Simulation of the Evolution of Aging: Effects of Aggression and Kin-Recognition

Krivenko, Svetlana; Burtsev, Mikhail

doi:10.1007/978-3-540-74913-4_9

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…So far, the inspiration has largely passed one way: from biology to algorithms. The field of artificial life gives some hint of traffic the other way, especially through agent-based simulations validating hypotheses about aspects of the interactions of population dynamics and evolution (Krivenko & Burtsev, 2007). However, the fields of optimisation and machine learning, in particular, have well-developed mathematical theories.…”

Section: Why Nature?mentioning

confidence: 99%

Nature That Breeds Solutions

Chiong

Neri

McKay

2012

International Journal of Signs and Semiotic Systems

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Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful techniques, algorithms and computational applications for dealing with large, complex and dynamic real world problems. In this article, the authors discuss why nature-inspired solutions have become increasingly important and favourable for tackling the conventionally-hard problems. They also present the concepts and background of some selected examples from the domain of natural computing, and describe their key applications in business, science and engineering. Finally, the future trends are highlighted to provide a vision for the potential growth of this field.

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Section: Why Nature?mentioning

confidence: 99%

Nature That Breeds Solutions

Chiong

Neri

McKay

2012

International Journal of Signs and Semiotic Systems

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“…Artificial evolving systems are used to build complex systems that expose intellectual behavior and study the link between intellectuality and complexity [13]. Alife systems are plau-sible playground to explore the mechanisms of adaptation: general evolving system features such as speciation ( [2], [13]), aging ( [14]), cooperation ( [15]), developmental processes in artificial systems [16], and learning.…”

Section: Introductionmentioning

confidence: 99%

Genotype dynamic for agent neuroevolution in artificial life model

Zavertanyy

Makarenko

2017

SRIT

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Cooperation behavior is one of the most used and spread Multi-agent system feature. In some cases emergence of this behaviour can be characterized by division of population on co-evolving subpopulations [1], [2]. Group interaction can take not only antagonistic conflict form but also genetic drift that results with strategies competition and assimilation [3]. In this work we demonstrate different relation between agent grouping and they behavior strategies. We use approach proposed in work [2] methodology of agent genotype dynamic tracking, due to this approach the evolving population can be presented in genotype space as a cloud of points where each point corresponds to one individual. In current work consider the movement of population centroid-the center of the genotype cloud. Analysis of such trajectories can shad the light on the regimes of population existence and genesis.

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Advances in Computational Systems Biology

Lodhi

2010

Elements of Computational Systems Biology

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Simulation of the Evolution of Aging: Effects of Aggression and Kin-Recognition

Cited by 3 publications

References 15 publications

Nature That Breeds Solutions

Nature That Breeds Solutions

Genotype dynamic for agent neuroevolution in artificial life model

Advances in Computational Systems Biology

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