论文信息 - Population optimization algorithm based on ICA

Population optimization algorithm based on ICA

We propose a new population optimization algorithm called univariate marginal distribution algorithm with independent component analysis (UMDA/ICA). Our main idea is to incorporate ICA into the UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

N. Allinson | Qingfu Zhang | Hujun Yin

[1] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[2] L. Darrell Whitley,et al. Test driving three 1995 genetic algorithms: New test functions and geometric matching , 1995, J. Heuristics.

[3] Dan Boneh,et al. On genetic algorithms , 1995, COLT '95.

[4] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[5] Heinz Mühlenbein,et al. The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[6] Te-Won Lee,et al. Independent Component Analysis , 1998, Springer US.

[7] A. Zhang,et al. The multiple lot sizing problem with rigid demand and interrupted geometric yield , 1998 .

[8] Shumeet Baluja,et al. Fast Probabilistic Modeling for Combinatorial Optimization , 1998, AAAI/IAAI.

[9] Terrence J. Sejnowski,et al. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..

[10] Terrence J. Sejnowski,et al. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.