Selection Of Parents Research Articles

Integrating multi‐trait genomic selection with simulation strategies to improve grain yield and parental line selection in rice

AbstractInclusion of correlated secondary traits in the prediction of primary trait in multi‐trait genomic selection (GS) models can improve the predictive ability. Our objectives in the present investigations were to (i) evaluate the effectiveness of multi‐trait and single‐trait GS models for the higher predictive ability and (ii) compare the breeding potential of parental lines selected based on phenotype and GS for grain yield in rice. We used phenotype data of five correlated traits as secondary traits evaluated to predict the grain yield, a primary trait. Yield related functional markers were used for prediction. Breeding populations were simulated using the best parents selected through GS and phenotype based selection. Results suggest that the multi‐trait model resulted in higher predictive abilities (0.82 for grain yield) than single‐trait models (0.76 for grain yield) and parents selected through GS have potential to produce superior progenies. We conclude that the use of a multi‐trait GS approach is advantageous over single‐trait models, and the GS also help selecting potential parents for developing improved populations. The results of the study have potential scope for improving quantitative traits using GS in rice.

Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator

The Traveling Salesman Problem (TSP) represents an extensively researched challenge in combinatorial optimization. Genetic Algorithms (GAs), recognized for their nature-inspired approach, stand as potent heuristics for resolving combinatorial optimization problems. Nevertheless, GA exhibits inherent deficiencies, notably premature convergence, which diminishes population diversity and consequential inefficiencies in computational processes. Such drawbacks may result in protracted operations and potential misallocation of computational resources, particularly when confronting intricate NP-hard optimization problems. To address these challenges, the current study underscores the pivotal role of the selection operator in ameliorating GA efficiency. The proposed methodology introduces a novel parameter operator within the Stochastic Universal Selection (SUS) framework, aimed at constricting the search space and optimizing genetic operators for parent selection. This innovative approach concentrates on selecting individuals based on their fitness scores, thereby mitigating challenges associated with population sorting and individual ranking while concurrently alleviating computational complexity. Experimental results robustly validate the efficacy of the proposed approach in enhancing both solution quality and computational efficiency, thereby positioning it as a noteworthy contribution to the domain of combinatorial optimization.

Breeding Brassica juncea hybrids with higher seed weight and oil content: Defining criteria for selection of parents

Most of the released high-yielding hybrids of Brassica juncea have a low 1000-seed weight (TSW) with no increment in the percent oil content (OC), and, therefore, these hybrids have poor acceptance among the farmers in India. It is, thus, imperative to understand the genetic basis of these traits and deploy them in commercial hybrid breeding programs. The present study utilized a set of 15 diverse B. juncea genotypes with TSW and OC ranging from 1.32 to 8.26 g and 31.93–43.39 %, respectively, to generate 210 hybrids following a full diallel mating scheme. These hybrids along with their parents, were evaluated in three different environments. Inheritance of TSW suggests the predominance of additive gene action, whereas non-additive gene action was observed to regulate OC. Further, TSW and OC were reported to be influenced by maternal and non-maternal effects, respectively. Parents with bold seeds viz., NPJ 253, RH 761 and EC 223389 were identified as good general combiners for both the traits. Hybrid generated from the cross NPJ 253 x NPJ 161, with both parents having high seed weights, exhibited the highest mean values (8.43 g) and heterobeltiosis (17.2 %) for TSW. Whereas, hybrid between parents NPJ 253 and IC 426372, possessing high and low seed weights, respectively, observed the highest mean value (44.95 %) and heterobeltiosis (14.89 %) for OC. Keeping both TSW and OC together, hybrids viz., NPJ 253 x EC 223389 (H1), NPJ 253 x NPJ 161 (H2) and NPJ 253 x Pusa Tarak (H4) were identified as promising using the Multi-trait Genotype-Ideotype Distance Index. Lines with higher TSW and better combining ability for OC shall be converted to male and/or female lines for generating commercial hybrids. The scheme for deploying higher seed weight and improved oil content in B. juncea hybrids is discussed.

Identification of Floral Fragrance Components in 20 Intersectional Hybrids of Paeonia in Luoyang Region

ABSTRACTIntersectional hybrids of Paeonia (Paeonia Itoh hybrids) have rich colours and strong fragrance, combining the advantages of sect. Moutan and sect. Paeonia, and have great economic value and development potential. However, there have been few studies on the floral fragrance components of intersectional hybrids of Paeonia. In this study, the types and contents of volatile components of 20 intersectional hybrids of Paeonia were conducted. A total of 53 floral volatiles were identified though gas chromatography–mass spectrometry (GC–MS) analysis, with linalool, nonanal and phenylethyl alcohol as the main floral components. Based on the compound contents and odour activity values (OAVs) analysis, the 20 cultivars were classified into four aroma types: a floral scent, a fresh floral scent, a floral with spicy scent and a fruity scent. ‘Magical Mystery Tour’ had the highest volatile compound contents and OAVs, and had potential for exploitation. This study provided a theoretical reference for the selection of parents for flower fragrance breeding of Paeonia Itoh hybrids.

Determination of S‐Allele Combination in an Italian Apple (Malus × domestica Borkh.) Germplasm Core Collection

ABSTRACTAn S‐RNase‐based gametophytic self‐incompatibility (GSI) system, a mechanism that forces outbreeding by preventing self‐fertilization, characterizes the genus Malus. Knowledge of the self‐incompatibility (S) genotypes of apple cultivars is crucial for choosing pollen donors for fruit production and breeding. Even though the S‐allele of most commercial apple cultivars has already been identified, limited information is available about the S‐allele diversity within local germplasm collections. In this study, 67 S‐allele combinations of local apple accessions were identified. The allele S3 was the most common among local apple accessions followed by S1 and S7. The main aim of this study is to provide new information on cultivar compatibility, and these results will be used to set up new parent selection in apple breeding programmes as well as pollinator selection for the commercial orchard.

Genetic Diversity and Population Structure Analysis in Guar.

Guar [Cyamopsis tetragonoloba (L.) Taub] was domesticated in India and Pakistan. It is mainly self-pollinated, bushy, and deeply tap-rooted. Guar seed endosperm contains galactomannan gum, which is used in many food products, pharmaceuticals, cosmetics, explosives, meat products, and pet foods, and in the textile industry, yet its genetic diversity remains largely underexplored. Using 7000 high-quality single nucleotide polymorphism (SNP) markers acquired from genotyping by sequencing (GBS), we analyzed the genetic diversity and population structure in 225 guar accessions from India, Pakistan, and the United States. Structure Harvester revealed that K = 3 had the best delta K, whereas K = 2 had the second-highest delta K. Three major genetic clusters (K = 3) were identified using population structure analysis, utilizing an admixture model: 156 accessions (69.3%) were classified into Q1, 23 accessions (10.2%) in Q2, and 16 accessions (7.1%) in Q3. The remaining 30 accessions (13.3%) were included in the admixture. In all three of the subpopulations at K = 3, most of the guar accessions came from India. We also found that these clusters mostly correlated with geographic origins. Results showed that the Q2 and Q3 subpopulations included only guar accessions from India. Genetic resources from Q2 and Q3 may represent an untapped reservoir for introducing beneficial variety into the U.S. breeding populations. This genetic diversity and population structure analysis of the guar gene pool will be of interest to conduct allele mining and donor parent selection for the development of new and better guar germplasm for desired traits.

Koi Fish (Cyprinus carpio) Nursery Techniques at the Batu Kumbung Lingsar Fish Seed Center, West Lombok

Koi fish is one type of freshwater fish that is classified as an ornamental freshwater fish that has economic value in the national and international markets. Efforts to develop koi fish cultivation cannot be separated from the breeding stage such as enlargement and nursery activities. One of the koi fish cultivation places in West Lombok Regency is at the Batu Kumbung Lingsar Fish Seed Center, so it is necessary to know what nursery techniques are applied there considering its good production. The activity was carried out for approximately 30 days at the Batu Kumbung Lingsar using a descriptive method. The results of the activities obtained were that in the koi fish nursery technique at the Batu Kumbung Lingsar Fish Seed Center, it started from pond preparation (drying, fertilization, liming, filling with water), parent selection, spawning, nursery and feeding. The pond drying is done for 2 days, liming, fertilization, and filling with water are done for 1 day, the weight of the parent that is ready to spawn is 1.5 kg for the male parent while for the female parent 4 kg, the ratio of the parent used is 2:1 where 6 male and 3 female broodstocks, the nursery is done at the age of 2 days with the results of the offspring spread in the nursery as many as 5000 larvae, and the feeding technique uses the ad satiation method.

Assessing population structure and morpho-molecular characterization of sunflower (Helianthus annuus L.) for elite germplasm identification.

Sunflower (Helianthus annuus L.), known for its adaptability and high yield potential, is vital in global edible oil production. Estimating genetic diversity is a key pre-breeding activity in crop breeding. The current study comprised of 48 genotypes which were assessed for their biometrical traits at department of Oilseeds, Tamil Nadu Agricultural University, during the rainy season of 2022. The lines were subsequently characterised using 103 simple sequence repeat (SSR) markers for molecular diversity analysis. The results indicated that the net nucleotide distances indicated varying genetic divergence, with subpopulations II and V showing the highest (0.056) and I and IV the lowest (0.014). Subpopulation IV exhibited the highest heterozygosity (0.352), while subpopulation III had the lowest heterozygosity and a low Fst (0.173). Principal components analysis (PCA) and hierarchical cluster analysis were employed for assessing the morphological diversity, facilitating genotype grouping and parent selection for breeding programs. The first four components cumulatively accounted for 86.72% of the total variation. Cluster Analysis grouped 48 sunflower genotypes into three clusters based on genetic diversity. COSF 13B stands out for its high head diameter, oil content, seed yield, and oil yield based on mean performance of morphological data. Principal coordinate analysis (PCoA) mirrored the groupings from the Neighbor Joining method, with the first three components explaining 27.24% of the total variation. Molecular data analysis identified five distinct clusters among the germplasm. By integrating morphological and molecular marker data with genetic distance analysis, substantial diversity was revealed with the genotypes RHA 273 and GMU 325 consistently demonstrated high oil yield per plant. The genotypes GMU 477, GMU 450, COSF 13B, RHA 102, CMS 1103B, and RHA GPR 58 have been identified as suitable parents for enhancing oil content in sunflower breeding programs. These findings also aid in selecting SSR markers for genotype characterization and in choosing diverse parents for breeding programs.

Screening of Inbred Lines of Maize against Turcicum Leaf Blight (Exserohilum turcicum) under Artificial Epiphytotic Conditions

Aims: Turcicum Leaf Blight (TLB), caused by Exserohilum turcicum, significantly affects maize in Karnataka, leading to yield losses of up to 70% in severe cases. To develop tolerant genotypes against this disease, an experiment was conducted using 100 inbred lines collected from the IMIC maize field day in Hyderabad. Study Design: Investigation was carried using Randomized complete block design with two replication. Place and Duration of Study: Maize Research Centre and Seed Farm, Devihosur during kharif-2022. Methodology: One hundred inbred lines were screened against TLB disease. among them 10 resistant lines were selected based on their maturity, disease reaction, grain yield and per se performance. These inbreds along with the checks (Resistant check CI-4 and Susceptible Check CM-202) were sown in Kharif 2022 at Maize Research Centre and Seed Farm Devihosur, UAS, Dharwad. Artificial inoculation was performed 45 days after sowing in the leaf whorls using infected leaves with TLB and the results were evaluated at the silk drying stage. To assess the disease reaction to TLB, we used 1 to 9 scale as established by the Indian Institute of Maize Research, Ludhiana (Anon., 2016). Lines that received disease scores between 1.0 and 3.0 were classified as resistant (R), scores of 4 to 5 as moderately resistant (MR), scores of 6 to 7 as moderately susceptible (MS), and scores of 8 to 9 as susceptible (S). Results: The performance of these lines were compared with the resistant check CI-4 and susceptible check CM-202. Screening trial revealed that out of hundred inbred lines, eighteen lines Showed highly resistant reaction. This information can be useful for selection of parents and to develop tolerant hybrids in breeding programs or utilize them as source of resistance.

Characterisation of the volatile compounds in nine varieties and three breeding selections of celery using GC–IMS and GC–MS

Celery (Apium graveolens L.) is a popular vegetable crop planted worldwide. In this study, the volatile compound characteristics of three A. graveolens var. dulce varieties (type D), and six A. graveolens var. secalinum varieties (type S), and three breeding selections were analysed and 41 and 73 volatile compounds were identified using GC–MS and GC–IMS, respectively. The results showed that type S emitted more terpenes and phthalides than type D varieties. Both GC–MS and GC–IMS could discriminate between type S and type D. Six potential biomarkers, i.e., sabinene, (E)-caryophyllene, α-phellandrene, limonene, γ-terpinene, and hexanal, were identified using both models. Both GC–MS and GC–IMS could discriminate between parent and breeding selection. The relative contents of seven (GC–IMS OPLS–DA model) and ten (GC–MS OPLS–DA model) volatile compounds showed over-parent heterosis. This study provides a technical reference for distinguishing celery varieties in the same habitat and preliminarily reveals the aroma relationship between breeding selections and their parents.

Whole genome sequencing of Castanea mollissima and molecular mechanisms of sugar and starch synthesis.

The chestnut tree exhibits self-incompatibility, where the selection of the male parent (pollen xenia) significantly affects seed starch metabolism, as well as fruit yield and quality. Despite its importance, the molecular mechanisms underlying pollen xenia remains largely unknown. In this study, we utilized the 'Lan You' variety of C. mollissima to construct a high-quality reference genome. As a result, a first Telomere-to-telomere (T2T) gap-free genome for this species was successfully assembled. A total of 560 transcription factors and 22 structural genes were identified as consistent across the TO-GCNs, indicating a consistent regulation pattern in the co-expression of genes involved in starch accumulation. These networks were further divided into three sub-networks: T1, T2, and T3. Among these, the T1 and T2 sub-networks exhibited a higher number of structural genes with consistent regulation patterns and were closely associated with sugar biosynthesis. The gene SBE (Camol08G0254600) was identified as the hub gene with the highest degree of connectivity, encoding a key rate-limiting enzyme in the amylopectin biosynthesis pathway. This study provides a foundation for further research on C. mollissima population genetics, genetic improvement, and strategies aimed at enhancing yield and quality.

Genome-wide association study of pineapple breeding population

Pineapples (Ananas comosus (L.) Merr.) are one of the most important tropical fruits. Although pineapple breeding program has been conducted in Japan, sufficient genomic information has not been adopted for breeding. The objective of this study was to identify quantitative trait loci (QTL) for pineapple breeding-related traits that can be applied to different populations. Here, we performed a genome-wide association study using seven breeding populations, including 703 individuals with genome-wide 9574 SNPs. Eleven QTLs were detected for nine traits (flesh color, flesh color values (a*, b*), bolting days, days from bolting to harvest, fruit shell color, soluble solid content, acidity, ascorbic acid content, and fruit cracking). Phenotypic value of each genotype in each population was confirmed for each QTL. Six QTLs (acidity QTL ACID-1, ascorbic acid QTL AsA-1 and AsA-2, flesh color QTL FCOL-1 and FCOLb-1, and fruit-cracking QTL CRACK-1) showed significant intergenotypic variance among all populations with genotype segregation. The obtained QTLs and their genotype information will contribute to the selection of cross-breeding parents and marker-assisted selection in pineapple breeding programs.

Genomic estimated selection criteria and parental contributions in parent selection increase genetic gain of maternal haploid inducers in maize.

Parental combinations determined by genomic estimated usefulness and parental contributions of the lines in bridging population can enhance the genetic gain of traits of interest in maternal haploid inducer breeding. Parent selection in crosses aligns well with the quantitative trait performance in the progenies. We herein take advantage of estimated genetic values (EGV) and usefulness criteria (UC) of bi-parental combinations by genomic prediction (GP) to compare the empirical performance of doubled haploid inducer (DHI) progenies of eight elite inducers crosses in a half-diallel. We used parental contribution and discovery of superiors from elite-by-historical bridging populations to enhance genetic gain for long-term selection. In this empirical study, the narrow-sense heritabilities of four traits of interest (Days to flowering, DTF; haploid induction rate, HIR; plant height, PHT; Total primary branch length, PBL) in DHI population were 0.81, 0.71, 0.45 and 0.46, respectively. The genomic estimated EGV_Mid/Mean and EGV/UC_Inferior was significantly correlated with the sample mean of progenies and inferiors in four traits in the breeding and bridging population. EGV/UC_Superior were significantly correlated with the mean of superiors in DTF, PHT, and PBL in breeding and bridging populations. The genomic estimated parent contributions in DH progenies of bridging populations enabled discovery of favorable genome region from historical inducers to improve the genetic gain of HIR for long-term selection.

Genetic Programming With Lexicase Selection for Large-Scale Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling is a prominent combinatorial optimisation problem with many real-world applications. Genetic programming has been widely used to automatically evolve effective scheduling heuristics for dynamic flexible job shop scheduling. A limitation of genetic programming is the premature convergence due to the loss of population diversity. To overcome this limitation, this work considers using lexicase selection to improve population diversity, which has achieved success on regression and program synthesis problems. However, it is not trivial to apply lexicase selection to genetic programming for dynamic flexible job shop scheduling, since a fitness case (training scheduling simulation) is often large-scale, making the fitness evaluation very time-consuming. To address this issue, we propose a new multi-case fitness scheme, which creates multiple cases from a single scheduling simulation. Based on the multi-case fitness, we develop a new genetic programming algorithm with lexicase selection, which uses a single simulation for fitness evaluation, thus achieving a better balance between the number of cases for lexicase selection and evaluation efficiency. The experiments on a wide range of dynamic scheduling scenarios show that the proposed algorithm can achieve better population diversity and final performance than the current genetic programming parent selection methods and a state-of-the-art deep reinforcement learning method.

Selection of superior Brassica napus L. hybrids by integrated graphical and numerical diallel analysis by yield×traits: Using hayman and biplot diallel approaches

Improving yield via assessment of different yield-related genetic parameters specifically with the challenges of yield × traits combination is a prominent issue for plant breeders. A graphical and numerical description of diallel analysis by yield × traits with the novelty of the parent selection with desirable genetic architecture in rapeseed was illustrated via Hayman and biplot approaches. Eleven divers' parents and their F1 offspring, achieved via a complete diallel mating fashion, were evaluated under field conditions based on randomized complete block design replicated thrice throught 2018–2020. Integrated yield × traits analysis revealed highly significant additive and non-additive gene effects which were confirmed by the meaningful general and specific combining ability of evaluated multi-traits. Graphical Wr-Vr analysis, depicted partial dominance gene action for yield × traits, except yield × thousand grain yield which is governed by over-dominance gene action.The biplot polygon panorama identified the most desirable heterotic parents with higher general and specific combining ability, and more dominant genes including [(P9) × (P4, P6, P10) for yield × pod]; [(P9) × (P7), (P6) × (P11) and (P7) × (P10) for yield × thousand grain yield] and [(P10 × P3) for yield/flowering, yield/physiological maturity, yield/plant height]. These findings suggested that a breeder should consider superior parents with high × high general and specific combining ability according to combining their yield and its related genetic parameters as important traits with all other target characters via descripted approaches.

A comparison of the physicochemical properties, digestibility, and expression patterns of starch-related genes of two supersweet corn hybrids (F1) and their parents

The quality difference of corn largely depends on parental selection. Herein, the structure, digestive characteristics, and expression patterns of starch-related genes of two supersweet maize hybrids and their parents were studied. The structural analysis revealed that the starch of supersweet corn is round or oval, and the particles are smaller compared to those of normal corn. Hybridization changed the grain morphology, crystal, and helical structure of starch. Parents had a significantly different influence on supersweet corn. Notably, hybridization improved the setback value and digestibility of Shantian1500F1 and Shantian2000F1 compared to that of the parents. ZmBEI, ZmPHOH, and ZmAGPL2 genes had a consistent high expression throughout the whole grain formation phase. The results of this study expand our understanding of the breeding of supersweet corn hybrids and the effect of parents on the new strand. These results provide a useful reference for further breeding and studies of supersweet corn for starch production in corn.

Heterotic grouping of wheat hybrids based on general and specific combining ability from line × tester analysis.

The most important step in plant breeding is the correct selection of parents, and it would be wise to use heterotic groups for this. The purpose of this study is to analyse yield and its components as well as genetic diversity in line ×tester wheat populations. It also seeks to present a coherent framework for the isolation of early superior families and the development of heterotic groups in bread wheat. F1 and F2 generations of 51 genotypes, including 36 combinations between 12 lines and three testers and 15 parents, were evaluated for yield and its components in a three-replication experiment according to the randomized block design. Line × tester analysis of variance, general and specific combining abilities, heterosis, heterobeltiosis and inbreeding depression were calculated. Heterotic groups created based on general and specific combining abilities were compared with each other. The results showed that there was sufficient genetic variation in the population and that further genetic calculations could be made. The selections made based on general and specific combining abilities, heterosis values and average performance of genotypes without heterotic grouping indicated different genotypes for each feature. The creation of heterotic groups made it possible to select genotypes that were superior in terms of all the criteria listed. It was concluded that heterotic groups created based on specific combining abilities may be more useful for breeding studies.

Optimizing purebred selection to improve crossbred performance.

Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations ( ). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels of . Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios as values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy when was less than 0.5. However, at = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across all levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of . Furthermore, when was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, the value emerged as a critical factor in predicting CP based on purebred data. However, the optimal model to maximize CP depends on the factors influencing . Consequently, ongoing research aims to develop models that optimize purebred selection, further enhancing CP.

The Genetic Diversity of 69 Widely Used Chinese Sorghum Hybrids Released between the 1970s and 2010s

Sorghum has a long history of cultivation in China. In this study, we aimed to clarify the genetic relationships and genetic variation trends in widely used Chinese sorghum hybrids which were released from the 1970s to 2010s and attempted to analyze the changes in sorghum breeding. A total of 257 alleles were detected by 51 polymorphic SSR markers among 69 widely used hybrids; an average of 5.04 alleles were detected by each marker. The average Shannon’s index and polymorphism information content (PIC) of markers were 1.39 and 0.70, respectively. Nei’s genetic diversity index continuously increased in four different breeding development stages (1973–1982, 1983–1992, 1993–2002, and 2003–2014). Genetic diversity gradually increased among the sorghum hybrids. Genetic similarity coefficients in the four breeding development stages first showed an increasing trend, and then a decreasing trend, finally stabilizing with an average value of 0.65. The genetic similarity changes in hybrids in early and late maturing areas were consistent at different breeding development stages. The genetic similarity coefficients in late maturing areas were constantly higher than those in the early maturing areas. This is related to China’s creative utilization of A2 cytoplasmic male sterile materials in the 1990s. A cluster analysis determined that 69 hybrids were divided into two groups, A and B. Group A could be further subdivided into four subgroups. These findings could provide a reference for parental selection and hybrid breeding in sorghum improvement programs.

A survey of genetic algorithms for clustering: Taxonomy and empirical analysis

Clustering, an unsupervised learning technique, aims to group patterns into clusters where similar patterns are grouped together, while dissimilar ones are placed in different clusters. This task can present itself as a complex optimization problem due to the extensive search space generated by all potential data partitions. Genetic Algorithms (GAs) have emerged as efficient tools for addressing this task. Consequently, significant advancements and numerous proposals have been developed in this field.This work offers a comprehensive and critical review of state-of-the-art mono-objective Genetic Algorithms (GAs) for partitional clustering. From a more theoretical standpoint, it examines 22 well-known proposals in detail, covering their encoding strategies, objective functions, genetic operators, local search methods, and parent selection strategies. Based on this information, a specific taxonomy is proposed. In addition, from a more practical standpoint, a detailed experimental study is conducted to discern the advantages and disadvantages of approaches. Specifically, 22 different cluster validation indices are considered to compare the performance of clustering techniques. This evaluation is performed across 94 datasets encompassing diverse configurations, including the number of classes, separation between classes, and pattern dimensionality. Results reveal interesting findings, such as the key role of local search in optimizing results and reducing search space. Additionally, representations based on centroids and labels demonstrate greater efficiency and crossover and mutation operators do not prove to be as relevant. Ultimately, while the results are satisfactory, real-world clustering problems introduce additional complexity, especially for algorithms aiming to determine the number of clusters, resulting in diminished performance and the need for new approaches to be explored. Code, datasets and instructions to run algorithms in the LEAL library are available in an associated repository, in order to facilitate future experiments in this environment.