ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2020
Using Differential Evolution to Design Optimal Experiments.
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design.Pharmaceutics · 2025Article
- An optimized differential evolution algorithm for constitutive model fitting of arteries.Acta mechanica · 2024Article
- Metaheuristics for pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2021Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Differential Evolution (DE) has become one of the leading metaheuristics in the class of Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest principles. This general purpose optimization algorithm is viewed as an improvement over Genetic Algorithms, which are widely used to find solutions to chemometric problems. Using straightforward vector operations and random draws, DE can provide fast, efficient optimization of any real, vector-valued function. This article reviews the basic algorithm and a few of its modifications with various enhancements. We provide guidance for practitioners, discuss implementation issues and give illustrative applications of DE with the corresponding R codes to find different types of optimal designs for various statistical models in chemometrics that involve the Arrhenius equation, reaction rates, concentration measures and chemical mixtures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.