Skip to main content

Advertisement

Log in

A simulation-based quantitative analysis on the topological heritability of Dandelion-encoded meta-heuristics for tree optimization problems

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The solutions to many optimization paradigms arising from different application domains can be modeled as a tree graph, in such a way that nodes represent the variables to be optimized and edges evince topological relationships between such variables. In these problems the goal is to infer an optimal tree graph interconnecting all nodes under a measure of topological fitness, for which a wide portfolio of exact and approximative solvers have hitherto been reported in the related literature. In this context a research line of interest in the last few years has been focused on the derivation of solution encoding strategies suited to deal with the topological constraints imposed by tree graph configurations, particularly when the encoded solution undergoes typical operators from Evolutionary Computation. Almost all contributions within this research area focus on the use of standard crossover and mutation operators from Genetic Algorithms onto the graph topology beneath encoded individuals. However, the pace at which new evolutionary operators have emerged from the community has grown much sharply during the last decade. This manuscript elaborates on the topological heritability of the so-called Dandelion tree encoding approach under non-conventional operators. This experimental application-agnostic-based study gravitates on the topological transmission of Dandelion-encoded solutions under a certain class of multi-parent crossover operators that lie at the core of the family of \((\mu +1)\) evolution strategies and in particular, the so-called Harmony Search algorithm. Metrics to define topological heritability and respect will be defined and evaluated over a number of convergence scenarios for the population of the algorithm, from which insightful conclusions will be drawn in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. In formulations where subindices i and j are involved it is implicitly assumed that \(i>j\), i.e. undirected trees.

  2. A similar behavior was observed for \(\mathbf {Q} \in \{\mathbf {Q}_{\mathrm{one}-X},\mathbf {Q}_{\mathrm{two}-X}\}\), whose plots have been omitted for brevity.

References

  • Bazlamaç FC, Hindi SK (2001) Minimum-weight spanning tree algorithms: a survey and empirical study. Comput Oper Res 28(8):767–785. doi:10.1016/S0305-0548(00)00007-1

    Article  MathSciNet  MATH  Google Scholar 

  • Bäck T, Hoffmeister F, Schwefel HP (1991) A survey of evolution strategies. In: Proceedings of the fourth international conference on genetic algorithms. Morgan Kaufmann

  • Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Borůvka O (1926) On a minimal problem. Práce Moravské Pridovedecké Spolecnosti 3:37–58

    Google Scholar 

  • Caminiti S, Petreschi R (2005) String coding of trees with locality and heritability. Lect Notes Comput Sci 3595:251–262. doi:10.1007/11533719_27

    Article  MathSciNet  MATH  Google Scholar 

  • Caminiti S, Petreschi R (2009) Parallel algorithms for Dandelion-Like codes. Lect Notes Comput Sci 5544:611–620. doi:10.1007/978-3-642-01970-8_60

    Article  MATH  Google Scholar 

  • Djauhari MA, Gan SL (2015) Optimality problem of network topology in stocks market analysis. Physica A 419:108–114. doi:10.1016/j.physa.2014.09.060

    Article  Google Scholar 

  • Durrett R (2010) Some features of the spread of epidemics and information on a random graph. Proc Natl Acad Sci USA 107(10):4491–4498. doi:10.1073/pnas.0914402107

    Article  Google Scholar 

  • Eğecioğlu Ö, Remmel JB (1986) Bijections for Cayley trees, spanning trees, and their q-analogues. J Comb Theory A 42(1):15–30. doi:10.1016/0097-3165(86)90004-X

    Article  MathSciNet  MATH  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi:10.1177/003754970107600201

    Article  Google Scholar 

  • Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley, Hoboken

    Google Scholar 

  • Gottlieb J, Julstrom BA, Raidl GR, Rothlauf F (2001) Prüfer numbers: a poor representation of spanning trees for evolutionary search. In: Spector L (ed) GECCO’01 Proceedings of the 3rd annual conference on genetic and evolutionary computation. Morgan Kaufmann Publishers Inc, San Francisco, pp 343–350

    Google Scholar 

  • Kruskal JB (1956) On the shortest spanning subtree of a graph and the travelling salesman problem. Proc Am Math Soc 7:48–50. doi:10.1090/S0002-9939-1956-0078686-7

    Article  MATH  Google Scholar 

  • Landa-Torres I, Manjarres D, Gil-López S et al (2012) A preliminary approach to near-optimal multi-hop capacitated network design using grouping-Dandelion encoded heuristics. In: International workshop on computer aided modeling and design of communication links and networks (CAMAD), Proceedings of IEEE 17th IEEE, Barcelona, pp 85–89. doi:10.1109/CAMAD.2012.6335385

  • Palmer C, Kershenbaum A (1994) Representing trees in genetic algorithms. World congress on computational intelligence. Proceedings of the First IEEE. IEEE, Orlando FL, pp 379–384

    Google Scholar 

  • Paulden T, Smith DK (2006) From the Dandelion code to the rainbow code: a class of bijective spanning tree representations with linear complexity and bounded locality. IEEE Trans Evol Comput 10(2):108–123. doi:10.1109/TEVC.2006.871249

    Article  Google Scholar 

  • Paulden T, Smith DK (2006b) Recent advances in the study of the Dandelion code, happy code, and blob code spanning tree representations. In: International conference on evolutionary computation, Proceedings of IEEE. IEEE, Vancouver BC, pp 2111–2118. doi:10.1109/CEC.2006.1688567

  • Perez-Bellido AM, Salcedo-Sanz S, Ortiz-Garcia EG et al (2009) A Dandelion-encoded evolutionary algorithm for the delay-constrained capacitated minimum spanning tree problem. Comput Commun 32(1):154–158. doi:10.1016/j.comcom.2008.09.030

  • Perfecto C, Bilbao MN, Del Ser J et al (2015) On the heritability of Dandelion-encoded harmony search heuristics for tree optimization problems. In: International symposium on innovations in intelligent systems and applications, Proceedings of IEEE. IEEE, Madrid, pp 1–8. doi:10.1109/INISTA.2015.7276763

  • Perfecto C, Bilbao MN, Del Ser J et al (2016) Dandelion-encoded harmony search heuristics for opportunistic traffic offloading in synthetically modeled mobile networks. In: Kim JH (ed) Harmony search algorithm. Advances in intelligent systems and computing, vol 382. Springer, Berlin, pp 133–145. doi:10.1007/978-3-662-47926-1_14

  • Picciotto S (1999) How to encode a tree. Dissertation, University of California

  • Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Tech J 36:1389–1401

    Article  Google Scholar 

  • Rechenberg I (1973) Evolutionsstrategie optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog, Stuttgart. doi:10.1002/fedr.19750860506

    Google Scholar 

  • Rothlauf F (2002) Representations for genetic and evolutionary algorithms. Springer, Berlin

    Book  MATH  Google Scholar 

  • Sabattin J, Bolton C, Arias M, Parada V (2012) Evolutionary optimization of electric power distribution using the Dandelion code. J Electr Comp Eng. doi:10.1155/2012/738409

    Google Scholar 

  • Salcedo-Sanz S, Naldi M, Perez-Bellido AM et al (2010) Evolutionary optimization of service times in interactive voice response systems. IEEE Trans Evol Comput 14(4):602–617. doi:10.1109/TEVC.2009.2039142

    Article  Google Scholar 

  • Salcedo-Sanz S, Del Ser J, Landa-Torres I et al (2014) The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. Sci World J. doi:10.1155/2014/739768

    Google Scholar 

  • Thompson E, Paulden T, Smith DK (2007) The Dandelion code: a new coding of spanning trees for genetic algorithms. IEEE Trans Evol Comput 11(1):91–100. doi:10.1109/TEVC.2006.880730

    Article  Google Scholar 

  • Türetken E, González G, Blum C et al (2011) Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9(2–3):279–302. doi:10.1007/s12021-011-9122-1

    Article  Google Scholar 

  • Weyland D (2010) A rigorous analysis of the harmony search algorithm: how the research community can be misled by a methodology. Int J Appl Metaheuristic Comput 1(2):50–60. doi:10.4018/jamc.2010040104

    Article  Google Scholar 

  • Weyland D (2015) A critical analysis of the harmony search algorithm–how not to solve sudoku. Oper Res Perspect 2:97–105. doi:10.1016/j.orp.2015.04.001

    Article  MathSciNet  Google Scholar 

  • Yang XS, Deb S, Fong S et al (2016) From swarm intelligence to metaheuristics: nature-inspired optimization algorithms. Computer 49(9):52–59. doi:10.1109/MC.2016.292

    Article  Google Scholar 

  • Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Frome

    Google Scholar 

  • Zhang L, Lampe M, Wang Z (2011) Topology Design of Industrial Ethernet Networks using a Multi-Objective Genetic Algorithm. In: Communications and Networking in China (CHINACOM), 6th International ICST Conference on. IEEE, Harbin, pp 735–741. doi:10.1109/ChinaCom.2011.6158251

Download references

Acknowledgements

This work has been funded in part by the Basque Government under the ELKARTEK program (BID3A Project, Grant Ref. 123456).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Javier Del Ser.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest regarding this work.

Additional information

Communicated by C. Analide.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Perfecto, C., Bilbao, M.N., Del Ser, J. et al. A simulation-based quantitative analysis on the topological heritability of Dandelion-encoded meta-heuristics for tree optimization problems. Soft Comput 21, 4939–4952 (2017). https://doi.org/10.1007/s00500-016-2436-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2436-z

Keywords

Navigation