In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Another comparison has been conducted to show that the I-CGA-SDA has fewer function evaluations, minimum search space percentage, faster convergence speed and has a higher optimal precision than that of the HGA.
Quantum channels enable the achievement of communication tasks inaccessible to their
classical counterparts. The most famous example is the distribution of secret keys. Unfortunately, the rate
of generation of the secret key by direct transmission is fundamentally limited by the distance. This limit
can be overcome by the implementation of a quantum repeater. In order to boost the performance of the
repeater, a quantum repeater based on cut-off with two different types of quantum memories is suggestd,
which reduces the effect of decoherence during the storage of a quantum state.
Most of the Internet of Things (IoT), cell phones, and Radio Frequency Identification (RFID) applications need high speed in the execution and processing of data. this is done by reducing, system energy consumption, latency, throughput, and processing time. Thus, it will affect against security of such devices and may be attacked by malicious programs. Lightweight cryptographic algorithms are one of the most ideal methods Securing these IoT applications. Cryptography obfuscates and removes the ability to capture all key information patterns ensures that all data transfers occur Safe, accurate, verified, legal and undeniable. Fortunately, various lightweight encryption algorithms could be used to increase defense against various at
... Show MoreThe study using Nonparametric methods for roubust to estimate a location and scatter it is depending minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .
It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu
... Show MorePurpose: This research is to identify the most important challenges for the local investment commissions and to develop solutions and proposals to encourage local and foreign investment in local governments in Iraq (the Iraqi provinces are irregular in the region). Theoretical Framework: This research suggests a conceptual framework for the local investment commissions in order to solve their problems, the most important of which was to identify the most critical challenges which are facing the Baghdad Investment Commission BIC and how to overcome them. Design/The methodology approach: Research involved a mixed-methods approach through two stages. During the first stage, the researcher gathered quantitative data from all inves
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The research aimed to test the relationship between the size of investment allocations in the agricultural sector in Iraq and their determinants using the Ordinary Least Squares (OLS) method compared to the Error Correction Model (ECM) approach. The time series data for the period from 1990 to 2021 was utilized. The analysis showed that the estimates obtained using the ECM were more accurate and significant than those obtained using the OLS method. Johansen's test indicated the presence of a long-term equilibrium relationship between the size of investment allocations and their determinants. The results of th
... Show MoreGenetic algorithms (GA) are a helpful instrument for planning and controlling the activities of a project. It is based on the technique of survival of the fittest and natural selection. GA has been used in different sectors of construction and building however that is rarely documented. This research aimed to examine the utilisation of genetic algorithms in construction project management. For this purpose, the research focused on the benefits and challenges of genetic algorithms, and the extent to which genetic algorithms is utilised in construction project management. Results showed that GA provides an ability of generating near optimal solutions which can be adopted to reduce complexity in project management and resolve difficult problem
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