Process Of Natural Selection Research Articles

Catalyst for Common Law Evolution: Experiment with ChatGPT and a Hypothetical Common Law Jurisdiction

Abstract This paper aims to carry out empirical analysis of the viability of large language models (LLMs), specifically ChatGPT, in simulating the common law system and facilitating its evolutionary processes. Drawing on the Theory of Rules Evolution, it is understood that common law generates efficient rules by natural selection through constant litigation. Nonetheless, this evolutionary mechanism faces several hindrances. The process of change is typically slow and incremental. Courts often have to wait for a case that’s deemed ‘appropriate’ before they can change the law, leading to extended delays. Additionally, courts frequently struggle to make efficient decisions due to limited information. Other factors that decelerate the creation of efficient rules include judicial bias, unequal distribution of resources among litigating parties, and the diminishing presence of a competitive legal order. This study first assesses ChatGPT’s capability to embrace the essence of the common law system, namely the doctrine of stare decisis. We then assess its potential to overcome the hindrances in common law development and promote efficient rules. Through a series of meticulously designed hypothetical cases set in a virtual jurisdiction called the “Matrix Kingdom,” we observed that ChatGPT mimic the functions of a common law court by citing, following, and distinguishing its own precedents, but it accomplishes this with significantly fewer resources and in less time. This implies that humans can introduce hypothetical legal situations, enabling LLMs to replicate the natural selection process observed in the common law system but with a significantly accelerated pace. Given that LLMs are trained with diverse information sources, not just the factual contexts of cases, they could potentially lower the informational constraints in decision-making. As such, LLMs might significantly contribute to the evolutionary processes of common law development. However, it is important to remain cautious of certain limitations, such as the potential for AI Hallucination and inherent biases in LLMs, which require careful consideration and management.

Clinal variation in natural populations of Drosophila melanogaster: An old debate about natural selection and neutral processes.

Distinguishing between environmental adaptations and neutral processes poses a challenge in population genetics and evolutionary studies, particularly when phenomena can be explained by both processes. Clines are genotypic or phenotypic characters correlated with environmental variables, because of that correlation, they are used as examples of spatially varying selection. At the same time, many genotypic clines can be explained by demographic history, like isolation by distance or secondary contact zones. Clines have been extensively studied in Drosophila melanogaster, especially in North America and Australia, where they are attributed to both differential selection and various demographic processes. This review explores existing literature supporting this conclusion and suggests new approaches to better understand the influence of these processes on clines. These innovative approaches aim to shed light on the longstanding debate regarding the importance of natural selection versus neutral processes in maintaining variation in natural populations.

Dry Weather Adaptations in Wastewater Treatment: Innovative Control Strategies for Effective Organic and Nitrogen Elimination

Through a meta-heuristic framework, this study examines various wastewater treatment methods in detail and proposes a novel application of genetic algorithms (GAs) in plant optimization. ASM models are adapted to include ion speciation and pairing models, and microplastics (MPs) are challenged, indicating the need for further research. An integrated model accounts for carbon, nitrogen, phosphorus, oxygen, and hydrogen, emphasizing pH’s crucial role in biological treatment processes by examining microbial growth rates and organic compound removal. By applying natural selection and evolutionary processes, GAs are investigated as an optimization tool for plants, improving gene sequence structures and, by extension, treatment processes. The importance of this is particularly evident when dealing with non-standard numerical solutions and algebraic calculations. A robust and adaptable wastewater treatment strategy that accommodates variable weather conditions is provided by the study, which illustrates GAs, their stopping conditions, and the selection process for fitness functions.

Revisiting ‘survival of the fittest’ principle in global stochastic optimisation: Incorporating anisotropic mutations

Evolutionary methods of global stochastic optimisation are very popular in the literature with various practical applications, including modelling of natural disasters. To enhance efficiency of optimisation, advanced algorithms seek to incorporate the heterogeneity of population (trial points) and consider more complex scenarios of mutations and recombination. On the other hand, most of the existing evolutionary algorithms have the major drawback that they cannot guarantee the convergence to the global extremum, in other words, such algorithms are purely heuristic. Here we proposed a new approach to global stochastic optimisation using evolutionary algorithms, where we can rigorously show convergence to the optimal solution with probability of one. This was a challenge for previous heuristic algorithms of optimisation. We provide the proof of convergence of the novel algorithm both for the case, where the parameter space has a finite dimensionality, and for optimisation in function spaces, with infinite number of dimensions. The crux of the proposed algorithms is to describe optimisation as a long-term natural selection process, which is a result of interplay between competition for limited resources and mutation, which is known as survival of the fittest principle. This is a modification of Monte-Carlo optimisation, where at each iteration, the search is refined by generating mutations to eventually attain the neighbourhood with the maximal population fitness. Unlike the previous algorithms, using survival of the fittest philosophy, we incorporate the anisotropic mutation for different spatial coordinates. We also suggest implementation of combination of the local and the global search. Using various multi-modal test functions in high dimensional spaces, we show that the new optimisation methods can demonstrate a better performance than some other well-known bio-inspired optimisation algorithms. As an important practical application, we implement our method to find the optimal control strategy in a multi-agent model of spread of infectious diseases. We argue that incorporating anisotropy in mutations is a promising direction in constructing more efficient evolutionary methods of optimisation, which has not been properly exploited in previous algorithms yet.

Genetic-efficient fine-tuning with layer pruning on multimodal Covid-19 medical imaging

Medical image analysis using multiple modalities refers to the process of analyzing and extracting information from more than one type of image in order to gain a comprehensive understanding of a given subject. To maximize the potential of multimodal data in improving and enhancing our understanding of the disease, sophisticated classification techniques must be developed as part of the integration process to classify meaningful information from different types of data. A pre-trained model, such as those trained on large datasets such as ImageNet, has learned rich representations that can be used for various downstream tasks. Fine-tuning a pre-trained model refers to the process of further developing the model using the knowledge and representations gained from a pre-existing dataset. In comparison to training a model from scratch, fine-tuning allows knowledge to be transferred from the pre-trained model to the target task, thus improving performance and efficiency. In evolutionary search, the genetic algorithm (GA) is an algorithm that emulates the process of natural selection and genetics. In this context, a population of candidate solutions is generated, fitness is evaluated and new candidate solutions are generated by applying genetic operations such as mutation and crossover. Considering the above characteristics, the present study presents an efficient architecture called Selective-COVIDNet for analyzing COVID-19 cases using a novel selective layer-pruning algorithm. To detect COVID-19 from multimodal data, the current study will use a genetic algorithm to fine-tune the performance of pre-trained models by adjusting specific layers selectively. Furthermore, the proposed approach provides flexibility in the depth of two deep learning architectures, VGG-16 and MobileNet-V2. The impact of freezing specific layers on fine-tuning performance was assessed using five different strategies, namely Random, Odd, Even, Half, and Full Freezing. Therefore, existing pre-trained models can be enhanced for Covid-19 tasks while minimizing their computational burden. For evaluating the effectiveness of the proposed framework, two multi-modal standard datasets are used, including CT-scan images and electrocardiogram (ECG) recordings of individuals with COVID-19. From the conducted experiments, it is found that the proposed framework can detect Covid-19 effectively with accuracy of 98.48% for MobileNet-V2 and 99.65% for VGG-16.

The Deterioration of the Human Genome Through Mutation Accumulation

Mutations are held inside the human genome, which serves as an instruction manual for our body. The genome contains our total genetic parts, including our nucleotides, chromosomes, genes, DNA, and RNA. With the human mutation rate displaying a directly proportional relationship to each generation, concern has arisen amongst biologists. When looking at the continued accumulation of mutations, one could expect progressive decay within every function of humanity. However, the Primary Axiom would not be able to arise from this perspective. The Primary Axiom is an idea shaped by Neo-Darwinism, which states that the human race was born from one initial genome after a series of mutations and natural selection. In this paper we will review the deleterious nature of mutations and the continued deterioration of the human genome since its inception. I will start by explaining the effects of the continued accumulation of various mutations, and go on to address the process of natural selection.

Integrative analysis of physiology and genomics provides insights into freeze tolerance adaptations of Acacia koa along an elevational cline.

Natural selection for plant species in heterogeneous environments creates genetic variation for traits such as cold tolerance. While physiological or molecular analyses have been used to evaluate stress tolerance adaptations, combining these approaches may provide deeper insight. Acacia koa (koa) occurs from sea level to 2300 m in Hawai'i, USA. At high elevations, natural koa populations have declined due to deforestation, and freeze tolerance is a limiting factor for tree regeneration. We used physiology and molecular analyses to evaluate cold tolerance of koa populations from low (300-750 m), middle (750-1500 m), and high elevations (1500-2100 m). Half of the seedlings were cold acclimated by exposure to progressively lowered air temperatures for eight weeks (from 25.6/22.2°C to 8/4°C, day/night). Using the whole plant physiology-freezing test and koa C-repeat Binding Factor CBF genes, our results indicated that koa can be cold-acclimated when exposed to low, non-freezing temperatures. Seedlings from high elevations had consistently higher expression of Koa CBF genes associated with cold tolerance, helping to explain variation in cold-hardy phenotypes. Evaluation of the genetic background of 22 koa families across the elevations with low coverage RNA sequencing indicated that high elevation koa had relatively low values of heterozygosity, suggesting that adaptation is more likely to arise in the middle and low elevation sources. This physiology and molecular data for cold tolerance of koa across the elevation gradient of the Hawaiian Islands provides insights into natural selection processes and may help to support guidelines for conservation and seed transfer in forest restoration efforts.

An Emerging Dilemma for Reciprocal Causation

Abstract Among advocates and critics of the “extended evolutionary synthesis” (EES), “reciprocal causation” refers to the view that adaptive evolution is a bidirectional phenomenon, whereby organisms and environments impinge on each other through processes of niche construction and natural selection. I argue that reciprocal causation is incompatible with the view that natural selection is a metaphysically emergent causal process. The emergent character of selection places reciprocal causation on the horns of dilemma, and neither horn can rescue it. I conclude that proponents of the EES must abandon the claim that the process of natural selection features in cycles of reciprocal causation.

Trends of Eurasian Perch (Perca fluviatilis) mtDNA ATP6 Region Genetic Diversity within the Hydro-Systems of the Eastern Part of the Baltic Sea in the Anthropocene.

The intraspecific genetic diversity of freshwater fish inhabiting hydro-systems of the macrogeographic area spreading from the Black to Baltic Seas requires comprehensive investigation from fundamental and practical perspectives. The current study focused on the involvement of the mtDNA ATP6 region in the adaptability and microevolution of Perca fluviatilis within phylogeographic and anthropogenic contexts. We sequenced a 627 bp fragment encompassing the ATP6 region and used it for genetic analysis of 193 perch caught in Latvia, Lithuania, Belarus, and Ukraine, representing natural and anthropogenically impacted populations. We evaluated patterns of intraspecific genetic diversity in the ATP6 region and phylogeographic trends within the studied area compared with previously established D-loop trends. Evaluation of ATP6 coding sequence variability revealed that among 13 newly detected haplotypes, only two were caused by non-synonymous substitutions of amino acids of the protein. PCoA revealed three genetic groups (I-III) based on the ATP6 region that encompassed four previously described genetic groups established based on the mtDNA D-loop. The two mtDNA regions (D-loop and ATP6) have microevolved at least partially independently. Prolonged anthropogenic impacts may generate new point mutations at the ATP6 locus, but this phenomenon could be mainly concealed by natural selection and reparation processes.

Polymorphism of the growth hormone (GH) gene and its association with growth traits in the Kilakarsal breed of sheep

The goal of this study was to investigate the single nucleotide polymorphism of the growth hormone gene and its association with growth traits in the Kilakarsal breed of sheep. Polymerase Chain Reaction – Restriction Fragment Length Polymorphism (PCR-RFLP) of the growth hormone gene (422 bp) with HaeIII restriction enzyme revealed an A781G transition mutation at the exon 2 and the existence of two genotypes, viz., AA (366 bp and 56 bp) and AB (422 bp, 366 bp and 56 bp). The occurrence of the B allele is due to the A781G transition in the exon 2 of the growth hormone gene, which causes an amino acid change from Serine to Glycine. The genotype BB was absent in the Kilakarsal sheep population. The absence of the BB genotype at the A781G locus of the growth hormone gene in the Kilakarsal sheep population indicates that the natural selection process might be acting against this particular genotype through reduced viability or early embryonic death. Further, the statistical analysis revealed the significantly higher yearling weight of the AB than the AA genotype (P=0.038), and an appreciable difference of 1.40 kg in 9-month weight. The highly significant (P<0.001) Chi-square value (29.94) showed that the population is not the under Hardy-Weinberg equilibrium. This indicates that molecular markers associated with body weight should be explored for their use in marker assisted selection in Kilakarsal population.

Presence of IgG antibodies is not a reliable marker of Toxoplasma gondii infection in feral mice

The single-celled parasite Toxoplasma gondii uses mice as a vector to reach its definitive host, the cat, where it can accomplish its sexual reproduction and produce oocysts, which will contaminate the environment. In this study, we have captured 103 feral house mice (Mus musculus) on Kangaroo Island, Australia. We have measured the level of exposure to T.gondii serologically with the Modified Agglutination Test and conjointly with a T.gondii B1 gene PCR. We have included stringent quality control steps in the molecular analysis to reduce the risk of false positivity and false negativity. Our results indicated a low seroprevalence of 0.97%, 95%CI [-0.36; 0.58] associated with the detection of T.gondii genetic material in 51.46%, 95%CI [41.93, 60.88] of mice brains. Neither sex nor mice body weight had an effect on the PCR outcome. We postulate that both the transmission route, horizontal or vertical, and natural selection processes could lead to this discordance which has been observed elsewhere in wild mice. The question of the biological mechanisms allowing the chronic infection of wild mice in the absence of a measurable humoral immune response remains. Our findings indicate that serological studies should not be used to measure the level of exposure to T.gondii in feral house mice.

Пошук новизни у методі нейроеволюції для позиціонування роботизованої кінцівки

The article considers the use of the neuroevolution algorithm for neural network policies search when creating a controller for a robotic arm, in particular for the subtask of positioning the end effector. Neuro-evolution is a family of machine learning methods that use evolutionary algorithms by imitating the process of natural selection. This approach has been found to be particularly effective for the positioning task, where the final position can be achieved in many optimal ways and therefore requires reinforcement learning. It is noted that the final result of neuroevolution is an optimal network topology, which makes the model more resource-efficient and easier to analyze. The paper considers the process of neural network policy search for controlling a two-dimensional robot with two links. According to the results of the experiments, an increase in the efficiency of the best solution found using novelty search for the NEAT algorithm is noted compared to the NEAT algorithm without novelty search. It was established that the proposed approach allows to obtain an effective neural network policy, which has a minimal configuration, which will allow to increase the speed of the controller, that is critical for the operation of a real system. Thus, the use of novelty search as a method of optimizing the neuroevolutionary process to solve the positioning problem allows to increase the efficiency of the learning process and obtain the optimal network topology.

Directional and stabilizing selection shaped morphological, reproductive, and physiological traits of the invader Solidago canadensis.

Trait evolution in invasive plant species is important because it can impact demographic parameters key to invasion success. Invasive plant species often show phenotypic clines along geographic and climatic gradients. However, the relative contributions of natural selection and neutral evolutionary processes to phenotypic trait variation among populations of invasive plants remain unclear. A common method to assess whether a trait has been shaped by natural selection or neutral evolutionary processes is to compare the geographical pattern for the trait of interest to the divergence in neutral genetic loci (i.e., Q ST -F ST comparisons). Subsequently, a redundancy analysis (RDA) can facilitate identification of putative agents of natural selection on the trait. Here, we employed both a Q ST -F ST comparisons approach and RDA to infer whether natural selection shaped traits of invasive populations of Solidago canadensis in China and identify the potential environmental drivers of natural selection. We addressed two questions: (1) Did natural selection drive phenotypic trait variation among S. canadensis populations? (2) Did climatic, latitudinal, longitudinal, and altitudinal gradients drive patterns of genetic variation among S. canadensis populations? We found significant directional selection for several morphological and reproductive traits (i.e., Q ST > F ST) and stabilizing selection for physiological traits (i.e., Q ST < F ST). The RDA showed that stem biomass of S. canadensis was strongly positively correlated with longitude, while leaf width ratio and specific leaf area were significantly positively correlated with the mean diurnal range. Stem biomass had a strong negative correlation with annual precipitation. Moreover, height of S. canadensis individuals was strongly positively correlated with altitude and precipitation of the wettest quarter. A longitudinal shift in precipitation seasonality likely selected for larger stem biomass in S. canadensis. Overall, these results suggest that longitudinal and altitudinal clines in climate exerted strong selection pressures that shaped the phenotypic traits of S. canadensis.

Microbiota revolution: How gut microbes regulate our lives.

The human intestine is a natural environment ecosystem of a complex of diversified and dynamic microorganisms, determined through a process of competition and natural selection during life. Those intestinal microorganisms called microbiota and are involved in a variety of mechanisms of the organism, they interact with the host and therefore are in contact with the organs of the various systems. However, they play a crucial role in maintaining host homeostasis, also influencing its behaviour. Thus, microorganisms perform a series of biological functions important for human well-being. The host provides the microorganisms with the environment and nutrients, simultaneously drawing many benefits such as their contribution to metabolic, trophic, immunological, and other functions. For these reasons it has been reported that its quantitative and qualitative composition can play a protective or harmful role on the host health. Therefore, a dysbiosis can lead to an association of unfavourable factors which lead to a dysregulation of the physiological processes of homeostasis. Thus, it has pre-viously noted that the gut microbiota can participate in the pathogenesis of autoimmune diseases, chronic intestinal inflammation, diabetes mellitus, obesity and atherosclerosis, neurological disorders (e.g., neurological diseases, autism, etc.) colorectal cancer, and more.

Phosphorus fluoride exchange: Multidimensional catalytic click chemistry from phosphorus connective hubs

Phosphorus Fluoride Exchange (PFEx) represents a cutting-edge advancement in catalytic click-reaction technology. Drawing inspiration from Nature's phosphate connectors, PFEx facilitates the reliable coupling of P(V)-F loaded hubs with aryl alcohols, alkyl alcohols, and amines to produce stable, multidimensional P(V)-O and P(V)-N linked products. The rate of P-F exchange is significantly enhanced by Lewis amine base catalysis, such as 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD). PFEx substrates containing multiple P-F bonds are capable of selective, serial exchange reactions via judicious catalyst selection. In fewer than four synthetic steps, controlled projections can be deliberately incorporated along three of the four tetrahedral axes departing from the P(V) central hub, thus taking full advantage of the potential for generating three-dimensional diversity. Furthermore, late-stage functionalization of drugs and drug fragments can be achieved with the polyvalent PFEx hub, hexafluorocyclotriphosphazene (HFP), as has been demonstrated in prior research.

To What Extent Can Evolutionary Theories Explain Mental Illness?

Clinical psychology, the psychiatry field, and medical conditions all tend to ignore the role of evolution in them. However, the human brain is indeed a product of natural selection. Its complexity, including both normal functions and potential dysfunctions, has developed over evolutionary time under the process of natural selection. Evolutionary psychology provides guidance for psychologists today on how to think about individual differences in terms of psychopathology. Therefore, understanding the evolutionary origin of psychiatric conditions is a crucial component of a complete understanding of psychological illnesses and their etiology. This paper explores this idea of evolution with four examples of mental illnesses, depression, postpartum depression, agoraphobia, schizophrenia and autism.

Tattooing as a phenotypic gambit.

Tattooing is not an evolved behavior, but it may be a phenotypic gambit to highlight immunological health. Phenotypic gambits are traits or behaviors that appear costly but occur at high rates as a honing process of natural selection not constrained by genetics. Tattooing is an ancient practice that is increasing in popularity worldwide, but it involves wounding the body, which seems counterintuitive because it challenges the immune system and makes one more susceptible to infection. But tattooing may represent a costly honest signal of fitness by "upping the ante" in an era of hygiene or a means to stimulate the immune system in a way that improves and highlights underlying fitness. We investigated this hypothesis by assessing bacteria killing activity (BKA) in saliva samples collected during two studies of tattooing (N = 40). We compared previous tattoo experience (extent of body tattooed and hours spent being tattooed) to BKA before and after getting a new tattoo. Tattoo experience positively predicts post-tattoo BKA (β = 0.48, p = 0.01), suggesting that people with more tattoo experience have a relatively more immediate and active immune response than those with less tattoo experience. Tattoo experience may elevate innate immunological vigilance, which could aid in protecting against future dermal insults.

Not by Design Alone! Modelling Practices to Identify Students’ Frameworks of Evolution in Real-Life Contexts

Despite being a fundamental concept in biology, evolution continues to be one of the most challenging topics to teach in science education. Ideas of evolution emphasising anatomical or behavioural features of individuals, as opposed to the interplay between genetics and the environment, are reinforced through language and culture, making them robust and persistent in the student population at all educational levels. Model-based reasoning has been reported to be useful for students to make sense of process-based science content, combining epistemological with linguistic and value dimensions. However, there is a dearth of evidence in biology education showing how modelling can instigate epistemological maturity, specifically about issues of agency and design in evolution by natural selection. Drawing on this perspective, this study focuses on describing the nature of students’ ideas while modelling the resistance developed by a population of mosquitoes in a lagoon after an insecticide is introduced. Data collection includes students’ written reports and drawings, which were analysed with content and discourse analysis. The findings show that, at first, students believed adaptation to feature at will was a behavioural characteristic instigated by a pre-existing design. After modelling the process of natural selection, the explanations appeared to improve (from Lamarckian to Neo-Darwinian views), and most groups showed accurate explanations about adaptation.

Genome Scan of Rice Landrace Populations Collected Across Time Revealed Climate Changes’ Selective Footprints in the Genes Network Regulating Flowering Time

Analyses of the genetic bases of plant adaptation to climate changes, using genome-scan approaches, are often conducted on natural populations, under hypothesis of out-crossing reproductive regime. We report here on a study based on diachronic sampling (1980 and 2011) of the autogamous crop species, Oryza sativa and Oryza glaberrima, in the tropical forest and the Sudanian savannah of West Africa. First, using historical meteorological data we confirmed changes in temperatures (+ 1 °C on average) and rainfall regime (less predictable and reduced amount) in the target areas. Second, phenotyping the populations for phenology, we observed significantly earlier heading time in the 2010 samples. Third, implementing two genome-scan methods (one of which specially developed for selfing species) on genotyping by sequencing genotypic data of the two populations, we detected 31 independent selection footprints. Gene ontology analysis detected significant enrichment of these selection footprints in genes involved in reproductive processes. Some of them bore known heading time QTLs and genes, including OsGI, Hd1 and OsphyB. This rapid adaptive evolution, originated from subtle changes in the standing variation in genetic network regulating heading time, did not translate into predominance of multilocus genotypes, as it is often the case in selfing plants, and into notable selective sweeps. The high adaptive potential observed results from the multiline genetic structure of the rice landraces, and the rather large and imbricated genetic diversity of the rice meta-population at the farm, the village and the region levels, that hosted the adaptive variants in multiple genetic backgrounds before the advent of the environmental selective pressure. Our results illustrate the evolution of in situ diversity through processes of human and natural selection, and provide a model for rice breeding and cultivars deployment strategies aiming resilience to climate changes. It also calls for further development of population genetic models for adaptation of plant populations to environmental changes. To our best knowledge, this is the first study dealing with climate-changes’ selective footprint in crops.

An Efficient Hybrid of an Ant Lion Optimizer and Genetic Algorithm for a Model Parameter Identification Problem

The immense application of mathematical modeling for the improvement of bioprocesses determines model development as a topical field. Metaheuristic techniques, especially hybrid algorithms, have become a preferred tool in model parameter identification. In this study, two efficient algorithms, the ant lion optimizer (ALO), inspired by the interaction between antlions and ants in a trap, and the genetic algorithm (GA), influenced by evolution and the process of natural selection, have been hybridized for the first time. The novel ALO-GA hybrid aims to balance exploration and exploitation and significantly improve its global optimization ability. Firstly, to verify the effectiveness and superiority of the proposed work, the ALO-GA is compared with several state-of-the-art hybrid algorithms on a set of classical benchmark functions. Further, the efficiency of the ALO-GA is proved in the parameter identification of a model of an Escherichia coli MC4110 fed-batch cultivation process. The obtained results have been studied in contrast to the results of various metaheuristics employed for the same problem. Hybrids between the GA, the artificial bee colony (ABC) algorithm, the ant colony optimization (ACO) algorithm, and the firefly algorithm (FA) are considered. A series of statistical tests, parametric and nonparametric, are performed. Both numerical and statistical results clearly show that ALO-GA outperforms the other competing algorithms. The ALO-GA hybrid algorithm proposed here has achieved an improvement of 6.5% compared to the GA-ACO model, 7% compared to the ACO-FA model, and 7.8% compared to the ABC-GA model.