TY - GEN
T1 - Estimation of the convergence points of a population using an individual pool
AU - Yu, Jun
AU - Takagi, Hideyuki
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - We employ an individual pool to increase the precision of the estimated convergence points of a population by using individual information from past generations. Better individuals from past generations are kept in the pool, and poorer individuals are replaced with new better individuals when the pool becomes full; convergence points for the population are thus estimated using those individuals from the pool that keeps excellent individuals in past generations. The estimated convergence points are used as elite individuals, and replace the worse individuals in current population to accelerate evolutionary computation. Besides the proposed basic pool storage mechanism, we further develop an extended version which enhances the interaction between an individual pool and the population in the latest generation. We evaluate these proposed methods using differential evolution and 14 benchmark functions. The experimental results show that introducing an individual pool can improve the convergence speed and accuracy with the same computational cost, and the extended version could further enhance the accelerated effect in almost all cases.
AB - We employ an individual pool to increase the precision of the estimated convergence points of a population by using individual information from past generations. Better individuals from past generations are kept in the pool, and poorer individuals are replaced with new better individuals when the pool becomes full; convergence points for the population are thus estimated using those individuals from the pool that keeps excellent individuals in past generations. The estimated convergence points are used as elite individuals, and replace the worse individuals in current population to accelerate evolutionary computation. Besides the proposed basic pool storage mechanism, we further develop an extended version which enhances the interaction between an individual pool and the population in the latest generation. We evaluate these proposed methods using differential evolution and 14 benchmark functions. The experimental results show that introducing an individual pool can improve the convergence speed and accuracy with the same computational cost, and the extended version could further enhance the accelerated effect in almost all cases.
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U2 - 10.1109/IWCIA.2017.8203563
DO - 10.1109/IWCIA.2017.8203563
M3 - Conference contribution
AN - SCOPUS:85047188282
T3 - 2017 IEEE 10th International Workshop on Computational Intelligence and Applications, IWCIA 2017 - Proceedings
SP - 67
EP - 72
BT - 2017 IEEE 10th International Workshop on Computational Intelligence and Applications, IWCIA 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2017
Y2 - 11 November 2017 through 12 November 2017
ER -