Reaching Meaningful Diversity with Speciation-Novelty in Genetic Improvement for Software

Created by W.Langdon from gp-bibliography.bib Revision:1.8458

@InProceedings{nemeth:2025:GECCO,

• author = "Zsolt Nemeth and Penn {Faulkner Rainford} and Barry Porter",
• title = "Reaching Meaningful Diversity with Speciation-Novelty in Genetic Improvement for Software",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "1017--1025",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, genetic improvement, genetic diversity, speciation, novelty search, fitness landscape",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726366",
• DOI = "doi:10.1145/3712256.3726366",
• size = "9 pages",
• abstract = "Genetic Improvement (GI) for software has been used in automated bug fixing and in automated performance improvement. Automated improvement has been targeted at multi-context problems, where one implementation variant might be best at one context, and another might be best at a different context. However, this application of GI generally requires a fresh improvement process for each new context, which can be computationally expensive. We propose a novel application of GI for multi-context problems, in which we aim for a diverse set of individuals in an initial training run for one context. We use a phenotypic speciation metric as a diversity indicator, allowing us to plot a diversity geometry through program search space. When a different context is introduced, as a new optimisation target for GI, we are able to select from one of these diverse individuals as a close starting point for fine-tuning. With a hash table implementation as an example to genetically improve, we show that we can exercise a high degree of control over population diversity, and that this diversity can be a useful starting point for finding individuals in successive alternative contexts.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Zsolt Nemeth Penelope Faulkner Rainford Barry Porter

Citations