Paper Info
Title
Connecting Automatic Parameter Tuning, Genetic Programming as a Hyper-heuristic, and Genetic Improvement Programming.
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 improving them. These methods form a range 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), 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.
Year
DOI
Venue
2016
10.1145/2908961.2931728
GECCO (Companion)
Keywords
Field
DocType
Genetic Improvement (GI), Genetic Programming (GP)
Mathematical optimization,Scratch,Computer science,Genetic programming,Hyper-heuristic,Genetic representation,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
3
Name
Order
Citations
PageRank
John R. Woodward127417.48
Colin G. Johnson2933115.57
Alexander E.I. Brownlee314418.46