Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games

Autor:	Feldmeier, Patric, Fraser, Gordon
Rok vydání:	2024
Předmět:	Computer Science - Software Engineering Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
DOI:	10.1145/3663532.3664467
Popis:	As games challenge traditional automated white-box test generators, the Neatest approach generates test suites consisting of neural networks that exercise the source code by playing the games. Neatest generates these neural networks using an evolutionary algorithm that is guided by an objective function targeting individual source code statements. This approach works well if the objective function provides sufficient guidance, but deceiving or complex fitness landscapes may inhibit the search. In this paper, we investigate whether the issue of challenging fitness landscapes can be addressed by promoting novel behaviours during the search. Our case study on two Scratch games demonstrates that rewarding novel behaviours is a promising approach for overcoming challenging fitness landscapes, thus enabling future research on how to adapt the search algorithms to best use this information. Comment: 6 pages, 5 figures, Foundations of Applied Software Engineering for Games Workshop (FaSE4Games 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.04985 Zobrazit plný text záznamu View this record from Arxiv