Evolutionary computation variants for cooperative spatial coordination

G. Yannakakis; J.M. Levine; J. Hallam

doi:10.1109/CEC.2005.1555035

Evolutionary computation variants for cooperative spatial coordination

G. Yannakakis, J.M. Levine, J. Hallam

Computer And Information Sciences

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Abstract

This paper presents a comparative study between genetic and probabilistic search approaches of evolutionary computation. They are both applied for optimizing the behavior of multiple neural-controlled homogeneous agents whose spatial coordination tasks can only be successfully achieved through emergent cooperation. Both approaches demonstrate effective solutions of high performance; however, the genetic search approach appears to be both more robust and computationally preferred for this multi-agent case study.

Original language	English
Title of host publication	Proceedings of the 2005 IEEE Congress on Evolutionary Computation
Publisher	IEEE
Pages	2715-2722
Number of pages	7
Volume	3
ISBN (Print)	0-7803-9363-5
DOIs	https://doi.org/10.1109/CEC.2005.1555035
Publication status	Published - Sept 2005

Keywords

evolutionary computation
genetic algorithms
multi-agent systems
search problems

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10.1109/CEC.2005.1555035

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abstract = "This paper presents a comparative study between genetic and probabilistic search approaches of evolutionary computation. They are both applied for optimizing the behavior of multiple neural-controlled homogeneous agents whose spatial coordination tasks can only be successfully achieved through emergent cooperation. Both approaches demonstrate effective solutions of high performance; however, the genetic search approach appears to be both more robust and computationally preferred for this multi-agent case study.",

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AU - Levine, J.M.

AU - Hallam, J.

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AB - This paper presents a comparative study between genetic and probabilistic search approaches of evolutionary computation. They are both applied for optimizing the behavior of multiple neural-controlled homogeneous agents whose spatial coordination tasks can only be successfully achieved through emergent cooperation. Both approaches demonstrate effective solutions of high performance; however, the genetic search approach appears to be both more robust and computationally preferred for this multi-agent case study.

KW - evolutionary computation

KW - genetic algorithms

KW - multi-agent systems

KW - search problems

UR - http://dx.doi.org/10.1109/CEC.2005.1555035

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DO - 10.1109/CEC.2005.1555035

M3 - Chapter

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VL - 3

SP - 2715

EP - 2722

BT - Proceedings of the 2005 IEEE Congress on Evolutionary Computation

PB - IEEE

ER -

Evolutionary computation variants for cooperative spatial coordination

Abstract

Keywords

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