Autor: Kisiel-Dorohinicki, Marek - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Fine tuning of agent-based evolutionary computing
Autorzy:: Mizera, Michal
Nowotarski, Pawel
Byrski, Aleksander
Kisiel-Dorohinicki, Marek
Powiązania:: https://bibliotekanauki.pl/articles/91820.pdf
Data publikacji:: 2019
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: multi-agent systems
metaheuristics
evolutionary computing
Opis:: Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 2; 81-97
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Population diversity in ant-inspired optimization algorithms
Autorzy:: Byrski, Aleksander
Węgrzyński, Krzysztof
Radwański, Wojciech
Starzec, Grażyna
Starzec, Mateusz
Bargiel, Monika
Urbańczyk, Aleksandra
Kisiel-Dorohinicki, Marek
Powiązania:: https://bibliotekanauki.pl/articles/2097962.pdf
Data publikacji:: 2021
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: ant colony optimization
diversity measuring
exploitation and exploration balance
metaheuristics
Opis:: Measuring the diversity in evolutionary algorithms that work in real-value search spaces is often computationally complex, but it is feasible; however, measuring the diversity in combinatorial domains is practically impossible. Nevertheless, in this paper we propose several practical and feasible diversitymeasurement techniques that are dedicated to ant colony optimization algorithms, leveraging the fact that we can focus on a pheromone table even though an analysis of the search space is at least an NP problem where the direct outcomes of the search are expressed and can be analyzed. Besides sketching out the algorithms, we apply them to several benchmark problems and discuss their efficacy.
Źródło:: Computer Science; 2021, 22 (3); 297-320
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

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