Biological Insights on Grammar-Structured Mutations Improve Fitness and Diversity

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{tiso:2023:GECCO,

• author = "Stefano Tiso and Pedro Carvalho and Nuno Lourenco and Penousal Machado",
• title = "Biological Insights on {Grammar-Structured} Mutations Improve Fitness and Diversity",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "558--567",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, mutation, optimizers, grammar-guided genetic programming",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590472",
• size = "10 pages",
• abstract = "Grammar-Guided Genetic Programming (GGGP) employs a variety of concepts from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we propose a new mutation approach called Facilitated Mutation (FM) that is based on the theory of Facilitated Variation. We evaluate the performance of FM on the evolution of neural network optimizers for image classification, a relevant task in Evolutionary Computation, with important implications for the field of Machine Learning. We compare FM and FM combined with crossover (FMX) against a typical mutation approach to assess the benefits of the approach. We find that FMX provides statistical improvements in key metrics, creating a superior optimizer overall (+0.5\% average test accuracy), improving the average quality of solutions (+53\% average population fitness), and discovering more diverse high-quality behaviors (+523 high-quality solutions discovered on average). Additionally, FM and FMX reduce the number of fitness evaluations in an evolutionary run, reducing computational costs. FM's implementation cost is minimal and the approach is theoretically applicable to any algorithm where genes are associated witha grammar non-terminal, making this approach applicable in many existing GGGP systems.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Stefano Tiso Pedro Carvalho Nuno Lourenco Penousal Machado

Citations