DocumentCode :
1231454
Title :
Evolutionary computation benchmarking repository [Developmental Tools]
Author :
Sendhoff, Bernhard ; Roberts, Mark ; Yao, Xin
Author_Institution :
Honda Res. Inst. Eur.
Volume :
1
Issue :
4
fYear :
2006
Firstpage :
50
Lastpage :
60
Abstract :
Evolutionary computation has been used with great success for the solution of hard optimization problems. Theoretical analysis, although important in its own right, e.g. for understanding underlying phenomena and characteristics of evolutionary search, can only provide upper and/or lower bounds of performance estimation of evolutionary algorithms for hard optimization problems. In practice, empirical analysis is the most important means to assess and compare the performance of algorithms. In order to facilitate this fair and transparent comparison, the Evolutionary Computation Benchmarking Repository (EvoCoBR) by M. Roberts et al. (2006) has been designed and put into operation in a beta version and trial phase. The aim is to create a central Web-based repository for storing detailed benchmark problem descriptions. However, with EvoCoBR we want to go one step further and archive, along with the problem description, a list of references to previously achieved results and the best result so far. This enables researchers to more easily see how their results compare to results in the literature. EvoCoBR will also invite researchers to submit and archive the programs that produced those results. EvoCcBR´s architecture enables the entire evolutionary computation community to contribute and own the Web-based archive. Its contents will be submitted by researchers and practitioners, and openly accessible by all. In other words, the EvoCoBR design defines the framework that needs to be filled by the evolutionary computation community for the evolutionary computation community
Keywords :
evolutionary computation; information retrieval systems; Evolutionary Computation Benchmarking Repository; Web-based archive; Web-based repository; evolutionary computation community; hard optimization problems; Acoustic noise; Algorithm design and analysis; Computer architecture; Databases; Engines; Evolutionary computation; Neural networks; Performance analysis; Security; Testing;
fLanguage :
English
Journal_Title :
Computational Intelligence Magazine, IEEE
Publisher :
ieee
ISSN :
1556-603X
Type :
jour
DOI :
10.1109/MCI.2006.329693
Filename :
4129848
Link To Document :
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