TY - GEN
T1 - Multiple-world genetic algorithm to identify locally reasonable behaviors in complex social networks
AU - Miura, Yutaro
AU - Toriumi, Fujio
AU - Sugawara, Toshiharu
N1 - Funding Information:
*This work was partly supported by JSPS KAKENHI Grant Number 17KT0044. 1Computer Science and Engineering, Waseda University, Tokyo 1698555, Japan y.miura@isl.cs.waseda.ac.jp, sugawara@waseda.jp 2Graduate School of Engineering, The University of Tokyo, Tokyo 1138656, Japan tori@sys.t.u-tokyo.ac.jp
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - We propose a novel method for evolutionary network analysis that uses the genetic algorithm (GA), called the multiple world genetic algorithm, to coevolve appropriate in-dividual behaviors of many agents on complex networks without sacrificing diversity. The GA is the powerful way, and thus, used in many domains, such as economics, biology, and social science as well as computer science, to find the interaction strategies on networks of agents. In evolutionary network analysis using GA, parents for reproduction of offspring are often selected among their neighbors under the assumption that neighbors' better strategies are useful. However, if they are on complex networks, agents exist in distinctive and diverse situations. Therefore, agents have their own appropriate interaction strategies that may be affected by a large number of neighboring agents. Here, we propose the evolutionary computation method that uses a GA on fixed networks to coevolve diverse strategies for individual agents. We conducted the experiments using simulated games of social networking services to evaluate the proposed method. The results indicate that it could effectively evolve the diverse strategy for each agent and the resulting fitness values were almost always larger than those derived through evolution using the conventional evolutionary network analysis using the GA.
AB - We propose a novel method for evolutionary network analysis that uses the genetic algorithm (GA), called the multiple world genetic algorithm, to coevolve appropriate in-dividual behaviors of many agents on complex networks without sacrificing diversity. The GA is the powerful way, and thus, used in many domains, such as economics, biology, and social science as well as computer science, to find the interaction strategies on networks of agents. In evolutionary network analysis using GA, parents for reproduction of offspring are often selected among their neighbors under the assumption that neighbors' better strategies are useful. However, if they are on complex networks, agents exist in distinctive and diverse situations. Therefore, agents have their own appropriate interaction strategies that may be affected by a large number of neighboring agents. Here, we propose the evolutionary computation method that uses a GA on fixed networks to coevolve diverse strategies for individual agents. We conducted the experiments using simulated games of social networking services to evaluate the proposed method. The results indicate that it could effectively evolve the diverse strategy for each agent and the resulting fitness values were almost always larger than those derived through evolution using the conventional evolutionary network analysis using the GA.
UR - http://www.scopus.com/inward/record.url?scp=85076722847&partnerID=8YFLogxK
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U2 - 10.1109/SMC.2019.8914277
DO - 10.1109/SMC.2019.8914277
M3 - Conference contribution
AN - SCOPUS:85076722847
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3665
EP - 3672
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -