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Applied Artificial Intelligence
An International Journal
Volume 31, 2017 - Issue 5-6
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Original Articles

Game-theoretic Evolutionary Algorithm Based on Behavioral Expectation and its Performance Analysis

Guanci YangKey Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang, China;School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, USACorrespondence[email protected]
ORCID Iconhttp://orcid.org/0000-0001-8761-5195View further author information
ORCID Icon,
Weihua ShengSchool of Electrical and Computer Engineering, Oklahoma State University, Stillwater, USAView further author information
,
Shaobo LiKey Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang, ChinaView further author information
,
Yang WangChengdu Institute of Computer Applications, University of the Chinese Academy of Sciences, Chengdu, ChinaView further author information
&
Fei XuSchool of Management, Guizhou University, Guiyang, ChinaView further author information
Pages 493-517 | Published online: 25 Oct 2017

Figures & data

Table 1. Definition of symbols.

Figure 1. General framework of GameEA.

Figure 1. General framework of GameEA.

Figure 2. General framework of the imitation operator.

Figure 2. General framework of the imitation operator.

Table 2. Test functions used for the experiments.

Table 3. Fifty independent experimental statistics results based on StGA, IMGA, RTS, DPGA, and GameEA using real-valued representation on functions f1–f13.

Figure 3. Pseudo-code for the belief learning operator for the real-valued presentation.

Figure 3. Pseudo-code for the belief learning operator for the real-valued presentation.

Figure 4. Statistics results based on 50 independent runs obtained by GameEA for function f3.

Figure 4. Statistics results based on 50 independent runs obtained by GameEA for function f3.

Figure 5. Average best-so-far curves for functions during evolution.

Figure 5. Average best-so-far curves for functions during evolution.

Table 4. Summary of statistically significance testing.

Table 5. Experimental results of GameEA with different players size.

Table 6. Results of homogeneity test of variances and one-sample Kolmogorov–Smirnov for function Rosenbrock with different parameters.

Table 7. Summary of ANOVA and a homogeneous subset for six group data obtained by GameEA with different parameters.

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