TY - GEN
T1 - Research on optimization mechanism of virus evolutionary genetic algorithm
AU - Jiaqing, Qiao
AU - Hongtao, Yin
AU - Ping, Fu
PY - 2012
Y1 - 2012
N2 - Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, the optimization mechanism of the binary-coding VEGA is analyzed. By the geometrical representation of the virus individual, the virus reverse transcription operations is transformed to be equivalent to the crossover among several host individuals in several different generations. As these host individuals may be close to the best solution of the target problem, VEGA's effectiveness is theoretical deterministic.
AB - Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, the optimization mechanism of the binary-coding VEGA is analyzed. By the geometrical representation of the virus individual, the virus reverse transcription operations is transformed to be equivalent to the crossover among several host individuals in several different generations. As these host individuals may be close to the best solution of the target problem, VEGA's effectiveness is theoretical deterministic.
KW - VEGA
KW - binary coding
KW - geometrical representation
KW - optimization mechanism
UR - https://www.scopus.com/pages/publications/84872398784
U2 - 10.1109/ICTC.2012.6387125
DO - 10.1109/ICTC.2012.6387125
M3 - 会议稿件
AN - SCOPUS:84872398784
SN - 9781467348287
T3 - International Conference on ICT Convergence
SP - 612
EP - 614
BT - 2012 International Conference on ICT Convergence
T2 - 2012 International Conference on ICT Convergence: "Global Open Innovation Summit for Smart ICT Convergence", ICTC 2012
Y2 - 15 October 2012 through 17 October 2012
ER -