Abstract
The "Royal Road" objective function was proposed by J. H. Holland in 1993 as a very hard benchmark problem for evolutionary algorithms. Generally, it belongs to the class of combinatorial optimization problems. In our work, we solve the problem in a distributed way by assigning each decision variable to an autonomous agent. The resulting multi-agent system "COHDA" forms a self-organizing complex system, where the global solution emerges from local interactions. By applying the XOR instance generator introduced by S. Yang in 2003, we are able to pertubate the system during runtime by modifying the objective function. This allows us to examine the robustness of COHDA against dynamic objectives. Here, we focus on the influence of runtime memory, which comprises the beliefs of each agent, on the adaptivity capabilities of the agents after an occured pertubation. We show that the final fitness values produced by the system do not suffer from a dynamic objective function, and are not influenced by the availability of an agents’ runtime memory. The time needed by the system to adapt to such a pertubation, however, significantly increases if the agents’ beliefs are being distorted. We conclude that, in terms of solution quality, COHDA is very robust against dynamic objective functions. With respect to adaptation speed, the heuristic benefits from the availability of runtime memory.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Talbi, E.G.: Metaheuristics. John Wiley & Sons Inc., Hoboken (2009)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization. ACM Computing Surveys 35, 268–308 (2003)
Hinrichs, C., Lehnhoff, S., Sonnenschein, M.: A decentralized heuristic for multiple-choice combinatorial optimization problems. In: Operations Research Proceedings. Springer (2012, to appear). http://www.springer.com/series/722
Hinrichs, C., Sonnenschein, M., Lehnhoff, S.: Evaluation of a self-organizing heuristic for interdependent distributed search spaces. In: Filipe, J., Fred, A.L.N. (eds) Proceedings of the 5th International Conference on Agents and Artificial Intelligence, ICAART 2013, vol. 1. SciTePress, Barcelona, February 15–18, 2013 (accepted)
Jones, T.: A Description of Holland’s Royal Road Function. Evolutionary Computation 2, 409–415 (1994)
Yang, S., Kingdom, U., Section, F.: Non-stationary problem optimization using the primal-dual genetic algorithm. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 3, pp. 2246–2253. IEEE Press, New York (2003)
Rechenberg, I.: Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Fommann-Holzboog, Stuttgart (1973)
Smart Nord (2012). http://www.smartnord.de
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hinrichs, C., Lehnhoff, S., Sonnenschein, M. (2016). Paving the Royal Road for Complex Systems: On the Influence of Memory on Adaptivity. In: Wunner, G., Pelster, A. (eds) Selforganization in Complex Systems: The Past, Present, and Future of Synergetics. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-27635-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-27635-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27633-5
Online ISBN: 978-3-319-27635-9
eBook Packages: EngineeringEngineering (R0)