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Intelligent Engineering Systems through Artificial Neural Networks

Editor
Cihan H. Dagli
Cihan H. Dagli
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K. Mark Bryden
K. Mark Bryden
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Steven M. Corns
Steven M. Corns
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Mitsuo Gen
Mitsuo Gen
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Kagan Tumer
Kagan Tumer
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Gürsel Süer
Gürsel Süer
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ISBN:
9780791802953
No. of Pages:
636
Publisher:
ASME Press
Publication date:
2009

This study builds on previous work that classifies evolutionary computation problems via graph-based taxonomic techniques. A new collection of graphs that permit the measurement of small population effects are employed as well as a new distance measure that incorporates information on the variability of problem hardness into the taxonomic process. The new set of graphs and improved distance measure on problems are tested on a diverse set of string-based optimization problems including two new types of problems: fragmentation and K-max. The familiar SAW, one-max, and royal road problems are also included. It is found that problem difficulty, although explicitly removed from the information used to classify the problems, is nevertheless detected by the taxonomic technique. The problems appearing in earlier studies are shown to retain their diversity of behavior, confirming that the improvements to the problem classification system did not degrade a valuable feature of the earlier system.

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