Why advanced population initialization techniques perform poorly in high dimension?

被引：9

作者：

Kazimipour, Borhan ^{[1
]}

Li, Xiaodong ^{[1
]}

Qin, A.K. ^{[1
]}

机构：

[1] School of Computer Science and Information Technology, RMIT University, Melbourne, 3000, VIC

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8886卷

关键词：

Evolutionary algorithm; Large-Scale optimization; Population initialization; Uniformity;

D O I：

10.1007/978-3-319-13563-2_41

中图分类号：

学科分类号：

摘要：

Many advanced population initialization techniques for Evolutionary Algorithms (EAs) have hitherto been proposed. Several studies claimed that the techniques significantly improve EAs’ performance. However, recent researches show that they cannot scale well to high dimensional spaces. This study investigates the reasons behind the failure of advanced population initialization techniques in large-scale problems by adopting a wide range of population sizes. To avoid being biased to any particular EA model or problem set, this study employs general purpose tools in the experiments. Our investigations show that, in spite of population size, uniformity of populations drops dramatically when dimensionality grows. The observation confirms that the uniformity loss exist in high dimensional spaces regardless of the type of EA, initializer or problem. Therefore, we conclude that the weak uniformity of the resulting population is the main cause of the poor performance of advanced initializers in high dimensions. © Springer International Publishing Switzerland 2014.

引用

页码：479 / 490

页数：11

共 20 条

[1]

Bratley P., Fox B.L., Algorithm 659: Implementing Sobol’s quasirandom sequence generator, ACM Transactions on Mathematical Software (TOMS), 14, 1, pp. 88-100, (1988)

[2]

Clerc M., Initialisations for particle swarm optimisation, (2008)

[3]

Fang K.T., Lin D.K., Uniform experimental designs and their applications in industry, Handbook of Statistics, 22, pp. 131-170, (2003)

[4]

Halton J.H., On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals, Numerische Mathematik, 2, 1, pp. 84-90, (1960)

[5]

Hickernell F., A generalized discrepancy and quadrature error bound, Mathematics of Computation of the American Mathematical Society, 67, 221, pp. 299-322, (1998)

[6]

Jun B., Kocher P., The Intel random number generator, (1999)

[7]

Kazimipour B., Li X., Qin A., Initialization methods for large scale global optimization, 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2750-2757, (2013)

[8]

Kazimipour B., Li X., Qin A., Effects of population initialization on differential evolution for large scale optimization, 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2404-2411, (2014)

[9]

Kazimipour B., Li X., Qin A., A review of population initialization techniques for evolutionary algorithms, 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2585-2592, (2014)

[10]

Kazimipour B., Omidvar M.N., Li X., Qin A., A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization, 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2833-2840, (2014)

← 1 2 →