Abstract
Recent advances in genetics controversially suggest that the model plant Arabidopsis thaliana performs genetic repair using genetic information that originates in the individual’s grandparent generation. We apply this ancestral genetic repair strategy within an Evolutionary Algorithm (EA) to solve a constraint based optimisation problem. Results indicate that the grandparent based genetic repair strategy outperforms the parent alternative. Within this framework, we investigate the impact of storing only the fittest ancestors for use as a repair template. The influence of performing repair in a fixed direction is compared to randomly varying the direction in which error detection proceeds. Finally we explore the impact of varying the direction of repair on the results produced. All results seem to support the non-Mendelian inheritance process suggested by Lolle et al.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lolle, S.J., Victor, J., Young, J., Pruitt, R.: Genome-wide non-mendelian inheritance of extra-genomic information in arabidopsis. Forensic Science International 434(1), 505–509 (2005)
Peng, Chan, Shah, Jacobsen: Increased outcrossing in hothead mutants. Nature 443(E8), 28 (2006)
Ray, A.: Plant genetics: Rna cache or genome trash? Nature 437(E1-E2) (2005)
Colorni, Dorigo, Maniezzo: Genetic algorithms and highly constrained problems: The time-table case. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 55–59. Springer, Heidelberg (1990)
Coello, C.C.: Theoretical and numerical constraint handling techniques in evolutionary algorithms: A survey. Computer Methods in Applied Mechanics and Engineering 191, 1245–1287 (2002)
Kalanmoy, D.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 2-4(186), 311–338 (2000)
Mitchell, G., O’Donoghue, D.P.: Generepair-repair operator for genetic algorithms. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 235–239. Springer, Heidelberg (2003)
FitzGerald, A., O’Donoghue, D.P.: Genetic repair for optimization under constraints inspired by arabidopsis thaliana. Parallel Problem Solving From Nature, 399–408 (2008)
Arroyo, C.: A parallel repair genetic algorithm to solve the unit commitment problem. IEEE Transactions on Power Systems 17(4) (2002)
Mitchell, G.: Evolutionary computation applied to combinatorial optimisation problems. Dublin City University (2007)
Nakano, R., Yamado, T.: Conventional genetic algorithm for job shop problems. In: Proc. 4th International Conference on Genetic Algorithms, pp. 474–479 (1991)
Michalewicz, Z., Fogel, D.B.: How to solve it: modern heuristics, 1st edn. Springer, Heidelberg (2000)
Frugoli, J.: Medicago truncatula handbook. Clemson University (November 2006)
Coghlan, A.: Rogue weeds defy rules of genetics. New Scientist 2492, 8 (2005)
Weigel, Jurgens: Hothead healer. Nature 434, 443 (2005)
Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation 8(1), 3–30 (1998)
Reinalt, G.: Tsplib - a travelling salesman problem library. OSRA Journal of Computing 3, 376–384 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
FitzGerald, A., O’Donoghue, D.P., Liu, X. (2010). Genetic Repair Strategies Inspired by Arabidopsis thaliana . In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-17080-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17079-9
Online ISBN: 978-3-642-17080-5
eBook Packages: Computer ScienceComputer Science (R0)