- author = "Nathaniel R. Kamrath and Aaron Scott Pope and Daniel R. Tauritz",
- title = "The Automated Design of Local Optimizers for Memetic Algorithms Employing Supportive Coevolution",
- year = "2020",
- editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik",
- isbn13 = "9781450371278",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
-
URL = "
https://doi.org/10.1145/3377929.3398132",
-
DOI = "
doi:10.1145/3377929.3398132",
- booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",
- pages = "1889--1897",
- size = "9 pages",
- keywords = "genetic algorithms, genetic programming, memetic algorithm, local optimization, automatic design, hyperheuristics",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
-
abstract = "One promising method of improving Evolutionary
Algorithm (EA) performance is to improve its fine
tuning capabilities by using an additional local
optimization operator in the evolutionary cycle. This
customization on the traditional EA is typically called
a Memetic Algorithm (MA). Adding the appropriate local
optimization algorithm can increase performance, while
a poor choice can decrease performance. Thus, some
knowledge is required to select the correct algorithm.
In many cases the optimal algorithm selection is not
known and it may not be static, but could change during
evolution.
This investigation combines a method of local optimizer evolution using Push Genetic Programming with a method of automatic, self-configuration called Supportive Coevolution. This combination creates a novel MA that coevolves local optimization operators with target fitness function solution candidates. Implementation methodology is shown and experimentation details with corresponding results are presented. Some additional parameters that were discovered for performance tuning are discussed along with a study of their impact on the algorithm's performance. Discussion of some interesting insights followed by some suggestions for further investigation are also provided. Results show the proposed technique can improve the performance of an EA by providing automatically configured, coevolved local optimization operators to a MA.",
- notes = "Also known as \cite{10.1145/3377929.3398132} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
