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Evolutionary computational synthesis of self-organizing systems

Published online by Cambridge University Press:  22 July 2014

James Humann ,
Newsha Khani  and
Yan Jin
James Humann
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
Newsha Khani
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
Yan Jin*
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
*
Reprint requests to: Yan Jin, Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Avenue, OHE430, Los Angeles, CA 90089-1453, USA. E-mail: yjin@usc.edu
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Abstract

A computational approach for the design of self-organizing systems is proposed that employs a genetic algorithm to efficiently explore the vast space of possible configurations of a given system description. To generate the description of the system, a two-field based model is proposed in which agents are assigned parameterized responses to two “fields,” a task field encompassing environmental features and task objects, and a social field arising from agent interactions. The aggregate effect of these two fields, sensed by agents individually, governs the behavior of each agent, while the system-level behavior emerges from the actions of and interactions among the agents. Task requirements together with performance preferences are used to compose system fitness functions for evolving functional and efficient self-organizing mechanisms. Case studies on the evolutionary synthesis of self-organizing systems are presented and discussed. These case studies focus on achieving system-level behavior with minimal explicit coordination among agents. Agents were able to collectively display flocking, exploration, and foraging through self-organization. The proposed two-field model was able to capture important features of self-organizing systems, and the genetic algorithm was able to generate self-organizing mechanisms by which agents could form task-based structures to fulfill functional requirements.

Keywords

Agent-Based ApproachComputational DesignDesign SynthesisGenetic AlgorithmSelf-Organization
Type
Research Article
Information
AI EDAM , Volume 28 , Issue 3: Computational Design Synthesis , August 2014 , pp. 259 - 275
Copyright
Copyright © Cambridge University Press 2014 

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