Browsing by Subject "evolutionary computation"

Now showing 1 - 4 of 4

Access status: Open Access ,
Evolutionary multi-agent computing in inverse problem
(Wydawnictwa AGH, 2013) Wróbel, Krzysztof; Torba, Paweł; Paszyński, Maciej; Byrski, Aleksander
This paper tackles the application of evolutionary multi-agent computing to solve inverse problems. High costs of fitness function call become a major difficulty when approaching these problems with population-based heuristics. However, evolutionary agent-based systems (EMAS) turn out to reduce the fitness function calls, which makes them a possible weapon of choice against them. This paper recalls the basics of EMAS and describes the considered problem (Step and Flash Imprint Lithography), and later, shows convincing results that EMAS is more effective than a classical evolutionary algorithm.
Access status: Open Access ,
Evolutionary multi-agent system with crowding factor and mass center mechanisms for multiobjective optimisation
(Wydawnictwa AGH, 2019) Różański, Mateusz; Siwik, Leszek
This work presents some additional mechanisms for Evolutionary Multi-Agent Systems for Multiobjective Optimisation trying to solve problems with population stagnation and loss of diversity. Those mechanisms reward solutions located in a less crowded neighborhood and on edges of the frontier. Both techniques have been described and also some preliminary results have been shown.
Access status: Open Access ,
Evolutionary multi-agent systems in non-stationary environments
(Wydawnictwa AGH, 2013) Kisiel-Dorohinicki, Marek
In this article, the performance of an evolutionary multi-agent system in dynamic optimization is evaluated in comparison to classical evolutionary algorithms. The starting point is a general introduction describing the background, structure and behavior of EMAS against classical evolutionary techniques. Then, the properties of energy-based selection are investigated to show how they may influence the diversity of the population in EMAS. The considerations are illustrated by experimental results based on the dynamic version of the well-known, high-dimensional Rastrigin function benchmark.
Access status: Restricted ,
Incorporated mathematical model and artifical neural network method for the electrochemical characterization of solid oxide fuel cells
(Data obrony: 2020-09-29) Gnatowski, Marek
Wydział Informatyki, Elektroniki i Telekomunikacji