Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations
Jin Y, Gruna R, Paenke I, Sendhoff B (2009)
In: 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making. IEEE: 38-45.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Jin, YaochuUniBi 
;
Gruna, Robin;
Paenke, Ingo;
Sendhoff, Bernhard
;
Gruna, Robin;
Paenke, Ingo;
Sendhoff, BernhardAbstract / Bemerkung
Robustness and innovation are two essential facets for biological evolution, where robustness means the relative insensitivity of an organism's phenotype to mutations, while innovation (evolvability) denotes the individual's ability to evolve novel phenotypes that help its survival and reproduction. Although much research has been conducted on robustness and evolvability of both biological and computational evolutionary systems, little work on the quantitative analysis of the relationship between robustness and evolvability has been reported. In this work, a measure for innovation called local variability has been suggested. Based on a neutrality degree borrowed from literature [1] and local variability, a multi-objective evolutionary algorithm has been employed to maximize the robustness and innovation by optimizing the genotype-phenotype mapping of the redundant representation. The obtained Pareto-optimal solutions are then analyzed to reveal the trade-off relationship between robustness and innovation of the redundant representation.
Erscheinungsjahr
2009
Titel des Konferenzbandes
2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making
Seite(n)
38-45
Konferenz
2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making (MCDM)
Konferenzort
Nashville, TN, USA
ISBN
978-1-4244-2764-2
Page URI
https://pub.uni-bielefeld.de/record/2978624
Zitieren
Jin Y, Gruna R, Paenke I, Sendhoff B. Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations. In: 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making. IEEE; 2009: 38-45.
Jin, Y., Gruna, R., Paenke, I., & Sendhoff, B. (2009). Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations. 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making, 38-45. IEEE. https://doi.org/10.1109/MCDM.2009.4938826
Jin, Yaochu, Gruna, Robin, Paenke, Ingo, and Sendhoff, Bernhard. 2009. “Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations”. In 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making, 38-45. IEEE.
Jin, Y., Gruna, R., Paenke, I., and Sendhoff, B. (2009). “Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations” in 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making (IEEE), 38-45.
Jin, Y., et al., 2009. Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations. In 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making. IEEE, pp. 38-45.
Y. Jin, et al., “Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations”, 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making, IEEE, 2009, pp.38-45.
Jin, Y., Gruna, R., Paenke, I., Sendhoff, B.: Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations. 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making. p. 38-45. IEEE (2009).
Jin, Yaochu, Gruna, Robin, Paenke, Ingo, and Sendhoff, Bernhard. “Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations”. 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making. IEEE, 2009. 38-45.
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