A review of population initialization techniques for evolutionary algorithms

Although various population initialization techniques have been employed in evolutionary algorithms (EAs), there lacks a comprehensive survey on this research topic. To fill this gap and attract more attentions from EA researchers to this crucial yet less explored area, we conduct a systematic review of the existing population initialization techniques. Specifically, we categorize initialization techniques from three exclusive perspectives, i.e., randomness, compositionality and generality. Characteristics of the techniques belonging to each category are carefully analysed to further lead to several sub-categories. We also discuss several open issues related to this research topic, which demands further in-depth investigations.

[1]  Ronald W. Morrison.  Dispersion-Based Population Initialization , 2003, GECCO.

[2]  Yuanzhen Wang,et al.  Differential Evolution using Uniform-Quasi-Opposition for Initializing the Population , 2010 .

[3]  Lei Peng,et al.  A Novel Differential Evolution with Uniform Design for Continuous Global Optimization , 2012, J. Comput..

[4]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[5]  Na Wang,et al.  Influence of Dimensionality and Population Size on Opposition-based Differential Evolution Using the Current Optimum , 2013 .

[6]  Michal Pluhacek,et al.  Chaos PSO algorithm driven alternately by two different chaotic maps - An initial study , 2013, 2013 IEEE Congress on Evolutionary Computation.

[7]  Sebastian Engell,et al.  Systematic Initialization Techniques for Hybrid Evolutionary Algorithms for Solving Two-Stage Stochastic Mixed-Integer Programs , 2011, IEEE Transactions on Evolutionary Computation.

[8]  Mark Richards,et al.  Choosing a starting configuration for particle swarm optimization , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[9]  Rakesh Kumar,et al.  A Population Initialization Method by Memetic Algorithm , 2013 .

[10]  Xiang Li,et al.  Initialization strategies to enhancing the performance of genetic algorithms for the p-median problem , 2011, Comput. Ind. Eng..

[11]  K. Miettinen,et al.  Quasi-random initial population for genetic algorithms , 2004 .

[12]  Kaisa Miettinen,et al.  On initial populations of a genetic algorithm for continuous optimization problems , 2007, J. Glob. Optim..

[13]  Xiaodong Li,et al.  Initialization methods for large scale global optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[14]  Ching-Yuen Chan,et al.  An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection , 2012, Comput. Math. Appl..

[15]  Pierre L'Ecuyer,et al.  Improved long-period generators based on linear recurrences modulo 2 , 2004, TOMS.

[16]  Li Zhao,et al.  A review of opposition-based learning from 2005 to 2012 , 2014, Eng. Appl. Artif. Intell..

[17]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution for Optimization of Noisy Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[18]  A. L. Gutierrez,et al.  Comparison of different PSO initialization techniques for high dimensional search space problems: A test with FSS and antenna arrays , 2011, Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP).

[19]  Russel E. Caflisch,et al.  Quasi-Random Sequences and Their Discrepancies , 1994, SIAM J. Sci. Comput..

[20]  Borhan Kazimipour,et al.  A novel genetic-based instance selection method: Using a divide and conquer approach , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[21]  Edmund K. Burke,et al.  Initialization Strategies and Diversity in Evolutionary Timetabling , 1998, Evolutionary Computation.

[22]  Pierre L'Ecuyer,et al.  TestU01: A C library for empirical testing of random number generators , 2006, TOMS.

[23]  Dan Simon,et al.  Oppositional biogeography-based optimization , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[24]  Rolf Wanka,et al.  Theoretical Analysis of Initial Particle Swarm Behavior , 2008, PPSN.

[25]  Shuhei Kimura,et al.  Genetic algorithms using low-discrepancy sequences , 2005, GECCO '05.

[26]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[27]  S. K. Park,et al.  Random number generators: good ones are hard to find , 1988, CACM.

[28]  Vinicius Veloso de Melo,et al.  Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems , 2012, Inf. Sci..

[29]  Ajith Abraham,et al.  Unconventional initialization methods for differential evolution , 2013, Appl. Math. Comput..

[30]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

[31]  Paul C. Kocher,et al.  The intel random number generator , 1999 .

[32]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[33]  Shahryar Rahnamayan,et al.  Center-based sampling for population-based algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[34]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[35]  Leonard A. Smith.  Chaos: A Very Short Introduction , 2007 .

[36]  uz,et al.  Truly random number generators based on non-autonomous continuous-time chaos , 2010 .

[37]  Shahryar Rahnamayan,et al.  Opposition based computing — A survey , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[38]  Jing Wang,et al.  A New Population Initialization Method Based on Space Transformation Search , 2009, 2009 Fifth International Conference on Natural Computation.

[39]  Bo Liu,et al.  Improved particle swarm optimization combined with chaos , 2005 .

[40]  J. Burkardt,et al.  International Journal of C 2007 Institute for Scientific Numerical Analysis and Modeling Computing and Information Latinized, Improved Lhs, and Cvt Point Sets in Hypercubes , 2022 .

[41]  G. Vandenbosch,et al.  Impact of Random Number Generators on the performance of particle swarm optimization in antenna design , 2012, 2012 6th European Conference on Antennas and Propagation (EUCAP).

[42]  Meng Zhang,et al.  Chaotic co-evolutionary algorithm based on differential evolution and particle swarm optimization , 2009, 2009 IEEE International Conference on Automation and Logistics.

[43]  Josef Dick,et al.  On the mean square weighted ℒ₂ discrepancy of randomized digital (t,m,s)-nets over ℤ₂ , 2005 .

[44]  Christophe Dutang,et al.  A note on random number generation , 2009 .

[45]  Konstantinos E. Parsopoulos,et al.  Initializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method , 2002 .

[46]  I. Sloan,et al.  Low discrepancy sequences in high dimensions: How well are their projections distributed? , 2008 .

[47]  Liang Gao,et al.  An effective genetic algorithm for the flexible job-shop scheduling problem , 2011, Expert Syst. Appl..

[48]  Yu Gao,et al.  A Memetic Differential Evolutionary Algorithm for High Dimensional Functions' Optimization , 2007, Third International Conference on Natural Computation (ICNC 2007).

[49]  Muhammad Asif Jan,et al.  Centroid-based Initialized JADE for global optimization , 2011, 2011 3rd Computer Science and Electronic Engineering Conference (CEEC).

[50]  Sanyang Liu,et al.  Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique , 2012 .

[51]  Michal Pluhacek,et al.  Do evolutionary algorithms indeed require randomness? , 2013, 2013 IEEE Congress on Evolutionary Computation.

[52]  Chih-Hsun Chou,et al.  Genetic algorithms: initialization schemes and genes extraction , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[53]  Mehmet Ergezer,et al.  Survey of oppositional algorithms , 2011, 14th International Conference on Computer and Information Technology (ICCIT 2011).

[54]  Dipankar Dasgupta,et al.  On the use of informed initialization and extreme solutions sub-population in multi-objective evolutionary algorithms , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[55]  Roman Senkerik,et al.  Influence of chaotic dynamics on the performance of evolutionary algorithms - An initial study , 2012 .

[56]  A. Owen.  Randomly Permuted (t,m,s)-Nets and (t, s)-Sequences , 1995 .

[57]  Xiaodong Li,et al.  A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[58]  Ajith Abraham,et al.  Particle Swarm Optimization: Performance Tuning and Empirical Analysis , 2009, Foundations of Computational Intelligence.

[59]  Nguyen Xuan Hoai,et al.  Initialising PSO with randomised low-discrepancy sequences: the comparative results , 2007, 2007 IEEE Congress on Evolutionary Computation.

[60]  Millie Pant,et al.  Differential Evolution using Quadratic Interpolation for Initializing the Population , 2009, 2009 IEEE International Advance Computing Conference.

[61]  A. Rukhin,et al.  Statistical Testing of Random Number Generators , 1999 .

[62]  José M. Molina López,et al.  Initialization Procedures for Multiobjective Evolutionary Approaches to the Segmentation Issue , 2012, HAIS.

[63]  Yanguang Cai,et al.  A hybrid chaotic quantum evolutionary algorithm , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[64]  Fang Liu,et al.  Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization , 2010, Knowledge and Information Systems.

[65]  Michal Pluhacek,et al.  Investigation on the Differential Evolution driven by selected six chaotic systems in the task of reactor geometry optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[66]  Millie Pant,et al.  Particle Swarm Optimization: Experimenting the Distributions of Random Numbers , 2007, IICAI.

[67]  Shahryar Rahnamayan,et al.  Opposition versus randomness in soft computing techniques , 2008, Appl. Soft Comput..

[68]  Alfonso Rodríguez-Patón,et al.  Initialization method for grammar-guided genetic programming , 2006, Knowl. Based Syst..

[69]  Shahryar Rahnamayan,et al.  A novel population initialization method for accelerating evolutionary algorithms , 2007, Comput. Math. Appl..

[70]  Dennis K. J. Lin,et al.  Ch. 4. Uniform experimental designs and their applications in industry , 2003 .

[71]  F. Pezzella,et al.  A genetic algorithm for the Flexible Job-shop Scheduling Problem , 2008, Comput. Oper. Res..

[72]  Xu Wei-bin.  A Modified Artificial Bee Colony Algorithm , 2011 .

[73]  Xiaodong Li,et al.  Effects of population initialization on differential evolution for large scale optimization , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).