Paper Info
Title
Quantifying ruggedness of continuous landscapes using entropy
Abstract
A major unsolved problem in the field of optimisation and computational intelligence is how to determine which algorithms are best suited to solving which problems. This research aims to analytically characterise individual problems as a first step towards attempting to link problem types with the algorithms best suited to solving them. In particular, an information theoretic technique for analysing the ruggedness of a fitness landscape with respect to neutrality was adapted to work in continuous landscapes and to output a single measure of ruggedness. Experiments run on test functions with increasing ruggedness show that the proposed measure of ruggedness produced relative values consistent with a visual inspection of the problem landscapes. Combined with other measures of complexity, the proposed ruggedness measure could be used to more broadly characterise the complexity of fitness landscapes in continuous domains.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983112
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
single measure,quantifying ruggedness,proposed measure,major unsolved problem,continuous domain,problem landscape,proposed ruggedness measure,fitness landscape,continuous landscape,problem type,characterise individual problem,computational intelligence,labeling,testing,data mining,prediction algorithms,inspection,entropy,visual inspection,information analysis,classification algorithms,benchmark testing,algorithm design and analysis,polynomials,shape,computational complexity
Mathematical optimization,Algorithm design,Fitness landscape,Polynomial,Computational intelligence,Computer science,Prediction algorithms,Artificial intelligence,Statistical classification,Machine learning,Benchmark (computing),Computational complexity theory
Conference
Citations 
PageRank 
References 
32
1.12
10
Authors
2
Name
Order
Citations
PageRank
Katherine Malan116212.77
Andries Petrus Engelbrecht22183125.32