Skip to main content

Advertisement

SpringerLink
Log in

Self-adjusting the intensity of assortative mating in genetic algorithms

  • Original Paper
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Arabas J, Michalewicz Z, Mulawka J (1994) GAVaPS—a genetic algorithm with varying population Size. In: Proceedings of the first IEEE conference on evolutionary computation 1:73–78

  • Bäck T (1996). Evolutionary algorithms in theory and practice. Oxford University, New York

    MATH  Google Scholar 

  • Bäck T, Eiben AE, van der Vaart NAL (2000) An empirical study on GAs without parameters. In: Proceedings of 5th international conference on parallel problem solving from Nature, LNCS 1917. Springer, Berlin, pp 315–324

  • Bian R, Chen Z, Yuan Z (2000) Improved crossover strategy of genetic algorithms and analysis of its performance. In: Proceedings of the third world congress on intelligent control and automation, pp 516–520

  • Craighurst R, Martin W (1995) Enhancing GA performance through crossover prohibitions based on ancestry. In: Proceedings of the sixth international conference on genetic algorithms, Morgan Kauffman, Los Altos

  • De S, Pal SK and Ghosh A (1998). Genotypic and phenotypic assortative mating in genetic algorithm. Inf Sci 105: 209–225

    Article  MathSciNet  Google Scholar 

  • Eschelman LJ (1991). The CHC algorithm: how to have safe search when engaging in non-traditional genetic recombination. Proc Found Genet algorithms 1: 70–79

    Google Scholar 

  • Eschelman LJ, Schaffer JD (1991) Preventing premature convergence in genetic algorithms by preventing incest. In: Proceedings of the fourth international conference on genetic algorithms, Morgan Kauffman, Los Altos

  • Fernandes C, Tavares R, Rosa AC (2000) NiGAVaPS—outbreeding in genetic algorithms. In: Proc of 2000 ACM symposium on applied computing, pp 477–482

  • Fernandes C, Rosa AC (2001) A study on non-random mating in evolutionary algorithms using a royal road function. In: Proceedings of the 2001 congress on evolutionary computation, pp 60–66

  • Fernandes C, Tavares T, Munteanu C, Rosa AC (2001) Using assortative mating in genetic algorithms for vector quantization problems. In: Proceedings of the 2001 ACM symposium on applied computing, pp 361–365

  • Fernandes C (2002) Algoritmos Genéticos e Acasalamento não-aleatório. MSc dissertation thesis, IST, Universidade Técnica de Lisboa, in Portuguese

  • Fernandes C, Rosa AC (2006) Self-regulated population size in evolutionary algorithms. In: Proceedings of 9th international conference on parallel problem solving from Nature, LNCS 4193, pp 920–929

  • García-Martínez C, Lozano M, Molina D (2006) A local genetic algorithm for binary-coded problems. In: Runarsson T et al. (eds) Proceedings of 9th international conference on parallel problem solving from Nature, LNCS 4193, pp 192–201

  • García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez AM (2007) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res (in press)

  • Glover F (1986). Future paths for integer programming and links to artificial intelligence. Comput Oper Res 5: 533–549

    Article  MathSciNet  Google Scholar 

  • Hillis W (1991) Co-evolving parasites improve simulated evolution as an optimization procedure. In: Artificial Life II, SFI Studies in the Sciences of Complexity, vol X. Addison-Wesley, Reading

  • Jaffe K (1999). On the adaptive value of some mate selection techniques. Acta Biotheoretica 47: 29–40

    Article  Google Scholar 

  • Jones T (1995). A description of Holland’s royal road function. Evol Comput 2(3): 409–415

    Google Scholar 

  • Lobo F, Lima C (2005) Revisiting evolutionary algorithm with on-the-fly population size adjustment. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, pp 1241–1248

  • Lobo F, Lima C (2006) On the utility of the multimodal problem generator for assessing the performance of evolutionary algorithms, UAlg-ILab Report No. 200601. Also arXiv report No. cs.NE/0602051

  • Lozano M, Herrera F, Krasnogor N and Molina D (2004). Real coded memetic algorithms with crossover hill-climbing. Evol Comput J 12(3): 273–302

    Article  Google Scholar 

  • Matsui K (1999) New selection method to improve the population diversity in genetic algorithms. In: Proceedings of the 1999 IEEE international conference on systems, man, and cybernetics

  • Mauldin M (1984) Maintaining genetic diversity in genetic search. In: National conference on artificial intelligence, pp 247–250

  • Munteanu C and Rosa AC (2004). Adaptive reservoir evolutionary algorithm: an evolutionary on-line adaptation scheme for global function optimization. J Heuristics 10(6): 555–586

    Article  Google Scholar 

  • Mitchell M (1994). When will a GA outperform hillclimbing?. Adv Neural Inf Proc Syst 6: 51–58

    Google Scholar 

  • Ochoa G, Madler-Kron C, Rodriguez R and Jaffe K (1999). On sex, selection and the Red Queen. J Theor Biol 199: 1–9

    Article  Google Scholar 

  • Ochoa G, Madler-Kron C, Rodriguez R, Jaffe K (2005) Assortative mating in genetic algorithms for dynamic problems. In: Rothlauf F et al (eds) Proceedings of the 2005 EvoWorkshops, LNCS 3449, pp 617–622

  • Ochoa G (2006). Error thresholds in genetic algorithms. Evol Comput 14(2): 157–182

    Article  MathSciNet  Google Scholar 

  • Ochoa G, Jaffe K (2006) Assortative mating drastically alters the magnitude of error thresholds. In: Proceedings of 9th international conference on parallel problem solving from Nature, LNCS 4193, pp 890–899

  • Petrowski A (1997) A new selection operator dedicated to speciation. In: Proceedings of the 7th international conference on genetic algorithms, pp 144–151

  • Ronald E (1995) When selection meets seduction. In: Proceedings of the 6th international conference on genetic algorithms, Morgan Kauffman, Los Altos, pp 167–173

  • Roughgarden J (1979). Theory of population genetics and evolutionary ecology. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Russel PJ (1998). Genetics. Benjamin/Cummings, Reading

    Google Scholar 

  • Shimodaira H (1997) DCGA: a diversity control oriented genetic algorithm, In: Proceedings of the IEEE international conference on tools with artificial intelligence, pp 367–374

  • Spears W (1998) The role of mutation and recombination in evolutionary algorithms. Ph.d. dissertation thesis, George Mason University

  • Ting C, Sheng-Tu L and Chungnan L (2003). On the harmounious mating strategy through tabu search. J Inf Sci 156(3–4): 189–214

    Article  Google Scholar 

  • Todd PM and Miller GF (1991). On the sympatric origin of species: mercurian mating in the quicksilver model. In: Belew, RK and Booker, LB (eds) Proceedings of the IV international conference on genetic algorithms, pp 547–554. Morgan Kaufmann, Los Altos

    Google Scholar 

  • Wagner S, Affenzeller M (2005) SexualGA: gender-specific selection for genetic algorithms. In: Proceedings of the 9th world multiconference on systemics, cybernetics and informatics

  • Whitley D (1988) GENITOR: a different genetic algorithm. In: Proceedings of the Rocky mountain conference on artificial intelligence

  • Whitley D (1991). Fundamental principles of deception in genetic search. Found Genet algorithms 1: 221–241

    MathSciNet  Google Scholar 

  • Yang S and Yao X (2005). Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9(11): 815–834

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LaSEEB-ISR-IST, Technical University of Lisbon (IST), Av. Rovisco Pais, 1, TN 6.21, 1049-001, Lisbon, Portugal

    Carlos Fernandes & Agostinho C. Rosa

Authors
  1. Carlos Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Agostinho C. Rosa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Fernandes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fernandes, C., Rosa, A.C. Self-adjusting the intensity of assortative mating in genetic algorithms. Soft Comput 12, 955–979 (2008). https://doi.org/10.1007/s00500-007-0265-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-007-0265-9

Keywords

Search

Navigation