1. bookVolume 7 (2017): Issue 3 (July 2017)
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Directed Evolution – A New Metaheuristc for Optimization

Published Online: 20 Mar 2017
Page range: 183 - 200
Received: 12 Mar 2016
Accepted: 27 Oct 2016
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

Recently, we have witnessed an infusion of calculating models based on models offered by nature, models with more or less fidelity to the original that have led to the development of various problem-solving computational procedures. Starting from the observation of natural processes at the macroscopic or microscopic level, various methods have been developed. Technological progress today allows the accelerated reproduction of natural phenomena in the laboratory, which is why a new niche has arisen in the landscape of nature-inspired methods. This niche is devoted to the emulation of artificial biological processes in computational problem-solving methods.

Keywords

[1] Cobb, R. E., Chao, R. and Zhao, H., Directed evolution: Past, present, and future. AIChE Journal, 59, 2013, p. 1432–1440.Search in Google Scholar

[2] Jckel, C., Kast P., and Hilvert D., Protein design by directed evolution, Annu. Rev. Biophys, 37, 2008, p. 153-173.Search in Google Scholar

[3] Rubin-Pitel S., et al., Directed evolution tools in bio-product and bioprocess development, In Bioprocessing for Value-Added Products from Renewable Resources: New Technologies and Applications, 2006, p. 49-72.Search in Google Scholar

[4] Moreno, P. C., Moreno A. G., and Peuela C. J., Using directed evolution techniques to solve hard combinatorial problems, Proceedings of the Computer Science & Information Technologies Conference. CSIT 2009, p. 225-229.Search in Google Scholar

[5] Berlik, S., Directed Evolutionary Algorithms by Means of the Skew-Normal Distribution, In S. Co. 2009 Sixth Conference. Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction. Maggioli Editore, 2009, p.67.Search in Google Scholar

[6] Rotar, C., Directed Evolution-a Bio-inspired Optimization Technique, Proceedings of International Conference on Theory and Applications in Mathematics and Informatics, Alba Iulia, 2015.Search in Google Scholar

[7] Oates M. J., D. W. Corne, and D. B. Kell, The bimodal feature at large population sizes and high selection pressure: implications for directed evolution, Recent Advances in Simulated Evolution and Learning, 2003, p. 215-240.Search in Google Scholar

[8] Voigt C. A., et al., Computationally focusing the directed evolution of proteins, Journal of Cellular Biochemistry, 2001, p. 58-63.Search in Google Scholar

[9] Yokobayashi, Yohei, et al., .Directed evolution of trypsin inhibiting peptides using a genetic algorithm, J. Chem. Soc., Perkin Trans. 1.20, 1996, p. 2435-2437.Search in Google Scholar

[10] Weber L., Applications of genetic algorithms in molecular diversity, Current Opinion in Chemical Biology 2.3, 1998, p. 381-385.Search in Google Scholar

[11] Arnold F. H., Design by directed evolution, Accounts of chemical research 31.3, 1998, p. 125-131.Search in Google Scholar

[12] Cadwell R. C., and Gerald F. J., Randomization of genes by PCR mutagenesis, Genome research 2.1, 1992, p. 28-33.Search in Google Scholar

[13] Stemmer W. PC., Rapid evolution of a protein in vitro by DNA shuffling, Nature 370.6488, 1994, p. 389-391.Search in Google Scholar

[14] Gartner Z. J., Evolutionary approaches for the discovery of functional synthetic small molecules, Pure and applied chemistry 78.1 2006, p. 1-14.Search in Google Scholar

[15] Biyani M., et al., Evolutionary Molecular Engineering to Efficiently Direct in vitro Protein Synthesis, CELL-FREE PROTEIN SYNTHESIS, 2012, p. 51.Search in Google Scholar

[16] Park S. J., and Cochran J. R., eds. Protein engineering and design. Vol. 75. CRC press, 2009.Search in Google Scholar

[17] Darwin Ch., and Beer G., The origin of species. Oxford: Oxford University Press, 1951.Search in Google Scholar

[18] Fisher R. A., The genetical theory of natural selection., 1958, available online at https://archive.orgSearch in Google Scholar

[19] Huxley J., Evolution. The Modern Synthesis, 1942. available online at www.ehudlamm.com/huxley.pdfSearch in Google Scholar

[20] Zitzler E., et al., Performance assessment of multiobjective optimizers: An analysis and review. Evolutionary Computation, IEEE Transactions on, 7(2), 2003, p. 117-132.Search in Google Scholar

[21] Zitzler E., Deb, K., Thiele, L., Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation, vol. 8 no, 2, 2000, p. 173-195.Search in Google Scholar

[22] Deb K., et al., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6 (2), 2002, pp. 182-197.Search in Google Scholar

[23] Shi, Y. and Eberhart, R., A modified particle swarm optimizer, In Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence., 1998, pp. 69-73.Search in Google Scholar

[24] Pisinger D., Where are the hard knapsack problems?, Computers & Operations Research 32.9, 2005, p. 2271-2284.Search in Google Scholar

[25] De Castro, L.N., Fundamentals of natural computing: basic concepts, algorithms, and applications. CRC Press, 2006.Search in Google Scholar

[26] Mitchell, M. An introduction to genetic algorithms. MIT press, 1998.Search in Google Scholar

[27] Dorigo M., Birattari M., and Stutzle T., Ant colony optimization, Computational Intelligence Magazine, IEEE 1.4, 2006, p. 28-39.Search in Google Scholar

[28] De Castro L.N., and Timmis J., Artificial immune systems: a new computational intelligence approach, Springer Science & Business Media, 2002.Search in Google Scholar

[29] Wilkins M. R. et al., From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis, BioTechnology 14, 1996, p. 61–65.Search in Google Scholar

[30] Adorio E. P., Diliman U., MVF-Multivariate Test Functions Library in C for Unconstrained Global Optimization, 2005, available online at http://www.geocities.ws/eadorio.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo