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https://doi.org/10.2498/cit.2003.04.02

A Genetic Algorithm With Self-Generated Random Parameters

Sonja Novkovic
Davor Sverko

Puni tekst: engleski, pdf (272 KB) str. 271-283 preuzimanja: 441* citiraj
APA 6th Edition
Novkovic, S. i Sverko, D. (2003). A Genetic Algorithm With Self-Generated Random Parameters. Journal of computing and information technology, 11 (4), 271-283. https://doi.org/10.2498/cit.2003.04.02
MLA 8th Edition
Novkovic, Sonja i Davor Sverko. "A Genetic Algorithm With Self-Generated Random Parameters." Journal of computing and information technology, vol. 11, br. 4, 2003, str. 271-283. https://doi.org/10.2498/cit.2003.04.02. Citirano 23.09.2020.
Chicago 17th Edition
Novkovic, Sonja i Davor Sverko. "A Genetic Algorithm With Self-Generated Random Parameters." Journal of computing and information technology 11, br. 4 (2003): 271-283. https://doi.org/10.2498/cit.2003.04.02
Harvard
Novkovic, S., i Sverko, D. (2003). 'A Genetic Algorithm With Self-Generated Random Parameters', Journal of computing and information technology, 11(4), str. 271-283. https://doi.org/10.2498/cit.2003.04.02
Vancouver
Novkovic S, Sverko D. A Genetic Algorithm With Self-Generated Random Parameters. Journal of computing and information technology [Internet]. 2003 [pristupljeno 23.09.2020.];11(4):271-283. https://doi.org/10.2498/cit.2003.04.02
IEEE
S. Novkovic i D. Sverko, "A Genetic Algorithm With Self-Generated Random Parameters", Journal of computing and information technology, vol.11, br. 4, str. 271-283, 2003. [Online]. https://doi.org/10.2498/cit.2003.04.02

Sažetak
In this paper we present a version of genetic algorithm (GA) where parameters are created by the GA itself, rather than predetermined by the programmer. Chromosome portions which do not translate into fitness (“genetic residual”) are given function to diversify control parameters for the GA,providing random parameter setting along the way, and doing away with fine-tuning of probabilities of crossover and mutation. We test the algorithm on Royal Road functions to examine the difference between our version (GAR) and the simple genetic algorithm (SGA) in the speed of discovering schema and creating building blocks. We also look at the usefulness of other standard improvements, such as non-coding segments, elitist selection and multiple crossover on the evolution of schema.

Hrčak ID: 44735

URI
https://hrcak.srce.hr/44735

Posjeta: 607 *