Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming

Citation

Woodward J, Johnson C & Brownlee A (2016) Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming. In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. GECCO 2016: Genetic and Evolutionary Computation Conference, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1357-1358. https://doi.org/10.1145/2908961.2931728

Abstract
Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improvement them.  These methods form a continuum of methodologies, from the “limited” ability to change (for example only the parameters) to the “complete” ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH) to automatically design algorithms, and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approaches

Keywords
Genetic Improvement (GI); Genetic Programming (GP)

People

Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division