Analyzing Problem Instance Space Based on Difficulty-distance Correlation
نویسندگان
چکیده
منابع مشابه
IRDDS: Instance reduction based on Distance-based decision surface
In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
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This work is a first step in the attempt to verify whether (and in which cases) fitness distance correlation can be a good tool for classifying problems on the basis of their difficulty for genetic programming. By analogy with the studies that have already been done on genetic algorithms, we define some notions of distance between genotypes. Then we choose one of these distances to calculate th...
متن کاملirdds: instance reduction based on distance-based decision surface
in instance-based learning, a training set is given to a classifier for classifying new instances. in practice, not all information in the training set is useful for classifiers. therefore, it is convenient to discard irrelevant instances from the training set. this process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
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Ameasure of search di culty tness distance correlation FDC is introduced and exam ined in relation to genetic algorithm GA performance In many cases this correlation can be used to predict the performance of a GA on problems with known global maxima It correctly classi es easy deceptive problems as easy and di cult non deceptive problems as di cult indicates when Gray coding will prove better t...
متن کاملIRDDS: Instance reduction based on Distance-based decision surface
In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
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ژورنال
عنوان ژورنال: Journal of Korean Institute of Intelligent Systems
سال: 2012
ISSN: 1976-9172
DOI: 10.5391/jkiis.2012.22.4.414