Collectively intelligent prediction in evolutionary multi-agent system

• Autorzy: Kijak J. , Martyna P. , Byrski A. , Faber Ł. , Piętak K. , Kisiel-Dorohinicki M.
• Języki publikacji: EN

Abstrakty

EN

In the paper a summary of our previously realized and published work connected with constructing collective intelligent evolutionary multi-agent systems for time series prediction, based on multi-layered perceptrons is shown. Besides recalling our past papers, we describe the whole concept, present an implementation in a contemporary, componentoriented software framework AgE 3.0 and we conduct a number of experiments, finding different optimal parametrization for the considered instances of the problems (popular Mackey-Glass chaotic time series). The paper may be useful for a practitioner willing to use our meatheuristic algorithm (EMAS) along with the idea of collective agent-based system in order to realize prediction tasks.

Słowa kluczowe

EN

evolutionary neural networks; agent-based computing; time series prediction; collective intelligence; metaheuristic optimization

PL

ewolucyjne sieci neuronowe; obliczenia agentowe; predykcja szeregów czasowych; inteligencja zbiorowa; optymalizacja metaheurystyczna

• Wydawca: Lodz University of Technology. Department of Microelectronics and Computer Science
• Czasopismo: International Journal of Microelectronics and Computer Science
• Rocznik: 2017
• Tom: Vol. 8, nr 3
• Strony: 85--96
• Opis fizyczny: Bibliogr. 21 poz., 1 il., rys., wykr.

Twórcy

autor

Kijak J.

• Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Martyna P.

• Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Byrski A.

• Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Faber Ł.

• Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Piętak K.

• Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Kisiel-Dorohinicki M.

• Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).