Automated design of algorithms and genetic improvement: contrast and commonalities

Citation

Haraldsson SO & Woodward JR (2014) Automated design of algorithms and genetic improvement: contrast and commonalities. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation. GECCO 2014: 2014 Annual Conference on Genetic and Evolutionary Computation, Vancouver, BC, Canada, 12.07.2014-16.07.2014. New York: ACM, pp. 1373-1380. https://doi.org/10.1145/2598394.2609874

Abstract
Automated Design of Algorithms (ADA) and Genetic Improvement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolutionary search methods and successfully produce human competitive programs. ADA and GI are used for improving functional properties such as quality of solution and non-functional properties, e.g. speed, memory and, energy consumption. Only GI of the two has been used to fix bugs, probably because it is applied globally on the whole source code while ADA typically replaces a function or a method locally. While GI is applied directly to the source code ADA works ex-situ, i.e. as a separate process from the program it is improving. Although the methodologies overlap in many ways they differ on some fundamentals and for further progress to be made researchers from both disciplines should be aware of each other's work.

Keywords
Automated Design of Algorithms (ADA); Genetic Improvement (GI); Genetic Programming (GP); Abstract Syntax Tree (AST); Genetic Algorithm (GA); Search Based Software Engineering (SBSE); Genetic Algorithm (GA)