2019 Evolutionary Algorithms Review

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Misc{DBLP:journals/corr/abs-1906-08870,

• author = "Andrew N. Sloss and Steven Gustafson",
• title = "2019 Evolutionary Algorithms Review",
• howpublished = "arXiv",
• year = "2019",
• month = jun # " 24",
• keywords = "genetic algorithms, genetic programming",
• eprint = "1906.08870",
• biburl = "https://dblp.org/rec/bib/journals/corr/abs-1906-08870",
• URL = "http://arxiv.org/abs/1906.08870",
• size = "38 pages",
• abstract = "Evolutionary algorithm research and applications began over 50 years ago. Like other artificial intelligence techniques, evolutionary algorithms will likely see increased use and development due to the increased availability of computation, more robust and available open source software libraries, and the increasing demand for artificial intelligence techniques. As these techniques become more adopted and capable, it is the right time to take a perspective of their ability to integrate into society and the human processes they intend to augment. In this review, we explore a new taxonomy of evolutionary algorithms and resulting classifications that look at five main areas: the ability to manage the control of the environment with limiters, the ability to explain and repeat the search process, the ability to understand input and output causality within a solution, the ability to manage algorithm bias due to data or user design, and lastly, the ability to add corrective measures. These areas are motivated by today's pressures on industry to conform to both societies concerns and new government regulatory rules. As many reviews of evolutionary algorithms exist, after motivating this new taxonomy, we briefly classify a broad range of algorithms and identify areas of future research.",
• notes = "Arm Inc., Bellevue, USA. MAANA Inc.",

}

Genetic Programming entries for Andrew N Sloss Steven M Gustafson

Citations