelete, StmtReplace, StmtInsert, ConstantSet, ConstantUpdate, ParamSet.
fitness = CPU instructions (rather than CPU time).
GraalVM disabled use of 'compressed ordinary object pointers'.",
Genetic Programming entries for Aymeric Blot Justyna Petke