Is Evolutionary Computation Evolving Fast Enough?

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Kendall:2018:ieeeCIM,

• author = "Graham Kendall",
• journal = "IEEE Computational Intelligence Magazine",
• title = "Is Evolutionary Computation Evolving Fast Enough?",
• year = "2018",
• volume = "13",
• number = "2",
• pages = "42--51",
• month = may,
• notes = "special issue on Automated Design of Machine Learning and Search Algorithms",
• keywords = "genetic algorithms, genetic programming, Artificial intelligence, Commercialization, Evolutionary computation, Job shop scheduling",
• ISSN = "1556-603X",
• URL = "http://eprints.nottingham.ac.uk/id/eprint/49527",
• URL = "http://eprints.nottingham.ac.uk/49527/article.pdf",
• DOI = "doi:10.1109/MCI.2018.2807019",
• size = "10 pages",
• abstract = "Evolutionary Computation (EC) has been an active research area for over 60 years, yet its commercial/home uptake has not been as prolific as we might have expected. By way of comparison, technologies such as 3D printing, which was introduced about 35 years ago, has seen much wider uptake, to the extent that it is now available to home users and is routinely used in manufacturing. Other technologies, such as immersive reality and artificial intelligence have also seen commercial uptake and acceptance by the general public. In this paper we provide a brief history of EC, recognizing the significant contributions that have been made by its pioneers. We focus on two methodologies (Genetic Programming and Hyper-heuristics), which have been proposed as being suitable for automated software development, and question why they are not used more widely by those outside of the academic community. We suggest that different research strands need to be brought together into one framework before wider uptake is possible. We hope that this position paper will serve as a catalyst for automated software development that is used on a daily basis by both companies and home users.",
• notes = "Also known as \cite{8335847}",

}

Genetic Programming entries for Graham Kendall

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