Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization
A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). T...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/185860 |
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| Summary: | A fitness landscape presents the relationship
between individual and its reproductive success in evolutionary
computation (EC). However, discrete and approximate
landscape in an original search space may
not support enough and accurate information for EC
search, especially in interactive EC (IEC). The fitness
landscape of human subjective evaluation in IEC is very
difficult and impossible to model, even with a hypothesis
of what its definition might be. In this paper, we
propose a method to establish a human model in projected
high dimensional search space by kernel classification
for enhancing IEC search. Because bivalent logic
is a simplest perceptual paradigm, the human model
is established by considering this paradigm principle.
In feature space, we design a linear classifier as a human
model to obtain user preference knowledge, which
cannot be supported linearly in original discrete search
space. The human model is established by this method
for predicting potential perceptual knowledge of human.
With the human model, we design an evolution
control method to enhance IEC search. From experimental
evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search
significantly. |
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| ISSN: | 2356-6140 1537-744X |