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The genetic algorithm (GA) is an optimization and search technique based on
the principles of genetics and natural selection. A GA allows a population
composed of many individuals to evolve under specified selection rules to a
state that maximizes the "fitness" function. In that process, crossover
operator plays an important role. To comprehend the...
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... I NTRODUCTION Genetic algorithms (GAs) represent general-purpose search and optimization technique based on evolutionary ideas of natural selection and genetics. They simulate natural processes based on principles of Lamarck and Darwin. In 1975, Holland developed this idea in his book “Adaptation in natural and artificial systems”. He described how to apply the principles of natural evolution to optimization problems and built the first GAs. Holland’s theory has been further developed and now GAs stand up as a powerful tool for solving search and optimization problems. GAs are based on the principle of genetics and evolution [1] . Today, there exists many variations on GAs and term “genetic algorithm” is used to describe concepts sometimes very far from Holland’s original idea [2]. The two most commonly employed genetic search operators are crossover and mutation. Crossover produces offspring by recombining the information from two parents. Mutation prevents convergence of the population by flipping a small number of randomly selected bits to continuously introduce variation. The driving force behind GAs is the unique cooperation between selection, crossover and mutation operator. A genetic operator is a process used in GAs to maintain genetic diversity. The most widely used genetic operators are recombination, crossover and mutation. The main goal of this paper is to introduce a new crossover operator called ring crossover (RC) and present the performance of this crossover operator. The rest of this paper is organized as follow. In section 2, definitions and concepts of the different crossover operators are introduced. In section 3, the proposed method in this study is given. In section 4, a number of the functions widely used in performance evaluation of GA operators are defined. In section 5, the optimization results and performance comparison of proposed method are shown. Finally, conclusions are discussed in section 6. II. C ROSSOVER OPERATORS The crossover operator is a genetic operator that combines (mates) two chromosomes (parents) to produce a new chromosome (offspring). The idea behind crossover is that the new chromosome may be better than both of the parents if it takes the best characteristics from each of the parents. Crossover occurs during evolution according to a user- definable crossover probability. For purpose of this work, only crossover operators that operate on two parents and have no self-adaptation properties will be considered. When performing crossover, both parental chromosomes are split at a randomly determined crossover point. Subsequently, a new child genotype is created by appending the first part of the first parent with the second part of the second parent [3, 4]. A single crossover point on both parents' organism strings is selected. All data beyond that point in either organism string is swapped between the two parent organisms. Figure 1 shows the single point crossover (SPC) process. Apart from SPC, many different crossover algorithms have been devised, often involving more than one cut point. It should be noted that adding further crossover points reduces the performance of the GA. The problem with adding additional crossover points is that building blocks are more likely to be disrupted. However, an advantage of having more crossover points is that the problem space may be searched more thoroughly. In two-point crossover (TPC), two crossover points are chosen and the contents between these points are exchanged between two mated parents [5, 6] In figure 2, the arrows indicate the crossover points. Thus, the contents between these points are exchanged between the parents to produce new children for mating in the next generation. Intermediate creates offsprings by a weighted average of the parents. Intermediate crossover (IC) is controlled by a single parameter Ratio : If Ratio is in the range [0,1] then the offsprings produced are within the hypercube defined by the parents locations at opposite vertices. Ratio can be a scalar or a vector of length number of variables. If Ratio is a scalar, then all of the offsprings will lie on the line between the parents. If Ratio is a vector then children can be any point within the hypercube [7]. In heuristic crossover (HC), heuristic returns an offspring that lies on the line containing the two parents, a small distance away from the parent with the better fitness value in the direction away from the parent with the worse fitness value. The default value of Ratio is 1.2. If parent1 and parent2 are the parents, and parent1 has the better fitness value, the function returns the child [7], In arithmetic crossover (AC), arithmetic creates children that are the weighted arithmetic mean of two parents. Children are feasible with respect to linear constraints and bounds. Alpha is random value between [0,1]. If parent1 and parent2 are the parents, and parent1 has the better fitness value, the function returns the child [7], III. P ROPOSED CROSSOVER OPERATOR : RING CROSSOVER The operator called ring crossover is consisted of four steps. The steps of the proposed operator in this paper are shown in figure 3. All of the steps in the algorithm are discussed one by one. Step-1: In this step, two parents such as parent1 and parent2 are considered for the crossover process, as shown in fig. 3(a). Step-2: The chromosomes of parents are firstly combined with a form of ring, as shown in fig. 3(b). Later, a random cutting point is decided in any point of ring. Step-3: The children are created with a random number generated in any point of ring according to the length of the combined two parental chromosomes. With reference to the cutting point in step 2, while one of the children is created in the clockwise direction, the other one is created in direction of the anti-clockwise, as shown in fig. 3(c). Step-4: In this step, swapping and reversing process is performed in the RC operator, as shown in fig. 3(d). In swapping process, a number of genes are swapped in crossed parents. In reversion process, the remaining genes are reversed in crossed parents. As the length of ring is equal to the total length of both of parents and the children are created according to a random point of ring, more variety can be provided in possible number of children by RC operator according to SPC and TPC ...
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Genetic algorithm includes some parameters that should be adjusting so that
the algorithm can provide positive results. Crossover operators play very
important role by constructing competitive Genetic Algorithms (GAs). In this
paper, the basic conceptual features and specific characteristics of various
crossover operators in the context of the Trav...
Citations
... Additionally, they employ other operators to generate novel solutions. The variability among these algorithms lies in the standard representation of problems and the nature and relative significance of the operations introducing new solutions [52,53,54]. ...
Data-driven models, in general, and machine learning (ML) models, in particular, have gained popularity over recent years with an increased usage of such models across the scientific and engineering domains. When using ML models in realistic and dynamic environments, users need to often handle the challenge of concept drift (CD). In this study, we explore the application of genetic algorithms (GAs) to address the challenges posed by CD in such settings. We propose a novel two-level ensemble ML model, which combines a global ML model with a CD detector, operating as an aggregator for a population of ML pipeline models, each one with an adjusted CD detector by itself responsible for re-training its ML model. In addition, we show one can further improve the proposed model by utilizing off-the-shelf automatic ML methods. Through extensive synthetic dataset analysis, we show that the proposed model outperforms a single ML pipeline with a CD algorithm, particularly in scenarios with unknown CD characteristics. Overall, this study highlights the potential of ensemble ML and CD models obtained through a heuristic and adaptive optimization process such as the GA one to handle complex CD events.
... as a benchmark test [34]. The resulting performance of the proposed GA-PSO algorithm over 100 iterations is compared to improved GA and improved PSO, respectively. ...
Autonomous driving is a complex and highly dynamic process that ensures controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control, distinguished by its predictive feature, optimal performance, and ability to handle constraints, makes it one of the most promising tools for this type of control application. The content of this article handles the problem of autonomous driving by proposing an adaptive linear parameter varying model predictive controller (LPV-MPC), where the controller's prediction model is adaptive by means of a recurrent neural network. The proposed LPV-MPC is further optimised by a hybrid Genetic and Particle Swarm Optimization Algorithm (GA-PSO). The developed controller is tested and evaluated on a challenging track under variable wind disturbance.
... By integrating these techniques, the KGWO algorithm aims to reduce convergence time, improve path smoothness, and optimize path length. The elite strategy ensures that the best solutions are retained and propagated through the generations, while the PMX mutation introduces diversity and helps escape local optima [25,26]. This hybrid approach not only accelerates the convergence process but also increases the robustness of the algorithm, making it less susceptible to getting stuck in local optima. ...
To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method’s potential in real-world applications.
... Two chromosomes (parents) are combined by the crossover operator to create new chromosomes (offspring). This can be applied in GA in a variety of ways, including a heuristic crossover, two-point crossover and single-point crossover [34]. Therefore, GAs are not only valued in theoretical research but also demonstrate significant potential and value in practical applications. ...
In today’s interconnected world, teamwork and collaboration are becoming essential competencies across all disciplines. This review examines various pedagogical strategies aimed at nurturing these skills, with a specific focus on integrating algorithms into educational practices. While traditional approaches classify teamwork strategies as either instructor-led or student-led, this review introduces a third method that is based on ML algorithms, which are promising methods for optimizing team composition based on both static and dynamic student characteristics. We investigate the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on the implementation of these technologies in educational settings, evaluating their impact on learning outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced strategies not only improve the academic and collaborative experience but also pave the way for more personalized and dynamic educational environments. This review aims to provide educators and curriculum developers with comprehensive insights into leveraging advanced computational tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall quality of education and preparing students for the collaborative demands of the professional world.
... The arithmetic crossover operator is used to join the two parent chromosomes in a linear fashion. In an arithmetic crossover, two chromosomes are selected randomly to cross, and they are linearly connected to form two offspring [35]. The following computation yields this linear combination: ...
Cloud providers frequently utilise two tightly
coupled resource management strategies like task scheduling &
data replication to boost performance of the system
generally, guarantee service level agreement (SLA) compliance,
as well as protect their own financial gain. Nevertheless, it can be
quite difficult to strike a balance among system performance &
supplier profit. The purpose of this study is to provide an
Improved Correlation strategy based Task Scheduling and Data
replication in Cloud (ICTSDC). The main phases of the suggested
system are as follows: Task scheduling and Replication
management. Initially, optimization-based task scheduling will
takes place under the consideration of objectives like Bottleneck
value, migration cost, VM load, improved correlation, and
replication, respectively. For this, a new Self-adaptive Dwarf
Mongoose Optimization (SADMO) Algorithm will be introduced,
which is the extended version. In Replication management step,
the first it needs to identify the candidate replicas based on the
objective. Replication is the reciprocal of improved correlation.
Further, the placement of replicas is carried out under the
optimization strategy by proposing SADMO model. The
outcomes of the ICTSDC technique are evaluated with those of
other methods using a variety of metrics, like bottleneck value,
migration cost, VM load, improved correlation, as well as
replication efficiency.
Keywords— Task Scheduling; Data Replication; Cloud;
Improved Correlation; Optimization.
... In addition, the crossover operator used is based on the Select Any Crossover (SAC) approach (Hassanat & Alkafaween, 2017) with the Order Crossover (OC) (Davis, 1985) and the Ring Crossover (RC) (Kaya et al., 2011). The underline motivation is that the SAC allows a wider range of crossover options, allowing the algorithm a better exploration. ...
The ability of governments to accurately forecast tax revenues is essential for the successful implementation of fiscal programs. However, forecasting state government tax revenues using only aggregate economic variables is subject to Lucas’s critique, which is left not fully answered as classical methods do not consider the complex feedback dynamics between heterogeneous consumers, businesses, and the government. In this study we present an agent-based model with a heterogeneous population and genetic algorithm-based decision-making to model and simulate an economy with taxation policy dynamics. The model focuses on assessing state tax revenues obtained from regions or cities within countries while introducing consumers and businesses, each with unique attributes and a decision-making mechanism driven by an adaptive genetic algorithm. We demonstrate the efficacy of the proposed method on a small village, resulting in a mean relative error of 5.44%±2.45% from the recorded taxes over 4 years and 4.08%±1.21 for the following year’s assessment. Moreover, we demonstrate the model’s ability to evaluate the effect of different taxation policies on economic activity and tax revenues.
... The other location was the length of the chromosome; the first offspring develops clockwise from the line (the original cut), and the second offspring evolves counter clockwise. They employed this type of crossover for the aspects mentioned and it outperformed the other types of assessed crossover [19]. Despite that, to prevent creating erroneous solutions, evolutionary algorithms that optimize the ordering of a very large series require specific Table 1 and each of them has been generated for a specific global solution. ...
The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select the best-fit point. The high efficiency of crossover operators allows engineers to minimize errors in engineering application optimization while saving time and avoiding overpricing. There are two crucial objectives behind this paper; first, we provide an overview of the crossover standards classification that has been used by researchers for solving engineering operations and problem representation. This paper proposes a novel standard crossover based on the Lagrangian Dual Function (LDF) to enhance the formulation of the Lagrangian Problem Crossover (LPX). The LPX for 100 generations of different pairs parent chromosomes is compared to Simulated Binary Crossover (SBX) standards and Blended Crossover (BX) for real-coded crossovers. Three unimodal test functions with various random values show that LPX has better performance in most cases and comparative results in other cases. Moreover, the LPB algorithm is used to compare LPX with SBX, BX, and Qubit Crossover (Qubit-X) operators to demonstrate accuracy and performance during exploitation evaluations. Finally, the proposed crossover stand operator results are demonstrated, proved, and analyzed statistically by the Wilcoxon signed-rank sum test.
... i as a benchmark test [22], and the resulting performance over 100 iterations is compared to GA with either RWS or TS methods separately. Fig. 6 shows that the improved version is indeed faster and able to find more optimal solutions, reaching a cost value of 25.029 compared to 27.725 and 28.722 for the GA with RWS and TS respectively. ...
... Types of MI (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)); however, none of the differences in subpopulations reached a p-value below 0.05 (see also Supplementary Table 2 for detailed information on the absolute and relative counts of the specific monocyte populations). ...
... Though DES has been found to be equally effective in small and large vessels, the resulting LLL occupies a higher percentage of the respective vessel diameter, leading to a higher incidence of ISR and other clinical events (40). Drug-coated balloon angioplasty has the theoretical advantage of providing immediate and homogenous drug uptake, leaving no metal in the coronary artery and respecting the vessel anatomy, thus forming a "leave nothing behind" strategy in the treatment of do novo CAD (41). Many notable RCTs involving small vessel disease have used this strategy and all studies have shown the benefits of DCB except the PICCOLETO (42) ( Table 3), which may be explained as the limitations of the first-generation Dior DCB (49). ...
... Since a high prevalence of diabetesrelated complications and comorbidities may further impair nutritional status (40). Compared with those without diabetes, people with diabetes are more likely to suffer from malnutrition due to the diabetes itself, injuries, medications, and other factors affecting metabolism (41), which suggests that malnutrition may contribute to a higher risk of adverse CV events in ACS patients with diabetes vs. without diabetes. ...
Background: Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is
angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography
are costly and have side effects. One of the alternative solutions is the use of machine
learning-based patterns for CAD diagnosis.
Methods: Hence, this paper provides a new hybrid machine learning model called
genetic support vector machine and analysis of variance (GSVMA). The analysis of
variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed
model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization
algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM
(LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF)
methods were applied to classify the dataset.
Results: As a result, the GSVMA hybrid method performs better than other methods.
This proposed method has the highest accuracy of 89.45% through a 10-fold
crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset.
Conclusion: We demonstrated that SVM combined with genetic optimization algorithm
could be lead to more accuracy. Therefore, our study confirms that the GSVMA method
outperforms other methods so that it can facilitate CAD diagnosis.
... Reproduction is implemented to exchange some genes of the parents' chromosomes. It can be approached using a one-point crossover, a two-point crossover or a multi-point crossover method (Kaya et al., 2011). The use of an adequate number of crossover points can significantly improve the performance of the genetic algorithm. ...
Abstract video:
https://alperkanyilmaz.com/decision-making-using-artificial-intelligence-in-civil-engineering/
The conceptual design decisions have the largest influence on a building project’s safety, value, and environmental impact; hence they are commonly assigned to a “senior engineer” to make use of his/her experience. However, the senior engineers can be biased towards solutions inside their area of expertise, which often prevents them from finding the best solutions among alternatives that must consider complex inter-related, and multi-disciplinary parameters. The engineering community could benefit from a rapid and high-quality decision-making method or tool to increase the speed and quality of its high-impact design choices. There are valuable studies in the literature exploiting Artificial Intelligence (AI) to improve the structural design process; however, most of them focus on the final design stage (e.g., Building Information Modeling), and the rest requires an existing project database (e.g., architectural drawings, already decided material types) to propose a small number of initial design alternatives. In this article, we present the development and validation of a genetic algorithm tool based on Non-dominated Sorted Genetic Algorithm II (NSGA-II) that can be used to analyse a wide range of safe, economical and low-CO2 options for the conceptual design of buildings. The design space starts from a design brief (with only the information about the site characteristics and project objectives). The solutions are explored with the material, grid size, floor type, lateral resistance, and foundation system variables. In a short computational time (< 2 min per run), users are provided with a Pareto graph of a large set of feasible solutions (in terms of cost, embodied CO2 emissions and free space) that an engineer would not be typically able to evaluate within a traditional conceptual design process. For future applications, the methodology presented in this paper is flexible to include more engineering materials (e.g., timber, masonry, structural glass), complex architectural forms and merge other disciplines in decision making (e.g., building physics construction management, fire safety).