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The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming

Published: 07 July 2007 Publication History

Abstract

It has been observed previously that genetic programming populations can collapse to all single node trees when a parsimony measure (tree node count) is used in a multiobjective setting. We have investigated the circumstances under which this can occur for both the 6-parity boolean learning task and a range of benchmark machine learning problems. We conclude that mutation is an important -- and we believe a hitherto unrecognized -- factor in preventing population collapse in multiobjective genetic programming; without mutation we routinely observe population collapse. From systematic variation of the mutation operator, we conclude that a necessary condition to avoid collapse is that mutation produces, on average, an increase in tree sizes (bloating) at each generation which is then counterbalanced by the parsimony pressure applied during selection. Finally, we conclude that the use of a genotype diversity preserving mechanism is ineffective at preventing population collapse.

References

[1]
C. L. Blake and C. J. Merz. UCI Repository of Machine Learning Databases. http://www.ics.uci.edu/.mlearn/MLRepository.html 1998.
[2]
S. Bleuler, M. Brack, L. Theile, and E. Zitzler. Multiobjective genetic programming:Reducing bloat using SPEA2. In Congress on Evolutionary Computation pages 536--543, Seoul, Korea, 2001. IEEE.
[3]
E. D. de Jong and J. B. Pollack. Multi-objective methods for tree size control. Genetic Programming and Evolvable Machines 4(3):211--233, 2003.
[4]
A. Ekárt and S. Z. Németh. Selection based on the Pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines 2(1):61--73, 2001.
[5]
P. J. Fleming. Personal communication, 2007.
[6]
C. M. Fonseca and P. J. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest, editor, 5th International Conference of Genetic Algorithms pages 416--423, San Mateo, CA, 1993. Morgan Kaufmann.
[7]
T. Ito, H. Iba, andS. Sato. Non-destructive depth-dependent crossover for genetic programming. In 1st European Workshop on Genetic Programming pages 14--15, Paris, France, 1998. Springer-Verlag.
[8]
W. B. Langdon and J. P. Nordin. Seeding GP populations. In R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, editors, 3rd European Conference on Genetic Programming (EuroGP'2000) pages 304--315, Edinburgh, 2000. Springer-Verlag.
[9]
W. B. Langdon and R. Poli. Fitness causes bloat: Mutation. In 1st European Workshop on Genetic Programming pages 37--48, Paris, France, 1998. Springer-Verlag.
[10]
T.-S. Lim, W.-Y. Loh, and Y.-S. Shih. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classi . cation algorithms. Machine Learning 40(3):203--229, 2000.
[11]
R. Poli and W. B. Langdon. Genetic programming with one-point crossover and point mutation. Technical Report CSRP-97-13, Department of Computer Science, University of Birmingham, Birmingham, UK, 1997.
[12]
K. Rodríguez-Vázquez, C. M. Fonseca, and P. J. Fleming. Identifying the structure of non-linear dynamic systems using multiobjective genetic programming. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans 34(4):531--547, 2004.
[13]
Y. Zhang and P. I. Rockett. Evolving optimal feature extraction using multi-objective genetic programming: A methodology and preliminary study on edge detection. In H.-G. Beyer, U.-M. O'Reilly, D. V. Arnold, W. Banzhaf, C. Blum, E. W. Bonabeaum, E. Cantu-Paz, D. Dasgupta, K. Deb, J. A. Foster, E. D. de Jong, H. Lipson, X. Llora, S. Mancoridis, M. Pelikan, G. R. Raidl, T. Soule, A. M. Tyrrell, J.-P. Watson, and E. Zitzler, editors, Genetic and Evolutionary Computation Conference (GECCO 2005) pages 795--802, Washington, DC, 2005. ACM Press.
[14]
Y. Zhang and P. I. Rockett. Feature extraction using multi-objective genetic programming. In Y. Jin, editor, Multi-Objective Machine Learning Springer, Heidelberg, 2006.

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  1. The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming

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      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958
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      Published: 07 July 2007

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      Author Tags

      1. bloat control
      2. diversity preservation
      3. genetic programming
      4. multiobjective optimization
      5. population collapse

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      GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      Cited By

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      • (2016)A Genetic Algorithm Parallel Strategy for Optimizing the Operation of Reservoir with Multiple Eco-environmental ObjectivesWater Resources Management10.1007/s11269-016-1274-130:7(2127-2142)Online publication date: 2-Mar-2016
      • (2013)Hybrid Evolutionary MethodsIntelligent Planning for Mobile Robotics10.4018/978-1-4666-2074-2.ch008(191-229)Online publication date: 2013
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      • (2011)Robotic path planning using evolutionary momentum-based explorationJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2010.49096323:4(469-495)Online publication date: 1-Dec-2011
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      • (2008)Evolving encapsulated programs as shared grammarsGenetic Programming and Evolvable Machines10.1007/s10710-008-9061-29:3(203-228)Online publication date: 1-Sep-2008
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