Directed evolution : a new metaheuristc for optimization

• Autorzy: Rotar C. , Iantovics L. B.

Identyfikatory

DOI

10.1515/jaiscr-2017-0013

• Języki publikacji: EN

Abstrakty

EN

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. This paper proposes a novel approach, which is to develop novel computational methods in the field of Natural Computing based on the semi-natural process, namely Directed Evolution. In the first step we explain Directed Evolution, defined as the artificial reproduction of the process of evolution in the laboratory in order to obtain performing biological entities. For computer scientists, this provide a strong source of inspiration in the search for efficient methods of optimization. The computational model that proposed here largely overlaps with the Directed Evolution protocol, and the results obtained in the numerical experiments confirm the viability of such techniques inspired by processes which are more artificial than natural. The paper describes a novel general algorithm, inspired by Directed Evolution, which is able to solve different optimization problems, such as single optimization, multiobjective optimization and combinatorial optimization problems.

Słowa kluczowe

EN

optimization; directed evolution; nature-inspired computing

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2017
• Tom: Vol. 7, No. 3
• Strony: 183--200
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Rotar C.

• Department of Exact and Engineering Sciences, “1 Decembrie 1918” University Unirii street, no. 15-17, 510109 Alba Iulia, Romania

autor

Iantovics L. B.

• Department of Computer Science, ”Petru Maior” University N. Iorga street, no. 1, 540088, Trgu Mure, ROMANIA

Bibliografia

• [1] Cobb, R. E., Chao, R. and Zhao, H., Directed evolution: Past, present, and future. AIChE Journal, 59, 2013, p. 1432–1440.
• [2] Jckel, C., Kast P., and Hilvert D., Protein design by directed evolution, Annu. Rev. Biophys, 37, 2008, p. 153-173.
• [3] Rubin-Pitel S., et al., Directed evolution tools in bioproduct and bioprocess development, In Bioprocessing for Value-Added Products from Renewable Resources: New Technologies and Applications, 2006, p. 49-72.
• [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.
• [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.
• [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.
• [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.
• [8] Voigt C. A., et al., Computationally focusing the directed evolution of proteins, Journal of Cellular Biochemistry, 2001, p. 58-63.
• [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.
• [10] Weber L., Applications of genetic algorithms in molecular diversity, Current Opinion in Chemical Biology 2.3, 1998, p. 381-385.
• [11] Arnold F. H., Design by directed evolution, Accounts of chemical research 31.3, 1998, p. 125-131.
• [12] Cadwell R. C., and Gerald F. J., Randomization of genes by PCR mutagenesis, Genome research 2.1, 1992, p. 28-33.
• [13] Stemmer W. PC., Rapid evolution of a protein in vitro by DNA shuffling, Nature 370.6488, 1994, p. 389-391.
• [14] Gartner Z. J., Evolutionary approaches for the discovery of functional synthetic small molecules, Pure and applied chemistry 78.1 2006, p. 1-14.
• [15] Biyani M., et al., Evolutionary Molecular Engineering to Efficiently Direct in vitro Protein Synthesis, CELL-FREE PROTEIN SYNTHESIS, 2012, p.51.
• [16] Park S. J., and Cochran J. R., eds. Protein engineering and design. Vol. 75. CRC press, 2009.
• [17] Darwin Ch., and Beer G., The origin of species. Oxford: Oxford University Press, 1951.
• [18] Fisher R. A., The genetical theory of natural selection. , 1958, available online at https://archive.org
• [19] Huxley J., Evolution. The Modern Synthesis, 1942. available online at www.ehudlamm.com/huxley.pdf
• [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.
• [21] Zitzler E., Deb, K., Thiele, L., Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation, vol. 8 no, 2, 2000, p. 173-195.
• [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.
• [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.
• [24] Pisinger D., Where are the hard knapsack problems?, Computers & Operations Research 32.9, 2005, p. 2271-2284.
• [25] De Castro, L.N., Fundamentals of natural computing: basic concepts, algorithms, and applications. CRC Press, 2006.
• [26] Mitchell, M. An introduction to genetic algorithms. MIT press, 1998.
• [27] Dorigo M., Birattari M., and Stutzle T., Ant colony optimization, Computational Intelligence Magazine, IEEE 1.4, 2006, p. 28-39.
• [28] De Castro L.N., and Timmis J., Artificial immune systems: a new computational intelligence approach, Springer Science & Business Media, 2002.
• [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.
• [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.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).