TY - GEN
T1 - Evolutionary programming improved by an individual random difference mutation
AU - Cai, Zhaoquan
AU - Huang, Han
AU - Hao, Zhifeng
AU - Li, Xueqiang
PY - 2010
Y1 - 2010
N2 - Evolutionary programming (EP) is a classical evolutionary algorithm for continuous optimization. There have been several EP algorithms proposed based on different mutations strategies like Gaussian, Cauchy, Levy and other stochastic distributions. However, their convergence speed should be improved. An EP based on individual random difference (EP-IRD) was proposed to attain better solutions in a higher speed. The mutation of EP-IRD uses a random difference of individuals selected randomly to update the variance with which offspring are generated. The IRD-based mutation can make the better offspring according to the current population faster than the mathematical stochastic distribution. The numerical results of solving benchmark problems indicate that EP-IRD performs better than other four EP algorithms based on mathematical stochastic distribution in the items of convergence speed, optimal value on average and standard deviation. © 2010 Springer-Verlag.
AB - Evolutionary programming (EP) is a classical evolutionary algorithm for continuous optimization. There have been several EP algorithms proposed based on different mutations strategies like Gaussian, Cauchy, Levy and other stochastic distributions. However, their convergence speed should be improved. An EP based on individual random difference (EP-IRD) was proposed to attain better solutions in a higher speed. The mutation of EP-IRD uses a random difference of individuals selected randomly to update the variance with which offspring are generated. The IRD-based mutation can make the better offspring according to the current population faster than the mathematical stochastic distribution. The numerical results of solving benchmark problems indicate that EP-IRD performs better than other four EP algorithms based on mathematical stochastic distribution in the items of convergence speed, optimal value on average and standard deviation. © 2010 Springer-Verlag.
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U2 - 10.1007/978-3-642-17563-3_41
DO - 10.1007/978-3-642-17563-3_41
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3642175627
SN - 9783642175626
VL - 6466 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 338
EP - 343
BT - Swarm, Evolutionary, and Memetic Computing
PB - Springer Verlag
T2 - 1st Swarm, Evolutionary and Memetic Computing Conference, SEMCCO 2010
Y2 - 16 December 2010 through 18 December 2010
ER -