Evolvable Systems: From Biology to Hardware: 8th by Yerbol Sapargaliyev, Tatiana G. Kalganova (auth.), Gregory

By Yerbol Sapargaliyev, Tatiana G. Kalganova (auth.), Gregory S. Hornby, Lukáš Sekanina, Pauline C. Haddow (eds.)

This ebook constitutes the refereed court cases of the eighth overseas convention on Evolvable structures, ICES 2008, held in Prague, Czech Republic, in September 2008.

The 28 revised complete papers and 14 revised poster papers awarded have been conscientiously reviewed and chosen from fifty two submissions. The papers are prepared in topical sections on evolution of analog circuits, evolution of electronic circuits, hardware-software codesign and systems for adaptive platforms, evolutionary robotics, improvement, real-world purposes, evolutionary networking, evolvable man made neural networks, and transistor-level circuit evolution.

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Extra resources for Evolvable Systems: From Biology to Hardware: 8th International Conference, ICES 2008, Prague, Czech Republic, September 21-24, 2008. Proceedings

Sample text

Like with the two previously described approaches, an incremental approach is chosen for the evolution, such that one FUR can be evolved at a time. Each FUR is fed with all the training vectors Vt , and fitness is based on the row’s ability to give a positive (1) output for vectors v belonging to its own category (Cv = Cp ), while giving a negative (0) output for the rest of the vectors (Cv = Cp ). In the case of a positive output when Cv = Cp , the value A is added to the fitness sum. When Cv = Cp and the row gives a negative output (value 0), 1 is added to the fitness sum.

IEEE, Los Alamitos (1996) 7. : Hardware Evolution at Function Level. -P. ) PPSN 1996. LNCS, vol. 1141, pp. 62–71. Springer, Heidelberg (1996) 8. : Improvements to the Action Decision Rate for a Multi-Function Prosthetic Hand. In: Proceedings 1st International Symposium on Measurement, Analysis and Modeling of Human Functions (ISHF), pp. 84–89 (2001) 9. : Two-Step Incremental Evolution of a Digital Logic Gate Based Prosthetic Hand Controller. , Yasunaga, M. ) ICES 2001. LNCS, vol. 2210, pp. 1–13.

The architectures differ mainly in the building blocks used for evolution and their overall structure. We measure qualities such as classification accuracy and evolution speed. Generally, it is difficult to compare performance figures for EHW classifiers from literature, since data sets and evaluation schemes differ. We therefore propose and rely on a common data set obtained from the classification of EMG signals for PHC. The paper is structured as follows. Section 2 describes approaches to EHW classification, especially for the domain of PHC.

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