Evolutionary Multi-Agent Computing in Inverse Problems

Krzysztof Wróbel, Paweł Torba, Maciej Paszyński, Aleksander Byrski

Abstract


The paper tackles the application of evolutionary multi-agent computing to solving inverse problems. High costs of fitness function call become a major difficulty when approachingthese problems with population-based heuristics, however evolutionary agent-based systems (EMAS)turn out to reduce the fitness function calls, which makes them a  possible weapon of choicefor them. The paper recalls the basics of EMAS and describes the considered problem (Step and Flash Imprint Lithography),later showing convincing results, that EMAS turns out to be more effective than classical evolutionary algorithm.

Full Text:

PDF

References


Bäck T., Fogel D., Michalewicz Z., editors. Handbook of Evolutionary Computation. IOP Publishing and Oxford University Press, 1997.

Byrski A., Drezewski R., Siwik L., Kisiel-Dorohinicki M.:. Evolutionary multi-agent systems. The Knowledge Engineering Review, Accepted for publication, 2012.

Cetnarowicz K., Kisiel-Dorohinicki M., Nawarecki E.:. The application of evolution process in multi-agent world (MAW) to the prediction system. In Tokoro M., editor, Proc. of the 2nd Int. Conf. on Multi-Agent Systems (ICMAS’96). AAAI Press, 1996.

Cetnarowicz K.:. Evolution in multi-agent world = genetic algorithms + aggregation + escape. In 7th European Workshop on Modelling Autonomous Agents in a Multi- Agent World (MAAMAW’96). Vrije Universiteit Brussel, Artificial Intelligence Laboratory, 1996.

Chen S.-H., Kambayashi Y., Sato H.:. Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies. IGI Global, 2011.

Demkowicz L., Kurtz J., Pardo D., Paszynski M., Rachowicz W., Zdunek A.:. Computing with hp-adaptive finite elements. In Frontiers: Three Dimensional Elliptic and Maxwell Problems with Applications. Chapman & Hall/CRC, 2007.

Hughes T.:. The Finite Element Method. Linear Statics and Dynamics Finite Element Method Analysis. Dover, 2000.

Kisiel-Dorohinicki M.:. Agent-oriented model of simulated evolution. In Grosky W. I., Plasil F., editors, SofSem 2002: Theory and Practice of Informatics, volume 2540 of LNCS. Springer, 2002.

Lutz M.:. Programming Python. O’Reilly Media, 2011.

M.E. C.:. Step and Flash Imprint Lithograpy: A Low Pressure, Room Temperature Nonoimprint Lithography. PhD thesis, The University of Texas in Austin, 2000.

Michalewicz Z.:. Genetic Algorithms Plus Data Structures Equals Evolution Programs. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1994.

Paszynski M., Barabasz B., Schaefer R.:. Efficient adaptive strategy for solving inverse problems. Lecture Notes in Computer Science, 4488:342–349, 2007.

Paszynski M., Demkowicz L.:. Parallel, fully automatic hp-adaptive 3d finite element package. Engineering with Computers, 22(3):255–276, 2006.

Paszynski M., Romkes A., Collister E., Meiring J., Demkowicz L., Willson C.:. On the modeling of step and flash imprint lithography. Technical report, ICES Report 05-38, 2005.

Sarker R., Ray T.:. Agent-Based Evolutionary Search. Springer, 2010.

Schaefer R., Kołodziej J.:. Genetic search reinforced by the population hierarchy. Foundations of Genetic Algorithms, 7, 2003.

Wolfram S.:. A New Kind of Science. Wolfram Media, 2002.

Wooldridge M.:. An Introduction to Multiagent Systems. John Wiley & Sons, 2009.

Zhong W., Liu J., Xue M., Jiao L.:. A multiagent genetic algorithm for global numerical optimization. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(2):1128–1141, 2004.




DOI: https://doi.org/10.7494/csci.2013.14.3.367

Refbacks

  • There are currently no refbacks.