Abstract
Users' fatigue is the biggest technological hurdle facing Interactive Evolutionary Computation (IEC). This paper introduces the idea of "absolute scale" and "neighbour scale" to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We experimentally evaluate the effect of the proposed method using two benchmark functions. The experimental results show that the convergence speed of IEC using the proposed predictor, which learns from absolute evaluation data, is much faster than the conventional one, which learns from relative data, especially in early generations. Also, IEC with predictors that use recent data are more effective than those which use all past data.
| Original language | English |
|---|---|
| Pages (from-to) | 81-85 |
| Number of pages | 5 |
| Journal | Journal of physiological anthropology and applied human science |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2005 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Social Sciences(all)
Cite this
Improving the performance of predicting users' subjective evaluation characteristics to reduce their fatigue in IEC. / Wang, Shangfei; Takagi, Hideyuki.
In: Journal of physiological anthropology and applied human science, Vol. 24, No. 1, 01.01.2005, p. 81-85.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improving the performance of predicting users' subjective evaluation characteristics to reduce their fatigue in IEC
AU - Wang, Shangfei
AU - Takagi, Hideyuki
PY - 2005/1/1
Y1 - 2005/1/1
N2 - Users' fatigue is the biggest technological hurdle facing Interactive Evolutionary Computation (IEC). This paper introduces the idea of "absolute scale" and "neighbour scale" to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We experimentally evaluate the effect of the proposed method using two benchmark functions. The experimental results show that the convergence speed of IEC using the proposed predictor, which learns from absolute evaluation data, is much faster than the conventional one, which learns from relative data, especially in early generations. Also, IEC with predictors that use recent data are more effective than those which use all past data.
AB - Users' fatigue is the biggest technological hurdle facing Interactive Evolutionary Computation (IEC). This paper introduces the idea of "absolute scale" and "neighbour scale" to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We experimentally evaluate the effect of the proposed method using two benchmark functions. The experimental results show that the convergence speed of IEC using the proposed predictor, which learns from absolute evaluation data, is much faster than the conventional one, which learns from relative data, especially in early generations. Also, IEC with predictors that use recent data are more effective than those which use all past data.
UR - http://www.scopus.com/inward/record.url?scp=15244344330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=15244344330&partnerID=8YFLogxK
U2 - 10.2114/jpa.24.81
DO - 10.2114/jpa.24.81
M3 - Article
C2 - 15684550
AN - SCOPUS:15244344330
VL - 24
SP - 81
EP - 85
JO - Journal of Physiological Anthropology
JF - Journal of Physiological Anthropology
SN - 1880-6791
IS - 1
ER -