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Uporaba genetskih algoritmov za optimizacijo parametrov adaptivnega modela skupinskega obnašanja ob napadu plenilca
ID GRBEC, DEJAN (Author), ID Lebar Bajec, Iztok (Mentor) More about this mentor... This link opens in a new window

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PID: 20.500.12556/rul/7d074ce2-2d9e-4e99-820e-73189975ef86

Abstract
Raziskovanje skupinskega vedenja temelji na opazovanju in odkrivanju lastnosti vedenja večjih skupin med seboj podobnih entitet. Najbolj raziskovane vrste skupin so jate ptic in jate rib. Za dana primera obstaja mnogo različnih modelov skupinskega vedenja, ki so jih raziskovalci uporabili pri raziskavah različnih lastnosti, kot so: prenos informacije med entitetami, koristnost združevanja v skupino (obramba pred plenilcem, hranjenje), proces odločanja skupine v različnih situacijah in režimi vedenja skupine. V naši magistrski nalogi smo se osredotočili na različne režime vedenja skupin, prehode med njimi in njihovo koristnost pri napadu plenilca. V raziskavi uporabljen model vedenja skupine temelji na članku Couzina in sod. [doi:10.1006/jtbi.2002.3065], ki so ugotovili, da s prilagajanjem radija območja poravnave dosežemo štiri različne režime vedenja skupine: neusklajeno prepletanje, kroženje okrog praznega jedra, delno usklajeno gibanje in močno usklajeno gibanje. Neusklajeno prepletanje (angl. swarming) je najbolj značilno za roje insektov, kroženja okrog praznega jedra (angl. torus) se v posebnih primerih poslužujejo jate rib, delno in močno usklajeno gibanje pa je primarna vrsta gibanja tako jate ptic kot rib. Z naštetimi režimi vedenja skupine smo želeli preveriti, katera vrsta vedenja je najbolj optimalna protiobramba za različne taktike napada plenilca ter ali prehodi med naštetimi režimi pripomorejo k bolj učinkoviti obrambi pred njim.

Language:Slovenian
Keywords:Jata ptic, jata rib, skupinsko vedenje, genetski algoritmi, evolucija, adaptivni model vedenja, režimi vedenja, napad plenilca, odziv na plenilca, taktike napada.
Work type:Master's thesis/paper
Organization:FRI - Faculty of Computer and Information Science
Year:2016
PID:20.500.12556/RUL-80850 This link opens in a new window
Publication date in RUL:07.03.2016
Views:1695
Downloads:557
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Secondary language

Language:English
Title:Using genetic algorithms to optimize the parameters of the adaptive model of collective behavior of the predator attack
Abstract:
Collective behaviour research focuses on behaviour properties of large groups of similar entities. The most studied collective behaviour species are flocks of birds and schools of fish. There exist many different computational models of collective behaviour, which researchers used to investigate various properties of collective behaviour like: transfer of information across the group, benefits of grouping (defence against predator, foraging), group decision-making process and group behaviour types. In this thesis we focused on various group behaviour types, transitions between them and their benefits when the prey group is exposed to predator attacks. In our study we implemented the collective behaviour computational model originally presented by Couzin et al. [doi:10.1006/jtbi.2002.3065], who discovered four different behaviour types by adjusting the radius of the orientation zone: swarming, rotating around an empty core, dynamic parallel movement and highly parallel movement. Swarming is typical for insects, rotating around an empty core (torus) is in special cases exhibited by fish schools and dynamic/highly parallel movement is primary associated with bird flocks and fish schools. With known behaviour types and using various attack tactics we tried to verify which behaviour type is the most efficient defence against predator attacks, and whether transitions between these behaviour types contribute to a more efficient defence.

Keywords:Flocking, schooling, collective behaviour, genetic algorithms, evolution, adaptive model, behaviour types, predator attack, prey response, attack tactics.

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