Paper Info
Title
On the sensitivity analysis of cartesian genetic programming hyper-heuristic
Abstract
The research on Genetic-Programming Hyper-heuristics (GPHH) for automated design of heuristic-based methods has been very active over the last years. Most efforts have focused on the development or improvements of GPHH methods or their applications to different problem domains. Studies that target on the analysis and understanding of the GPHH behavior are still scarce, despite their relevance for easing the application of GPHH in practice and for advancing this research field. In order to advance the body of knowledge on the understanding of GPHH behavior, this paper aims at analyzing the impact of its parameters on the evolution, diversity, and quality of generated algorithms. In particular, a Cartesian Genetic-Programming hyper-heuristic (CGPHH) applied to an NP-Complete problem of production planning (multi-level capacitated lot-sizing problem) is considered. The effects of five parameters on response variables that reflect various aspects of the CGPHH behavior, such as diversity and quality of generated algorithms, are analyzed based on a full-factorial design of experiments. Results indicate that mainly three factors affect the CGPHH behavior in different ways: mutation rate, the CGP representation, and the number of graph nodes. Nonetheless, the CGPHH still generates competitive algorithms, despite the changes applied to its parameters.
Year
DOI
Venue
2020
10.1145/3377929.3398142
GECCO '20: Genetic and Evolutionary Computation Conference Cancún Mexico July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7127-8
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Luis Filipe de Araujo Pessoa100.34
bernd hellingrath26318.39
Fernando B. de Lima Neto300.34