The Differential Evolution (DE) algorithm is one of the most successful evolutionary computation techniques. However, its structure is not trivially translatable in terms of mathematical transformations that describe its population dynamics. In this work, analytical expressions are developed for the probability of enhancement of individuals after each application of a mutation operator followed by a crossover operation, assuming a population distributed radially around the optimum for the sphere objective function, considering the DE/rand/1/bin and the DE/rand/1/exp algorithm versions. These expressions are validated by numerical experiments. Considering quadratic functions given by f(x)=xTDTDx and populations distributed according to the linear transformation D-1 of a radially distributed population, it is also shown that the expressions still hold in the cases when f(x) is separable (D is diagonal) and when D is any nonsingular matrix and the crossover rate is Cr=1.0. The expressions are employed for the analysis of DE population dynamics. The analysis is extended to more complex situations, reaching rather precise predictions of the effect of problem dimension and of the choice of algorithm parameters.
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http://dx.doi.org/10.1162/evco_a_00318 | DOI Listing |
Int J Mol Sci
January 2025
Key Laboratory of Cotton Breeding and Cultivation in Huang-Huai-Hai Plain, Ministry of Agriculture and Rural Affairs, Institute of Industrial Crops Shandong Academy of Agricultural Sciences, Jinan 250100, China.
The Really Interesting New Gene (RING) E3 ubiquitin ligases represent the largest class of E3 ubiquitin ligases involved in protein degradation and play a pivotal role in plant growth, development, and environmental responses. Despite extensive studies in numerous plant species, the functions of RING E3 ligases in cotton remain largely unknown. In this study, we performed systematic identification, characterization, and expression analysis of genes in cotton.
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December 2024
Department of Physiology and Pathophysiology, College of Medicine, Yanbian University, Yanji 133002, China.
Myocardial infarction (MI) is a highly challenging and fatal disease, with diverse challenges arising at different stages of its progression. As such, non-coding RNAs (ncRNAs), which can broadly regulate cell fate, and stem cells with multi-differentiation potential are emerging as novel therapeutic approaches for treating MI across its various stages. NcRNAs, including microRNAs (miRNAs) and long non-coding RNAs (LncRNAs), can directly participate in regulating intracellular signaling pathways, influence cardiac angiogenesis, and promote the repair of infarcted myocardium.
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December 2024
Graduate School of Organic Materials Science, Yamagata University, 4-3-16 Jonan, Yonezawa 992-8510, Japan.
Plants (Basel)
December 2024
School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun 130052, China.
The precise identification of maize kernel varieties is essential for germplasm resource management, genetic diversity conservation, and the optimization of agricultural production. To address the need for rapid and non-destructive variety identification, this study developed a novel interpretable machine learning approach that integrates low-field nuclear magnetic resonance (LF-NMR) with morphological image features through an optimized support vector machine (SVM) framework. First, LF-NMR signals were obtained from eleven maize kernel varieties, and ten key features were extracted from the transverse relaxation decay curves.
View Article and Find Full Text PDFAnimals (Basel)
December 2024
College of Fisheries, Southwest University, Chongqing 402460, China.
Schizothoracine fishes in saltwater lakes of the Tibetan Plateau are important models for studying the evolution and uplift of the Tibetan Plateau. Examining their adaptation to the high-salt environment is interesting. In this study, we first assembled the RNA-Seq data of each tissue of , , and from Qinghai Lake, Selincuo Lake, and Namtso Lake, respectively, obtained by the group previously.
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