Literature DB >> 27139941

Evolution of Swarming Behavior Is Shaped by How Predators Attack.

Randal S Olson, David B Knoester, Christoph Adami1.   

Abstract

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of domains of danger. Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.

Keywords:  Group behavior; density-dependent predation; digital evolutionary model; evolutionary algorithm; predator attack mode; predator-prey coevolution; selfish herd theory

Mesh:

Year:  2016        PMID: 27139941     DOI: 10.1162/ARTL_a_00206

Source DB:  PubMed          Journal:  Artif Life        ISSN: 1064-5462            Impact factor:   0.667


  8 in total

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3.  Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

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4.  Birds of a feather flock together: Insights into starling murmuration behaviour revealed using citizen science.

Authors:  Anne E Goodenough; Natasha Little; William S Carpenter; Adam G Hart
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5.  Deep-Reinforcement Learning-Based Co-Evolution in a Predator-Prey System.

Authors:  Xueting Wang; Jun Cheng; Lei Wang
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6.  Collective predator evasion: Putting the criticality hypothesis to the test.

Authors:  Pascal P Klamser; Pawel Romanczuk
Journal:  PLoS Comput Biol       Date:  2021-03-15       Impact factor: 4.475

7.  Confined System Analysis of a Predator-Prey Minimalistic Model.

Authors:  Siddhant Mohapatra; Pallab Sinha Mahapatra
Journal:  Sci Rep       Date:  2019-08-02       Impact factor: 4.379

8.  Evolving flocking in embodied agents based on local and global application of Reynolds' rules.

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Journal:  PLoS One       Date:  2019-10-29       Impact factor: 3.240

  8 in total

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