Abstract
The Genetic Algorithm (GA) has been one of the most studied topics in evolutionary algorithm literature. Mimicking natural processes of inheritance, mutation, natural selection and genetic operators, GAs have been successful in solving various optimization problems. However, standard GA is often criticized as being too biased in candidate solutions due to genetic drift in search. As a result, GAs sometimes converge on premature solutions. In this paper, we survey the major advances in GA, particularly in relation to the class of structured population GAs, where better exploration and exploitation of the search space is accomplished by controlling interactions among individuals in the population pool. They can be classified as spatial segregation, spatial distance and heterogeneous population. Additionally, secondary factors such as aging, social behaviour, and so forth further guide and shape the reproduction process. Restricting randomness in reproduction has been seen to have positive effects on GAs. It is our hope that by reviewing the many existing algorithms, we shall see even better algorithms being developed.
Similar content being viewed by others
References
Alba E, Alfonso H, Dorronsoro B (2005) Advanced models of cellular genetic algorithms evaluated on SAT. In: GECCO ’05: proceedings of the 2005 conference on genetic and evolutionary computation, pp 1123–1130
Al-Madi NA, Khader AT (2007) A social based model for genetic algorithms. In: Proceedings of the third international conference on information technology (ICIT), 9–11 May 2007, Zaytoonah University, Amman
Al-Madi NA, Khader AT (2008) De Jong’s sphere model test for a social-based genetic algorithm (SBGA). IJCSNS Int J Comput Sci Netw Secur 8(3): 179–187
Al-Madi NAQ (2009) A human community-based genetic algorithm model (HCBGA). Universiti Sains Malaysia (USM), Ph.D. Dissertation
Artyushkenko B (2009) Analysis of global exploration of island model genetic algorithm. CAD systems in microelectronics, 2009. CADSM 2009. 10th international conference, pp 280–281
Cantu-Paz E (1997) A survey of parallel genetic algorithms. Calculateurs Paralleles 10(2): 141–171
Cezary ZJ, St. Clair D (1995) Genetic algorithms: simulating nature’s methods of evolving the best design solution. IEEE Potentials
Chakraborty G, Chakraborty B (1999) Ideal marriage for fine tuning in GA. In: Systems, man, and cybernetics conference proceedings, pp 631–636. IEEE Press
Charbonneau P (1995) Genetic algorithms in astronomy and astrophysics. Astrophys J Suppl Ser 101: 309–334
Coley DA (1999) An introduction to genetic algorithms for scientists and engineers. World Scientific, Singapore
Conceicao A (2006) A hierarchical genetic algorithm with age structure for multimodal optimal design of hybrid composites. Struct Multidisc Optim 31: 280–294
Da Silva JD, Simoni PO (2001) The island model parallel GA and uncertainty reasoning in the correspondence problem. Neural Netw. Proceedings of IJCNN ’01. vol 3, pp 2247–2253
Dick G (2003) The spatially-dispersed genetic algorithms. Lecture notes in computer science, 2003, vol 2724, genetic and evolutionary computation—GECCO 2003, p 210
Fernandes C, Rosa A (2001) A study on non-random mating and varying population size in genetic algorithms using a royal road function. In: Proceedings of congress on evolutionary computation, Seoul
Fogel DB (1994) Asymptotic convergence properties of genetic algorithms and evolutionary programming: analysis and experiments. Cybern Syst 25(3): 389–407
Giro R, Cyrillo M, Galvao DS (2002) Designing conducting poluymers using genetic algorithms. Chem Phys Lett 366(1-2): 170–175
Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, MA
Goldberg DE, Korb B, Deb K (1989) Messy genetics algorithms: motivation, analysis, and first results. Complex Syst 3(5):493–530 (also IlliGAL report no.890003)
Gordon VS, Pirie R, Wachter A, Sharp S (1999) Terrain-based genetic algorithm (TBGA): modeling parameter space as terrain. In: Banzhaf W, daida J, Eiben AE, Garzon HH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (1999), vol 1. Morgan Kaufmann, pp 229–235
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Hornby GS (2006) ALPS: age-layered population structure for reducing the problem of premature convergence. In: Proceedings of the 8th annual conference on genetic and evolutionary computation 2006, pp 815–822
Huber A, Mlynski DA (1998) An age-controlled evolutionary algorithm for optimization problems in physical layout. In: International symposium on circuits and systems. IEEE Press, pp 262–265
Krink T, Mayoh BH, Michalewicz Z (1999) A patchwork model for evolutionary algorithms with structure and variable size populations. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smih RE (eds) Proceedings of the genetic and evolutionary computation conference (1999), vol 2. Morgan Kaufmann, pp 1321–1328
Kubota N, Fukuda T (1997) Genetic algorithms with age structure. Soft Comput 1: 155–161
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. 3rd Revised and extended ed. Springer, Berlin
Mahfoud S, Mani G (1996) Financial forecasting using genetic algorithms. Appl Artif Intell 10(6): 543–565
Obayashi S, Daisuke S, Yukihiro T, Naoki H (2000) Multiobjective evolutionary computation for supersonic wing-shape optimization. IEEE Trans Evol Comput 4(2): 182–187
Oguzhan H, Fuat E (2000) Evaluation of crossover techniques in genetic algorithm based optimum structural design. Comput Struct 78(1-3): 435–448
Popvic D (1997) Retaining diversity of search point distribution through a breeder genetic algorithm for neural network learning. IEEE Int Conf Neural Netw 1: 495–498
Rafael L-B, Gabriela O, Uwe A (2009) Cheating for problem solving: a genetic algorithm with social interactions. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, pp 811-817
Reza A, Vahid Z, Koorush Z. (2010). MLGA: a multilevel cooperative genetic algorithm. Bio-inspired computing: theories and applications (BIC-TA). IEEE fifth international conference, pp 271–277
Sambridge M, Gallagher K (1993) Earthquake hypocenter location using genetic algorithms. Bull Seismol Soc Am 83(5): 1467–1491
Thomsen R, Rickers P, Krink T (2000) A religion-based spatial model for evolutionary algorithms. Parallel problem solving from nature PPSN VI. Lecture notes in computer science, 2000, 1917/2000, 817–826m. doi:10.1007/3-540-45356-3_80
Ursem RK (1999) Multinational evolutionary algorithms. In: Proceedings of the congress of evolutionary computation (1999), vol 3. IEEE Press, pp 1633–1640
Zbigniew S, De Jong K (2005) The influence of migration sizes and intervals on island models. In: GECCO ’05 proceedings of the 2005 conference on genetic and evolutionary computation
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lim, T.Y. Structured population genetic algorithms: a literature survey. Artif Intell Rev 41, 385–399 (2014). https://doi.org/10.1007/s10462-012-9314-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-012-9314-6