Connectionists: comparing algorithms performance

[renato at krohling.com.br]

Dear All, 

 We would like to announce an article 

_Ranking and
comparing evolutionary algorithms with Hellinger-TOPSIS [1]_ 

_which
might be of interest of the community, since it is of general purpose in
computational intelligence._ 

Available online for download from:


http://www.sciencedirect.com/science/article/pii/S1568494615005104 


Best regards,
 Renato Krohling 

ps: abstract 

When multiple algorithms
are applied to multiple benchmarks as it is common in evolutionary
computation, a typical issue rises, how can we rank the algorithms? It
is a common practice in evolutionary computation to execute the
algorithms several times and then the mean value and the standard
deviation are calculated. In order to compare the algorithms performance
it is very common to use statistical hypothesis tests. In this paper, we
propose a novel alternative method based on the Technique for Order
Preference by Similarity to Ideal Solution (TOPSIS) to support the
performance comparisons. In this case, the _alternatives_ are the
algorithms and the _criteria_ are the benchmarks. Since the standard
TOPSIS is not able to handle the stochastic nature of evolutionary
algorithms, we apply the Hellinger-TOPSIS, which uses the Hellinger
distance, for algorithm comparisons. Case studies are used to illustrate
the method for evolutionary algorithms but the approach is general. The
simulation results show the feasibility of the Hellinger-TOPSIS to find
out the ranking of algorithms under evaluation. 
 

Links:
------
[1]
http://www.sciencedirect.com/science/article/pii/S1568494615005104
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