Abstract
Many diversity techniques have been developed for addressing premature convergence, which is a serious problem that stifles the search effectiveness of evolutionary algorithms. However, approaches that aim to avoid premature convergence can often take longer to discover a solution. The Genetic Marker Diversity algorithm is a new technique that has been shown to find solutions significantly faster than other approaches while maintaining diversity in genetic programming. This study provides a more in-depth analysis of the search behavior of this technique compared to other state-of-the-art methods, as well as a comparison of the performance of these techniques on a larger and more modern set of test problems.
Similar content being viewed by others
Notes
https://github.com/burks-pub/gecco2015.
References
L. Beadle, C.G. Johnson, Semantically driven crossover in genetic programming. in IEEE Congress on Evolutionary Computation, (2008), pp 111–116
E. Burke, S. Gustafson, G. Kendall, Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004)
A.R. Burks, W.F. Punch, An efficient structural diversity technique for genetic programming. in Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, (ACM, New York, NY, USA, GECCO ’15, 2015), pp 991–998. doi:10.1145/2739480.2754649
J.M. Daida, H. Li, R. Tang, A.M. Hilss, What makes a problem gp-hard? Validating a hypothesis of structural causes. in Genetic and Evolutionary Computation GECCO, (Springer, Berlin, 2003), pp 1665–1677
E. Galvan-Lopez, B. Cody-Kenny, L. Trujillo, A. Kattan, Using semantics in the selection mechanism in genetic programming: a simple method for promoting semantic diversity. in Evolutionary Computation (CEC), 2013 IEEE Congress on, IEEE, (2013), pp 2972–2979
D.E. Goldberg, J. Richardson, Genetic algorithms with sharing for multimodal function optimization. in Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, (Lawrence Erlbaum, Hillsdale, NJ, 1987), pp 41–49
S. Gustafson, E.K. Burke, G. Kendall, Sampling of unique structures and behaviours in genetic programming. in Genetic Programming, (Springer, 2004), pp 279–288
T. Helmuth, L. Spector, J. Matheson, Solving uncompromising problems with lexicase selection. IEEE Trans. Evol. Comput. 19(5), 630–643 (2015). doi:10.1109/TEVC.2014.2362729
G.S. Hornby, Alps: the age-layered population structure for reducing the problem of premature convergence. in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, (ACM, New York, NY, USA, GECCO ’06, 2006), pp 815–822. doi:10.1145/1143997.1144142
J. Hu, K. Seo, S. Li, Z. Fan, R.C. Rosenberg, E.D. Goodman, Structure fitness sharing (SFS) for evolutionary design by genetic programming. in Proceedings of the Genetic and Evolutionary Computation Conference, (Morgan Kaufmann, Burlington 2002), pp 780–787
J. Hu, E. Goodman, K. Seo, Z. Fan, R. Rosenberg, The hierarchical fair competition (HFC) framework for sustainable evolutionary algorithms. Evol. Comput. 13(2), 241–277 (2005). doi:10.1162/1063656054088530
M. Hutter, S. Legg, Fitness uniform optimization. IEEE Trans. Evol. Comput. 10(5), 568–589 (2006)
D. Jackson, Phenotypic diversity in initial genetic programming populations. in Genetic Programming, Lecture Notes in Computer Science vol .6021, ed. by A. Esparcia-Alczar, A. Ekrt, S. Silva, S. Dignum, A. Uyar, (Springer Berlin Heidelberg, 2010a), pp 98–109. doi:10.1007/978-3-642-12148-7_9
D. Jackson, Promoting phenotypic diversity in genetic programming. in Parallel Problem Solving from Nature, PPSN XI, Lecture Notes in Computer Science, vol 6239, ed. by R. Schaefer, C. Cotta, J. Koodziej, G. Rudolph (Springer, Berlin Heidelberg, 2010b), pp 472–481. doi:10.1007/978-3-642-15871-1_48
D. Jackson, Mutation as a diversity enhancing mechanism in genetic programming. in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, (ACM, New York, NY, USA, GECCO ’11, 2011), pp 1371–1378. doi:10.1145/2001576.2001761
E. de Jong, R. Watson, J. Pollack, Reducing bloat and promoting diversity using multi-objective methods. in Proceedings of the Genetic and Evolutionary Computation Conference, (Morgan Kaufmann, 2001)
M. Keijzer, Improving symbolic regression with interval arithmetic and linear scaling, in Genetic programming, (Springer, 2003), pp. 70–82
J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, 1992)
K. Krawiec, U.M. O’Reilly, Behavioral programming: a broader and more detailed take on semantic gp. in Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, Association for Computing Machinery (ACM), GECCO ’14, (2014). doi:10.1145/2576768.2598288
W.B. Langdon, R. Poli, Foundations of Genetic Programming. (Springer Science+Business. Media, 2002). doi:10.1007/978-3-662-04726-2
S. Luke, When short runs beat long runs. in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), (2001), pp 74–80
J. McDermott, D.R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K. De Jong, U.M. O’Reilly, Genetic programming needs better benchmarks. in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, (ACM, New York, NY, USA, GECCO ’12, 2012), pp 791–798. doi:10.1145/2330163.2330273
N.F. McPhee, N.J. Hopper, Analysis of genetic diversity through population history. in Proceedings of the Genetic and Evolutionary Computation Conference, (Morgan Kaufmann, 1999), pp 1112–1120
A. Moraglio, K. Krawiec, C.G. Johnson, Geometric semantic genetic programming. in Parallel Problem Solving from Nature, PPSN XII (part 1), vol. 7491, ed. by C.A. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, M. Pavone, (Springer, Taormina, Italy, Lecture Notes in Computer Science, 2012), pp 21–31
Q.U. Nguyen, T.A. Pham, X.H. Nguyen, J. McDermott, Subtree semantic geometric crossover for genetic programming. in Genetic Programming and Evolvable Machines (2015), pp 1–29
T.P. Pawlak, B. Wieloch, K. Krawiec, Review and comparative analysis of geometric semantic crossovers. Genet. Program. Evolvable Mach. 16(3), 351–386 (2015)
R. Poli, Hyperschema theory for gp with one-point crossover, building blocks, and some new results in ga theory. in Genetic Programming, Lecture Notes in Computer Science, vol. 1802, ed. by R. Poli, W. Banzhaf, W. Langdon, J. Miller, P. Nordin, T. Fogarty, (Springer Berlin Heidelberg, 2000), pp 163–180. doi:10.1007/978-3-540-46239-2_12
J.P. Rosca, D.H. Ballard, Rooted-tree schemata in genetic programming. in Advances in Genetic Programming vol. 3, ed. by L. Spector, W.B. Langdon, U.M. O’Reilly, P.J. Angeline, (MIT Press, Cambridge, MA, USA, 1999), chap 11, pp 243–271
M. Schmidt, H. Lipson, Age-fitness pareto optimization. in Genetic Programming Theory and Practice VIII, (Springer, 2011), pp 129–146
L. Vanneschi, M. Castelli, L. Manzoni, S. Silva, in Genetic Programming: 16th European Conference, EuroGP (2013), Vienna, Austria, April 3-5, 2013. Proceedings, Springer Berlin Heidelberg, Berlin, Heidelberg, chap A New Implementation of Geometric Semantic GP and Its Application to Problems in Pharmacokinetics, pp 205–216. doi:10.1007/978-3-642-37207-0_18
L. Vanneschi, M. Castelli, S. Silva, A survey of semantic methods in genetic programming. Genet. Program. Evolvable Mach. 15(2), 195–214 (2014)
Acknowledgments
This work was supported in part by Michigan State University through computational resources provided by the Institute for Cyber-Enabled Research.
Author information
Authors and Affiliations
Corresponding author
Additional information
This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Appendix: Listing of detailed results tables
Appendix: Listing of detailed results tables
This section lists the detailed results in table form for each metric discussed in Sect. 4. Table 14 shows the success rates of each algorithm on every problem in the test suite, and Tables 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 show the mean value of the given metric for each method across all 100 independent trials for each problem in the test suite.
Rights and permissions
About this article
Cite this article
Burks, A.R., Punch, W.F. An analysis of the genetic marker diversity algorithm for genetic programming. Genet Program Evolvable Mach 18, 213–245 (2017). https://doi.org/10.1007/s10710-016-9281-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10710-016-9281-9