- author = "Hao Wang2 and Yitan Lou and Thomas Baeck",
- title = "Hyper-Parameter Optimization for Improving the Performance of Grammatical Evolution",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "2649--2656",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, grammatical evolution",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790026",
- size = "8 pages",
- abstract = "State-of-the-art Grammatical Evolution systems such as PonyGE2 have a number of hyper-parameters that control the behaviour of the internal evolutionary algorithm for evolving the representations of programs. In this paper, a variant of the efficient global optimization (EGO) algorithm is applied for optimizing these hyper-parameters of the PonyGE2-system. This approach is tested on four test problems used in the Grammatical Evolution community: StringMatch, symbolic regression (the `Vladislavleva-4' problem), bank note classification and the so-called Pymax task. The experimental results show that the average performance of the GE system is improved significantly (between 25percent and 168percent) on all of the test problems. In addition, the resulting overall best hyper-parameter settings are substantially different from the defaults used in PonyGE2.",
-
notes = "also known as \cite{8790026},
IEEE Catalog Number: CFP19ICE-ART",
