Evolving Dilation Functions for Parameter Estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Papetti:2023:CIBCB,

• author = "Daniele M. Papetti and Vasco Coelho",
• booktitle = "2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)",
• title = "Evolving Dilation Functions for Parameter Estimation",
• year = "2023",
• abstract = "Global optimisation problems are among the most complex and widespread tasks in Computer Science. The capability of finding the global optimum is often hindered by many features-e.g., multi-modality, noisiness and non-differentiability- of the fitness landscape related to the problem. To overcome such issues, Dilation Functions (DFs) can be used to perform problem-dependent manipulations of the fitness landscape to 'expand' promising regions and 'compress' less promising regions. Since in many real-world scenarios the knowledge of the problem characteristics to handcraft tailored DFs is lacking, two automatic approaches to evolve ad-hoc DFs have been proposed and assessed on benchmark problems. One approach is a two-layered method that leverages an Evolution Strategies (ES) and a self-tuning variant of Particle Swarm optimisation to evolve a DF. The other approach uses Genetic Programming (GP) to evolve a set of tailored DFs for each dimension of the search space. In this work, we introduce Evolutionary LBDFs (EvLBDFs), a novel approach based on ES to evolve Local Bubble Dilation Functions, a family of DFs that locally dilate hyper-spherical bounded regions of the search space. Moreover, we compare these approaches to solve the Parameter Estimation (PE) problems of two bio-chemical systems. Our results highlight that all three approaches evolved DFs that simplified the PE landscapes. The GP-based approach outperformed the other approaches on the PE problem with the higher number of kinetic parameters to infer.",
• keywords = "genetic algorithms, genetic programming, Heating systems, Parameter estimation, Evolution (biology), Benchmark testing, Search problems, Kinetic theory, Parameter Estimation, Dilation Functions, Evolutionary Computation, Optimisation Problems, Local Bubble Dilation Functions",
• DOI = "doi:10.1109/CIBCB56990.2023.10264902",
• month = aug,
• notes = "Also known as \cite{10264902}",

}

Genetic Programming entries for Daniele M Papetti Vasco Coelho

Citations