Advertisement

Evolutionary Genes Algorithm to Path Planning Problems

  • Paulo SalgadoEmail author
  • Paulo Afonso
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)

Abstract

Genes are fundamental pieces for reproductive processes and one force field creator of the evolutionary mechanisms of the species, whose laws and mechanism are not well known. In this paper a new evolutionary optimization strategy that combines the standard genetics algorithms (GA) with selfish perspective of evolution of genes is presented. Natural selection theory is explained by a mechanism, which is centred in individuals, that are the elements of a population, characterized by their chromosomes. The primary variables are the genes (characters or words), which are non-autonomous entities, grouped in a Chromosome structure (phrases of live). However, genes make their influence felt far beyond the chromosome structure (entity of the individual). Based on this paradigm, we propose the Evolutionary Genes algorithm (EGA) that enriches the GA with a new line field generating of evolutions. Genes-centred evolution (GCE) improve the search engine of Chromosome-centred evolution (CGE) of the GA. Its impact is apparent on the increased algorithm speed, but mainly on the improvement of genetics solutions, which may be useful to solve complex problems. This approach was used to path-planning problems, in a continuous search space, to show its effectiveness in complex and interdependent sub-paths and evolution processes. GCE improved local sub-paths search as sub-processes that catalyse the CCE engine to find an optimal trajectory solution, task that the standard genetic algorithm have no ability to solve.

Keywords

Genes algorithms Genetic algorithms Path planning 

References

  1. 1.
    Simon, D.: Evolutionary Optimization Algorithms. Wiley, Hoboken (2013)Google Scholar
  2. 2.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Bradford Books Editions (1975). (Reprint, ISBN 978-0262581110, 1992)Google Scholar
  3. 3.
    Qu, B.Y., Zhu, Y.S., Jiao, Y.C., Wu, M.Y., Suganthan, P.N., Liang, J.J.: A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm Evol. Comput. 38, 1–11 (2018)CrossRefGoogle Scholar
  4. 4.
    Zelinka, I.: A survey on evolutionary algorithms dynamics and its complexity – mutual relations, past, present and future. Swarm Evol. Comput. 25, 2–14 (2015)CrossRefGoogle Scholar
  5. 5.
    Gregory, T.R.: Understanding natural selection: essential concepts and common misconceptions. Evol.: Educ. Outreach 2(2), 156–175 (2009)Google Scholar
  6. 6.
    Godfrey-Smith, P.: Conditions for evolution by natural selection. J. Philos. 104, 489–516 (2007)CrossRefGoogle Scholar
  7. 7.
    Zeigler, D.: Natural selection. In: Zeigler, D. (ed.) Evolution. Academic Press, Chap. 2, pp. 9–22 (2014)Google Scholar
  8. 8.
    Efremov, V.V.: Equilibrium between genetic drift and migration at various mutation rates: simulation analysis. Russ. J. Genet. 41(9), 1055–1058 (2005)CrossRefGoogle Scholar
  9. 9.
    Burt, A., Trivers, R.: Genes in Conflict: The Biology of Selfish Genetic Elements. Belknap Press, Cambridge (2006)CrossRefGoogle Scholar
  10. 10.
    Avise, J.C.: 1976 selfish genes. In: Avise, J.C. (eds.) Conceptual Breakthroughs in Evolutionary Genetics. Academic Press, Chap. 48, pp. 101–102 (2014). ISBN 9780124201668Google Scholar
  11. 11.
    Okasha, S.: Population genetics. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (Fall 2015 Edition) (2015)Google Scholar
  12. 12.
    Sibly, R.M., Curnow, R.N.: Evolution of discrimination in populations at equilibrium between selfishness and altruism. J. Theor. Biol. 313, 162–171 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Demsetz, H.: Seemingly altruistic behavior: selfish genes or cooperative organisms. J. Bioecon. 11(3), 211–221 (2009)CrossRefGoogle Scholar
  14. 14.
    Kleiner, K.: The selfish gene that learned to cooperate. New Sci. 191(2564), 13 (2006)CrossRefGoogle Scholar
  15. 15.
    Salgado, P., Igrejas, G., Afonso, P.: Hybrid PSO-cubic spline for autonomous robots optimal trajectory planning. In: INES 2017 of 21st International Conference on Intelligent Engineering Systems, pp. 131–136, 20–23 October, Larnaca, Cyprus (2017)Google Scholar
  16. 16.
    Salgado, P., Igrejas, G., Afonso, P.: Evolutionary based on selfish and altruism strategies - an approach to path planning problems. In: IEEE International Conference on Intelligent Systems (IS), Madeira, Portugal (2018)Google Scholar
  17. 17.
    Kramer, O.: Genetic Algorithm Essentials. Studies in Computational Intelligence. Springer, Cham (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CITABUniversidade de Trás-os-Montes e Alto DouroVila RealPortugal
  2. 2.Instituto de Telecomunicações/ESTGAUniversidade de AveiroÁguedaPortugal

Personalised recommendations