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Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

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Evolution of synchronization and desynchronization in digital organisms.

David B Knoester¹, Philip K McKinley

¹Department of Computer Science and Engineering, Michigan State University, Easting Lansing, MI, USA. dk@cse.msu.edu

Artificial Life

|November 20, 2010

Summary

Digital evolution with group selection enabled digital organisms to evolve synchronization and desynchronization algorithms. These evolved behaviors mimic natural systems and are robust to message loss.

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Area of Science:

Evolutionary computation
Complex systems
Computational biology

Background:

Synchronization and desynchronization are key temporal behaviors in natural and artificial systems.
Digital evolution provides a platform for studying the emergence of complex behaviors.
Group selection promotes cooperation and can influence the evolution of collective behaviors.

Purpose of the Study:

To investigate the evolution of synchronization and desynchronization in digital organisms.
To explore the role of group selection in fostering cooperative temporal behaviors.
To compare evolved digital behaviors with natural models like firefly synchronization.

Main Methods:

Utilized digital evolution with self-replicating computer programs in a defined environment.
Implemented group selection to link individual survival to group success.
Introduced a neighbor-based communication mechanism inspired by the firefly model.

Main Results:

Digital organisms evolved robust algorithms for synchronization and desynchronization.
Evolved synchronization behavior closely resembles the Ermentrout model of firefly synchronization.
The evolved algorithms demonstrated resilience to message loss.

Conclusions:

Digital evolution can successfully evolve complex temporal behaviors like synchronization.
Group selection is a viable mechanism for promoting cooperative behaviors in evolving systems.
This study offers insights into both distributed computing and biological synchronization mechanisms.