RETRACTED ARTICLE: Multimedia music education based on adaptive genetic algorithm and heterogeneous processors

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

In this paper, a hybrid model (BP-GA) including a back propagation network and a genetic algorithm is used to estimate the thermal conductivity of the nanofluids. The genetic algorithm is used to optimize the initial weight and threshold of BP neural network, so that the optimized BP neural network can better predict the function output. In this study, CuO-ZnO hybrid nanofluids with the mass fraction of 0%, 1%, 2%, and 3% were studied at temperatures of 25, 30, 35, 40, 45, 50, 55, and 60 °C, respectively. The thermal conductivity of the hybrid nanofluids base liquid is 20:80, 40:60, 50:50, 60:40, 80:20, and the mass ratio of CuO-ZnO is 50:50. The mass fraction of CuO-ZnO, the temperature and the mixing ratio of different base liquids were used as input parameters, and the thermal conductivity was the output parameter, forming a 3-input ANN neural network. The predicted thermal conductivity results of BP neural network and genetic algorithm optimized BP neural network (GA-BP) were compared with experimental data. The results illustrate that the BP neural network optimized by genetic algorithm exerts a positive influence on the accuracy and stability of the predicted output.

On the relationship between changes in stock prices and bond yields in the G7 countries: Wavelet analysis

A company's financial statements provide information that is useful in making economic decisions. This may include information about the company's financial performance, financial position and cash flows. Valuation of financial instruments at fair value can have a significant impact on bank share prices. Banks that use fair value accounting for financial instruments may experience greater variability in their share prices than banks that do not use this method. The purpose of this research is to investigate the relationship between accounting valuation of financial instruments at fair value and changes in bank stock prices. The research aims to determine whether and to what extent there is a relationship between the fair value of financial instruments and changes in stock prices. Research may also aim to identify any factors that may influence this relationship and provide information and recommendations for stakeholders such as investors, regulators and financial institutions. Based on the results achieved, the need for optimal and accurate use of fair value indicators due to their impact on the company's performance, as well as clearer compliance by banks with international financial reporting standards regarding the fair value assessment of financial instruments, was investigated. And thus, it can lead to increased accuracy of forecasts and increased confidence of users of financial statements in making economic decisions. The findings of the study can contribute to the existing scientific literature on the impact of fair value accounting on stock prices and provide further understanding of this issue. The results of the research can be used by banks to assess the potential impact of the implementation of fair value accounting for financial instruments on the share price.

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Optimization model is build for solving the aircraft departure sequencing problem in this paper first. Then, an improved genetic algorithm (GA) using symbolic coding is proposed, where a type of total probability crossover and big probability mutation are performed. In this way, the evolutionary policy of Particle Swarm Optimization (PSO) is absorbed into the improved GA, which reduces the complexity and enhance the efficiency greatly. Last, a simulation program using basic GA, adaptive GA, and improved GA is performed. The simulation result shows that the model is effective and Improved GA has better performance than Basic GA or Adaptive GA.

In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions. High-fitness solutions are 'protected', while solutions with subaverage fitnesses are totally disrupted. By using adaptively varying p/sub c/ and p/sub ,/ we also provide a solution to the problem of deciding the optimal values of p/sub c/ and p/sub m/, i.e., p/sub c/ and p/sub m/ need not be specified at all. The AGA is compared with previous approaches for adapting operator probabilities in genetic algorithms. The Schema theorem is derived for the AGA, and the working of the AGA is analyzed. We compare the performance of the AGA with that of the standard GA (SGA) in optimizing several nontrivial multimodal functions with varying degrees of complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Uncertainties in the production process would inevitably result in deviations from the existing schedule in flexible manufacturing systems (FMSs). This paper tries to study the problem in an environment with realistic interruptions and a requirement of time-restricted response in rescheduling. In our paper, the rescheduling system is based on the records of a dynamic database (DDB). It is able to reform the up-to-date status of a disturbed system via summarizing the remaining resources and works in process (WIPs) precisely. By using it as the new initial state, the new schedule is configured smoothly in conjunction with the existing schedule to improve the efficiency of FMS at this critical instant. Considering both speed and economic benefit, an adaptive genetic algorithm (AGA) is proposed for finding the new sub-optimal schedule of a large and complicated job shop FMS shortly after the interruption occurred. The AGA is designed to prevent the premature convergence and refine the performance of genetic algorithms in re-scheduling. During the AGA evolution process, the probabilities of crossover and mutation are varied, depending on both the fitness value and the normalized fitness distances between solutions. With help from DDB, the FMS scheduling model and AGA, the results obtained in the test examples of rescheduling are quite satisfactory.

Genetic algorithm (GA) is widely applied to optimal path planning of mobile robots. In this work, an adaptive genetic algorithm (AGA) is proposed, which is expected to solve some difficulties that conventional GA inevitably faces, including local optimum in the early stage, too lower convergence speed, and large complicated computation process. Sine-AGA denotes that the cross probability and mutation probability could realize the adaptive adjustments by conforming to a set of sine functions, guaranteeing to achieve a preservation scheme for optimal individuals. The whole iterative process consists of path coding, choices of fitness function, design of reproduction, crossover and mutation operations, and the setting of initial parameters of AGA. Simulation under the same condition indicates that the convergence performances of average solution and optimal solution are highly enhanced, better than the ones obtained through the pure AGA scheme, and the convergence speed is proved to be increased as expected.

Inversion of groundwater contamination source based on a two-stage adaptive surrogate model-assisted trust region genetic algorithm framework

A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX

As a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of constraints deriving from the capability of available platforms and munitions (or weapons), target characteristics, timings for operation and so on. In order to overcome the drawbacks of easily falling into premature convergence and local optimum of existing heuristic algorithms when they are employed to optimize WTA problems, this paper proposes a hybrid genetic algorithm (HGA) which combines an adaptive genetic algorithm (AGA) with an adaptive variable neighborhood search algorithm (AVNSA) to balance the exploration and exploitation ability. In the framework of HGA, AGA is used for wide scope search in the solution space to avoid premature convergence while AVNSA for local search to jump out of the local optimal space. The simulation result demonstrates the effectiveness and feasibility of the proposed HGA in small-scale WTA optimization problems and through a comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm in large-scale WTA optimization problems are revealed.

This paper considers variance bounds for stock price changes in a general setting that allows for ex-dividend stock prices, risk averse investors, and exponentially-growing dividends. I show that providing investors with more information about future dividends can either increase or decrease the variance of stock price changes, depending on some key parameters, namely, those governing the properties of dividends and the stochastic discount factor. This finding contrasts with the results of Engel (2005) who shows that news about future dividends will always decrease the variance of stock price changes in a specialized setting with cum-dividend stock prices and risk neutral investors.

Financial Performance of Pakistani Banks after the Implementation of Reforms: A Post-Global-Crisis Study