Crossover Probability Research Articles

On capacity and performance of noncoherent OFDM-IM systems with binary codes

This paper studies the capacity and error performance of a noncoherent index-modulated orthogonal frequency division multiplexing (OFDM-IM) system with binary coded subcarrier activation patterns. To that end, soft-decision (i.e., unquantized) and hard-decision (i.e., quantized) channels are considered, where the latter is equivalent to a binary asymmetric channel (BAC). By using maximum likelihood and maximum a posteriori criteria, crossover probabilities of the quantized channel are derived in order to characterize its capacity and probability of error in detecting IM signals. Capacity expressions are then given for both channels. Using the Blahut-Arimoto algorithm, an iterative algorithm is developed to find the optimal input distribution for the unquantized channel, while for the quantized channel, it is found by solving a nonlinear equation for its zero. It is shown that using a uniform input distribution instead of the optimal (nonuniform) one results in a negligible capacity loss, therefore binary codes with a uniform distribution of 1s and 0s are nearly optimal. Compared to the unquantized channel, it is shown that an approximate loss less than 2dB is incurred when quantization is used and this loss diminishes rapidly as signal-to-noise ratio (SNR) increases. In addition, it is demonstrated that the gap (in dB) between uncoded noncoherent OFDM-IM system and the capacity limit is generally wide, and the gap narrows when subcarrier clustering is considered. The findings in this paper can have theoretical and practical significance for understanding capacity limits and can provide some guidance for coding design in noncoherent OFDM-IM systems.

A two-variable control and optimization method for imbalance of high pressure compressor based on improved genetic algorithm.

To solve the problem of low quality rate for one-time assembly of high-pressure compressors, an improved genetic algorithm (GA) is used to adjust and optimize the imbalance after assembly. This paper takes the post-assembly imbalance of a multi-stage rotor of a high-pressure compressor as the objective function, to reduce the post-assembly imbalance by adjusting the arrangement order of rotor blades and the assembly phase between rotors. We used a four-sector staggered distribution method to generate high-quality initial populations and added an elite retention strategy. The crossover and mutation probabilities are adaptively adjusted according to the fitness function values. The threshold termination condition is added to make the algorithm converge quickly so as to achieve fast, stable, and efficient search. The simulation results show that the imbalance is reduced by 99.46% by using the improved genetic algorithm, which is better than the traditional GA. The experimental results show that the imbalance of the two correction surfaces can be reduced to 640 and 760 g·mm, respectively, which is 86.7% and 87.1% better than the zero-degree assembly.

Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared.

In the past few decades, there has been significant progress made in metasurfaces and integrated and miniaturized optical devices. As one of the most prominent applications of metasurfaces, the metalens is the subject of significant research. In this paper, for achieving better focusing performance of the initial metalens designed by the Pancharatnam-Berry (PB) phase, a concept of micro-dimensional oscillation is proposed to optimize the geometric parameters of nanopillars. A strategy of grouping iteration is proposed to reduce the loss rate and computational effort in a holistic way. Its essence is to divide an extremely large-scale optimization space into many overlapping groups. Meanwhile, an improved genetic-simulated annealing (IGSA) algorithm is presented for the optimal solution of each group. By introducing the adaptive crossover and mutation probabilities in traditional genetic algorithms, the IGSA algorithm has both strong global searching capability and excellent local searching capability. After optimization, the maximum field intensity of the central hot spot can be increased by about 8% compared to the initial metalens. Moreover, the field intensity of the side lobes around the hot spot is almost constant, and the central hot spot increases, which provides a potential for the realization of high imaging contrast.

Optimization and Control for Separation of Ethyl Benzene from C8 Aromatic Hydrocarbons with Extractive Distillation

Extractive distillation has great significance for the separation of ethylbenzene from C8 aromatic hydrocarbons. Herein, a distillation process for the separation of ethylbenzene was designed using methyl phenylacetate as an extractant. A genetic algorithm (GA) was used to evaluate the economic and environmental factors of the process, and Aspen Dynamic was used to assess the dynamic performance. The sequential optimization method was used to obtain the initial process parameters. Then, the total annual cost and CO2 emissions were minimized by NSGA-III to increase the economic and environmental benefits. To enhance the search performance of GA, the mutation probability and crossover probability were studied and adjusted. The optimal total annual cost and CO2 emissions were 11.7% and 23.7% lower than those of the initial process. Based on a steady process, two control strategies, which were the flow rate of the recycling solvent controlled by entrainer makeup flow rate (CS1) and the bottom flow rate of the extractant recovery column (CS2), were designed. The results showed that the temperature deviation of CS2 was smaller than that of CS1, and the temperature of the process was more stable under the control of CS2.

Application of Artificial Intelligence in Academic Mental Health and Employment Evaluation

The enrollment expansion of colleges and universities and the acceleration of today's social process bring fierce competition to students. Colleges and universities should attach great importance to the mental health and employment anxiety of graduate students. In order to better explore the relationship between them, this paper uses artificial intelligence (AI) to evaluate students' psychology. The results show that: (1) When the crossover probability P value is less than 1, the psychology tends to be stable, and the emotion simulation conforms to the law of emotion change. (2) The accuracy of this model is higher than 82%, and the weighted fuzzy reasoning method can effectively analyze psychological symptoms. (3) After iteration, CNN has different recognition degrees for six emotions. (4) Finally, according to the emotional analysis given by the model, the source of students' psychological problems is discussed, and it is found that these students have different degrees of bad academic behavior; while they are anxious about employment, the employment rate is not satisfactory.

Fatigue Detection of Air Traffic Controllers Based on Radiotelephony Communications and Self-Adaption Quantum Genetic Algorithm Optimization Ensemble Learning

Air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. Speech-based fatigue-state detection is proposed in this paper. The speech signal is preprocessed to further extract the Mel frequency cepstrum coefficient (MFCC) from speech discourse. The machine learning method is used in fatigue detection. However, single machine learning fatigue detection methods often have low detection accuracy. To solve this problem, an ensemble learning method based on self-adaption quantum genetic algorithm (SQGA) heterogeneous learning methods is proposed. Pattern-level and feature-level resampling are used to increase the differences in the base learner’s training dataset. To enlarge the diversity of single learners, k-nearest neighbor (KNN), Bayesian network (BN), back propagation neural network (BPNN) and support vector machine (SVM) are adopted for the heterogeneous ensemble. On this basis, finally, the detection result is obtained by weighted summation. The weight of each base learner was determined by SQGA. The SQGA method combines the quantum genetic algorithm with the adaptive strategy. The adaptive strategy includes adaptive adjustment of the quantum rotation gate, adaptive generation of crossover probability and adaptive generation of mutation probability. The experiments on real civil aviation radio land–air communication show that the proposed method can obtain 98.5% detection accuracy, with a 1.2% false and 3.0% missing report rate, whereas the SVM only obtains 94.0% detection accuracy, with a 5.4% false and 9.0% missing report rate.

Comparison of multi-objective evolutionary algorithms applied to watershed management problem

Simulation-based optimization (S–O) frameworks are effective in developing cost-effective watershed management strategies, where optimization algorithms have substantial effect on the quality of strategies. Despite the development and improvement of multi-objective evolutionary algorithms (MOEAs) provide more robust alternatives for optimization, they typically have limited applications in real-world decision contexts. In this study, three advanced MOEAs, including NSGA-II, MOEA/D and NSGA-III, were introduced into the S–O framework and applied to a real-world watershed management problem, and their performance and characteristics were quantified through performance metrics. Results show that a higher crossover or mutation probability do not necessarily promote convergence and diversity of solutions, while a larger generation and population size is helpful for MOEAs to find high-quality solutions. Compared to the other two MOEAs, NSGA-II consistently exhibits robust performance in finding solutions with good convergence and high diversity, and provides more options at the same computational cost, while the degenerate Pareto front of the proposed watershed management problem may account for the poor performance of MOEA/D and NSGA-III in terms of diversity. For a 10% TN or TP reduction target, the average cost of the NSGA-II optimized strategies is 32.22% or 47.83% of the commonly used strategies. In addition, this study also discussed the development of resilient watershed management to buffer the impacts of climate change on aquatic system, the incorporation of fuzzy programming into the S–O framework to develop robust watershed management strategies under uncertainty, and the application of machine learning-based surrogate models to reduce computational cost of the S–O framework. These results can contribute to the understanding of MOEAs and provide useful guidance to decision makers.

Decision fusion for multi-route and multi-hop Wireless Sensor Networks over the Binary Symmetric Channel

The decision fusion for multi-route and multi-hop Wireless Sensor Networks (WSNs) is studied, wherein a discrete memoryless channel model, i.e., the Binary Symmetric Channel (BSC), is considered to characterize the relay transmission of each hop from the local sensor to the fusion center. In particular, we first develop the optimal log-likelihood ratio (LLR) based decision fusion rule, wherein the fusion center is assumed to have perfect knowledge of both the local sensor performance indices and the Channel State Information (CSI), i.e., crossover probability for each BSC. Secondly, we derive the suboptimum and robust fusion rules for two cases. In the first case, channel condition from the source to the local sensor is considered to be ideal. In the second case, the crossover probability for each BSC is assumed to be relatively large or small. Our result show that our suboptimum fusion detectors require less or no a priori information about crossover probability and/or the local sensor performance indices, and thus are easy to implement. We also show that the simple decision fusion statistic, i.e., the counting-based statistic, can be directly derived from the optimal LLR-based statistic for both cases. These suboptimum fusion rules are clearly desired for applications, wherein perfect estimation of the local sensor performance and CSI is complexity-intensive or unachievable. Furthermore, the optimal LLR-based scheme for joint decision fusion and CSI estimation is proposed. We uniformly quantize the equivalent crossover probability into discrete status, and thus give a suboptimum but more computationally practical scheme. The performance evaluation is finally developed both analytically and through simulation.

CONSTRUÇÃO DE MODELO MATEMÁTICO BASEADO NO ALGORITMO GENÉTICO PARA OTIMIZAÇÃO DE UM PROGRAMA DE APTIDÃO EM ESTUDANTES DO ENSINO MEDIO

ABSTRACT Introduction: The genetic algorithm is one of the essential theoretical mathematical models for simulating biological development. It is widely used in many fields such as engineering, medicine, and economics. Objective: Use the genetic algorithm as a mathematical model basis for optimization in the high school students’ aptitude program. Methods: The selection method by competition is adopted to elect the random crossover of male crossover probability with high similarity to generate a new population. A genetic algorithm was proposed to adjust the crossover probability and dynamic mutation according to fitness, aiming to solve the problem of dynamic changes. A comparative analysis is performed between the nonlinear differential equations and the Levenberg–Marquardt method algorithm. Results: The algorithm improvement was obtained after analyzing the operation process and structuring of the traditional genetic algorithm; the mathematical model application revealed improvement in the motion accuracy model established by the genetic algorithm. Conclusion: The physical enhancement optimization scheme was tested and verified by a genetic algorithm and proves the research results hold theoretical feasibility. Evidence Level II; Therapeutic Studies – Investigating the results.

Research on the parameter identification of PV module based on fuzzy adaptive differential evolution algorithm

The rapid and accurate acquisition of model parameters of photovoltaic (PV) modules is of great significance for the efficient operation and maintenance of photovoltaic power plants under the background of the development of new power systems. To solve the problems of poor accuracy and slow velocity of identification of traditional PV modules model parameters, this paper proposed an identification of parameter method based on fuzzy adaptive differential evolution algorithm (FADE). In the proposed method, based on the I–V output characteristics of PV modules, a DE/current-to-SP-best/1 variation strategy is constructed to increase the local search capability of module model parameter identification; In addition, fuzzy selection strategy and an adaptive parameter adjustment strategy are introduced to effectively control the crossover probability and mutation factors to avoid the discrimination into local optimum while improving the convergence of the algorithm. The performance of the proposed method has been verified by extracting classical polycrystalline and monocrystalline modules parameters, The solution results of the polycrystalline module Photowatt-PWP201 (2.42507E−3), STP6-120/36 (1.66006E−2) and monocrystalline module STM6-40/36 (1.72981E−3) comprehensively show that FADE has better accuracy and robustness compared with other algorithms.

A Study of Intelligent Paper Grouping Model for Adult Higher Education Based on Random Matrix

This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results.

Product evolution analysis for design adaptation using big sales data

Products are constantly evolving through design adaptations to satisfy market requirements. Efficient design adaptation via maximal reusing of existing design can improve product quality, reduce manufacturing cost, and time to market. Understanding of product evolution mechanism is required for design adaptations through rationalized modularity and interfaces. Emerging big sales data provide rich resources for product evolution analysis. To support efficient design adaptations, a framework and associated methods are developed in this work using big sales data for product evolution analysis. Reproduction, mutation, and crossover of product specifications are introduced as specification adaptation operations. Methods are proposed for identification of specification adaptations and estimation of the adaptation probability. Based on modeling and analyzing relationships among product specifications and components, different types of modules and interfaces are proposed through components clustering and their potential operation (mutation, reproduction, and crossover) probabilities. Design recommendations of adaptable product architecture with modules and interfaces are made for facilitating design adaptations. A case study of consumer Unmanned Aerial Vehicles (UVAs) illustrates the proposed method. Limitations and potential extensions of the newly developed method are discussed. Further investigations of the product evolution mechanism using game theory to support competitive product development are included.

A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

An optimized solution to the course scheduling problem in universities under an improved genetic algorithm

Abstract The increase in the size of universities has greatly increased the number of teachers, students, and courses and has also increased the difficulty of scheduling courses. This study used coevolution to improve the genetic algorithm and applied it to solve the course scheduling problem in universities. Finally, simulation experiments were conducted on the traditional and improved genetic algorithms in MATLAB software. The results showed that the improved genetic algorithm converged faster and produced better solutions than the traditional genetic algorithm under the same crossover and mutation probability. As the mutation probability in the algorithm increased, the fitness values of both genetic algorithms gradually decreased, and the computation time increased. With the increase in crossover probability in the algorithm, the fitness value of the two genetic algorithms increased first and then decreased, and the computational time decreased first and then increased.

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.

Tourism Route Selection Model for Tourism Sustainable Development Based on Improved Genetic Algorithm

The improvement of people’s quality of life also promotes the development of tourism. The traditional travel mode is no longer suitable for the needs of modern people. How to quickly determine the optimal tourist route according to the needs of different families is the current sustainable development direction of tourism. Scientific planning of tourist routes to minimize the cost and time of tourists is very important for the improvement of tourism experience. This study employs an improved genetic algorithm (IGA) to find the best tourist route based on this requirement. With the increase of tourist attractions, routes, and demands, the traditional genetic algorithm (GA) has problems such as premature convergence and poor local search ability when planning the tourist route. The existence of these problems will affect the effect of route planning. IGA proposes three improvements to traditional GA. One is to introduce the ant colony algorithm (ACA) to initialize the parameters in the GA. This algorithm is introduced to alleviate the over-reliance of GA on initializing the population and the poor adaptability of individual populations. Second, because the crossover probability parameter in GA has such a large influence on the final solution, this study proposes an adaptive strategy for adjusting the crossover probability to improve population fitness. Third, considering the weak local search ability of GA and the problem of premature convergence, this study introduces the 2-opt optimization algorithm to improve the quality of the solution. The results of the experimental analysis confirm the effectiveness of the proposed method.

A Variant Genetic Algorithm for a Specific Examination Timetabling Problem in a Japanese University

Background: Examination Timetabling Problem which tries to find an optimal examination schedule for schools, colleges, and universities, is a well-known NP-hard problem. This paper presents a Genetic Algorithm variant approach to solve a specific examination timetabling problem common in Japanese colleges and universities. Methods: The proposed algorithm uses direct chromosome representation Genetic Algorithm and implements constraint-based initialization and constraint-based crossover operations to satisfy the hard and soft constraints. An Island model with varying crossover and mutation probabilities and an improvement approach called pre-training are applied to the algorithm to further improve the result quality. Results: The proposed model is tested on synthetic as well as real datasets obtained from Sophia University, Japan and shows acceptable results. The algorithm was fine-tuned with different penalty points combinations and improvement combinations. Conclusion: The comparison results support the idea that the initial population pre-training and the island model are effective approaches to improve the result quality of the proposed model. Although the current island model used only four islands, incorporating greater number of islands, and some other diversity maintenance approaches such as memetic structures are expected to further improve the diversity and the result quality of the proposed algorithm on large scale problems.

Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics

To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers.

The DNA Storage Channel: Capacity and Error Probability Bounds

The DNA storage channel is considered, in which the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> Deoxyribonucleic acid (DNA) molecules comprising each codeword are stored without order, sampled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> times with replacement, and then sequenced over a discrete memoryless channel. For a constant coverage depth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M/N$ </tex-math></inline-formula> and molecule length scaling <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta (\log M)$ </tex-math></inline-formula> , lower (achievability) and upper (converse) bounds on the capacity of the channel, as well as a lower (achievability) bound on the reliability function of the channel are provided. Both the lower and upper bounds on the capacity generalize a bound which was previously known to hold only for the binary symmetric sequencing channel, and only under certain restrictions on the molecule length scaling and the crossover probability parameters. When specified to binary symmetric sequencing channel, these restrictions are completely removed for the lower bound and are significantly relaxed for the upper bound in the high-noise regime. The lower bound on the reliability function is achieved under a universal decoder, and reveals that the dominant error event is that of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">outage</i> – the event in which the capacity of the channel induced by the DNA molecule sampling operation does not support the target rate.

Application of LSTM model optimized by individual-ordering-based adaptive genetic algorithm in stock forecasting

PurposeTo improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).Design/methodology/approachIn this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.FindingsThe simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.Originality/value(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.