Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration originates in evolutionary principles in nature. Parallelization of genetic algorithms provides not only faster processing but also new and better solutions. Parallel genetic algorithms are also closer to real nature than their sequential counterparts. This paper describes the most used models of parallelization of genetic algorithms. Moreover, it provides the design and implementation in programming language Python. Finally, the implementation is verified in several test cases.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:377098 |
| Date | January 2018 |
| Creators | Tuleja, Martin |
| Contributors | Ilgner, Petr, Oujezský, Václav |
| Publisher | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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