TY - JOUR
T1 - An extended probabilistic model building genetic network programming using both of good and bad individuals
AU - Li, Xianneng
AU - Mabu, Shingo
AU - Hirasawa, Kotaro
PY - 2013/7
Y1 - 2013/7
N2 - Classical estimation of distribution algorithms (EDAs) generally use truncation selection to estimate the distribution of the good individuals while ignoring the bad ones. However, various researches in evolutionary algorithms (EAs) have reported that the bad individuals may affect and help solving the problem. This paper proposes a new method to use the bad individuals by studying the substructures rather than the entire individual structures to solve reinforcement learning (RL) problems, which generally factorize their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named probabilistic model building genetic network programming (PMBGNP), which could solve RL problems successfully, to propose an extended PMBGNP. The effectiveness of this work is verified in an RL problem, namely robot control. Compared to other related work, results show that the proposed method can significantly speed up the evolution efficiency.
AB - Classical estimation of distribution algorithms (EDAs) generally use truncation selection to estimate the distribution of the good individuals while ignoring the bad ones. However, various researches in evolutionary algorithms (EAs) have reported that the bad individuals may affect and help solving the problem. This paper proposes a new method to use the bad individuals by studying the substructures rather than the entire individual structures to solve reinforcement learning (RL) problems, which generally factorize their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named probabilistic model building genetic network programming (PMBGNP), which could solve RL problems successfully, to propose an extended PMBGNP. The effectiveness of this work is verified in an RL problem, namely robot control. Compared to other related work, results show that the proposed method can significantly speed up the evolution efficiency.
KW - Bad individuals
KW - Estimation of distribution algorithms (EDAs)
KW - Probabilistic model building genetic network programming
KW - Probabilistic modeling
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=84879275907&partnerID=8YFLogxK
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U2 - 10.1002/tee.21864
DO - 10.1002/tee.21864
M3 - Article
AN - SCOPUS:84879275907
VL - 8
SP - 339
EP - 347
JO - IEEJ Transactions on Electrical and Electronic Engineering
JF - IEEJ Transactions on Electrical and Electronic Engineering
SN - 1931-4973
IS - 4
ER -