Book
Cellular Genetic Algorithms for Identifying Variables in Hybrid Gene Regulatory Networks
| العنوان: | Cellular Genetic Algorithms for Identifying Variables in Hybrid Gene Regulatory Networks |
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| المؤلفون: | Michelucci, Romain, Callegari, Vincent, Comet, Jean-Paul, Pallez, Denis |
| المساهمون: | Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), Polytech Nice-Sophia, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD, Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Stephen Smith, João Correia, Christian Cintrano |
| المصدر: | Applications of Evolutionary Computation. 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I ; https://hal.science/hal-04557498 ; Stephen Smith; João Correia; Christian Cintrano. Applications of Evolutionary Computation. 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I, 14634, Springer Nature Switzerland, pp.131-145, 2024, Lecture Notes in Computer Science, 978-3-031-56851-0. ⟨10.1007/978-3-031-56852-7_9⟩ |
| بيانات النشر: | HAL CCSD Springer Nature Switzerland |
| سنة النشر: | 2024 |
| المجموعة: | HAL Université Côte d'Azur |
| مصطلحات موضوعية: | cellular genetic algorithm, epistatic and multimodal optimisation problem, RS-CMSA-ESII, hybrid GRN, chronotherapy, real-world application, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-RO]Computer Science [cs]/Operations Research [math.OC] |
| الوصف: | International audience ; The hybrid modelling framework of gene regulatory networks (hGRNs) is a functional framework for studying biological systems, taking into account both the structural relationship between genes and the continuous time evolution of gene concentrations. The goal is to identify the variables of such a model, controlling the aggregated experimental observations. A recent study considered this task as a free optimisation problem and concluded that metaheuristics are well suited. The main drawback of this previous approach is that panmictic heuristics converge towards one basin of attraction in the search space, while biologists are interested in finding multiple satisfactory solutions. This paper investigates the problem of multimodality and assesses the effectiveness of cellular genetic algorithms (cGAs) in dealing with the increasing dimensionality and complexity of hGRN models. A comparison with the second variant of covariance matrix self-adaptation strategy with repelling subpopulations (RS-CMSA-ESII), the winner of the CEC’2020 competition for multimodal optimisation (MMO), is made. Results show evidence that cGAs better maintain a diverse set of solutions while giving better quality solutions, making them better suited for this MMO task. |
| نوع الوثيقة: | book part |
| اللغة: | English |
| ردمك: | 978-3-031-56851-0 3-031-56851-6 |
| Relation: | hal-04557498; https://hal.science/hal-04557498; https://hal.science/hal-04557498/document; https://hal.science/hal-04557498/file/evo2024.pdf |
| DOI: | 10.1007/978-3-031-56852-7_9 |
| الاتاحة: | https://hal.science/hal-04557498 https://hal.science/hal-04557498/document https://hal.science/hal-04557498/file/evo2024.pdf https://doi.org/10.1007/978-3-031-56852-7_9 |
| Rights: | http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.3E981675 |
| قاعدة البيانات: | BASE |
| ردمك: | 9783031568510 3031568516 |
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| DOI: | 10.1007/978-3-031-56852-7_9 |