Real-World Applications of Evolutionary Computing: - download pdf or read online

By Joe Dumoulin, James A. Foster, James F. Frenzel, Steve McGrew (auth.), Stefano Cagnoni (eds.)

ISBN-10: 3540455612

ISBN-13: 9783540455615

ISBN-10: 3540673539

ISBN-13: 9783540673538

The more and more energetic eld of Evolutionary Computation (EC) offers val- ble instruments, encouraged by means of the speculation of common choice and genetic inheritance, to challenge fixing, computer studying, and optimization in lots of real-world app- cations. regardless of a few early intuitions approximately EC, that may be dated again to the - vention of desktops, and a greater formal de nition of EC, made within the Sixties, the hunt for real-world purposes of EC simply begun within the overdue Eighties. The dramatic elevate in laptop performances within the final decade of the twentieth c- tury gave upward push to a good suggestions method: EC options turned a growing number of appropriate, stimulating the expansion of curiosity of their examine, and permitting, in flip, new strong EC paradigms to be devised. In parallel with new theoretical effects, the variety of elds to which EC is being utilized is expanding daily, in addition to the complexity of functions and alertness domain names. particularly, industrially correct elds, equivalent to sign and snapshot processing, laptop imaginative and prescient, trend reputation, commercial keep an eye on, telecommunication, scheduling and timetabling, and aerospace engineering are applying EC options to resolve advanced real-world problems.

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Additional resources for Real-World Applications of Evolutionary Computing: EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight Edinburgh, Scotland, UK, April 17, 2000 Proceedings

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An evolutionary algorithm to optimize signal representations by adaptively choosing a basis depending on the signal is presented. We show how evolutionary algorithms can be exploited to search larger waveform dictionaries for best basis selection than those considered in current standard approaches. 1 Introduction In order to facilitate an empirical comparison of the performance of di erent evolutionary algorithms a test environment must be provided. Traditionally, sets of test functions with speci c topological properties, commonly known as tness landscapes, have been proposed by several authors to be used in performance benchmarking.

R. Tonjes, S. Growe, J. -E. Liedtke, Knowledge-based interpretation of remote sensing images using semantic nets", Photogrammetric Engineering & Remote Sensing, vol. 65, no. 7, pp. 811 821, jul 1999. 6. J. Bala, K. DeJong, and P. Pachowicz, Using genetic algorithms to improve the performance of classi cation rules produced by symbolic inductive methods", in S c e n e In te rp re ta tio n Fig. 4. 4 3 Best Labeling of the Gulf Stream found by the GA Proceedings of 6th International Symposium Methodologies for Intelligent Systems ISMIS'91, Z.

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Real-World Applications of Evolutionary Computing: EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight Edinburgh, Scotland, UK, April 17, 2000 Proceedings by Joe Dumoulin, James A. Foster, James F. Frenzel, Steve McGrew (auth.), Stefano Cagnoni (eds.)


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