Abstract
This paper uses a recent explanation for the fundamental haploid-diploid lifecycle of eukaryotic organisms to present a new evolutionary algorithm that differs from all previous known work using diploid representations. A form of the Baldwin effect has been identified as inherent to the evolutionary mechanisms of eukaryotes and a simplified version is presented here which maintains such behaviour. Using a well-known abstract tuneable model, it is shown that varying fitness landscape ruggedness varies the benefit of haploid-diploid algorithms. Moreover, the methodology is applied to optimise the targeted delivery of a therapeutic compound utilizing nano-particles to cancerous tumour cells with the multicellular simulator PhysiCell.
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
References
Balaz, I., Petrić, T., Kovacevic, M., Tsompanas, M.A., Stillman, N.: Harnessing adaptive novelty for automated generation of cancer treatments. Biosystems 199, 104290 (2021)
Baldwin, J.M.: A new factor in evolution. Am. Nat. 30(354), 441–451 (1896)
Bernstein, H., Bernstein, C.: Evolutionary origin of recombination during meiosis. Bioscience 60(7), 498–505 (2010)
Bhasin, H., Mehta, S.: On the applicability of diploid genetic algorithms. AI & Soc. 31(2), 265–274 (2016)
Bull, L.: On coevolutionary genetic algorithms. Soft Comput. 5(3), 201–207 (2001)
Bull, L.: The evolution of sex through the Baldwin effect. Artif. Life 23(4), 481–492 (2017)
Bull, L.: The Evolution of Complexity: Simple Simulations of Major Innovations. Emergence, Complexity and Computation. Springer International Publishing (2020). https://books.google.gr/books?id=-iPNDwAAQBAJ
Eiben, A.E., Smith, J.E., et al.: Introduction to Evolutionary Computing, vol. 53. Springer (2003)
Ghaffarizadeh, A., Friedman, S.H., Macklin, P.: Biofvm: an efficient, parallelized diffusive transport solver for 3-d biological simulations. Bioinformatics 32(8), 1256–1258 (2015)
Ghaffarizadeh, A., Heiland, R., Friedman, S.H., Mumenthaler, S.M., Macklin, P.: Physicell: an open source physics-based cell simulator for 3-d multicellular systems. PLoS Comput. Biol. 14(2), e1005991 (2018)
Hinton, G.E., Nowlan, S.J.: How learning can guide evolution. Complex Syst. 1(3), 495–502 (1987)
Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2014)
Kauffman, S., Levin, S.: Towards a general theory of adaptive walks on rugged landscapes. J. Theor. Biol. 128(1), 11–45 (1987)
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. OUP USA (1993)
Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge (1983)
Margulis, L., Sagan, D.: Origins of Sex: Three Billion Years of Genetic Recombination (1986)
Metzcar, J., Wang, Y., Heiland, R., Macklin, P.: A review of cell-based computational modeling in cancer biology. JCO Clin. Cancer Informatics 2, 1–13 (2019)
Preen, R.J., Bull, L., Adamatzky, A.: Towards an evolvable cancer treatment simulator. Biosystems 182, 1–7 (2019)
Smith, J.M., Szathmary, E.: The Major Transitions in Evolution. Oxford University Press, Oxford (1997)
Spears, W.M.: Evolutionary Algorithms: The Role of Mutation and Recombination. Springer Science & Business Media (2013)
Stillman, N.R., Balaz, I., Tsompanas, M.A., Kovacevic, M., Azimi, S., Lafond, S., Adamatzky, A., Hauert, S.: Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment. NPJ Comput. Mater. 7(1), 1–12 (2021)
Trun, N., Trempy, J.: Fundamental Bacterial Genetics. Wiley (2009)
Tsompanas, M.A., Bull, L., Adamatzky, A., Balaz, I.: Evolutionary algorithms designing nanoparticle cancer treatments with multiple particle types [application notes]. IEEE Comput. Intell. Mag. 16(4), 85–99 (2021)
Tsompanas, M.A., Bull, L., Adamatzky, A., Balaz, I.: Novelty search employed into the development of cancer treatment simulations. Informatics Med. Unlocked 19, 100347 (2020)
Tsompanas, M.A., Bull, L., Adamatzky, A., Balaz, I.: In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times. Comput. Methods Programs Biomed. 200, 105886 (2021)
Tsompanas, M.A., Bull, L., Adamatzky, A., Balaz, I.: Metameric representations on optimization of nano particle cancer treatment. Biocybern. Biomed. Eng. 41(2), 352–361 (2021)
Tsompanas, M.A., Bull, L., Adamatzky, A., Balaz, I.: Utilizing differential evolution into optimizing targeted cancer treatments. In: Modern Trends in Controlled Stochastic Processes, pp. 328–340. Springer (2021)
Acknowledgements
This work was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 800983.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tsompanas, MA., Bull, L., Adamatzky, A., Balaz, I. (2022). A Haploid-Diploid Evolutionary Algorithm Optimizing Nanoparticle Based Cancer Treatments. In: Balaz, I., Adamatzky, A. (eds) Cancer, Complexity, Computation. Emergence, Complexity and Computation, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-031-04379-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-04379-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04378-9
Online ISBN: 978-3-031-04379-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)