On the landscape of combinatorial optimization problems

This paper carries out a comparison of the fitness landscape for four classic optimization problems: Max-Sat, graph-coloring, traveling salesman, and quadratic assignment. We have focused on two types of properties, local average properties of the landscape, and properties of the local optima. For the local optima we give a fairly comprehensive description of the properties, including the expected time to reach a local optimum, the number of local optima at different cost levels, the distance between optima, and the expected probability of reaching the optima. Principle component analysis is used to understand the correlations between the local optima. Most of the properties that we examine have not been studied previously, particularly those concerned with properties of the local optima. We compare and contrast the behavior of the four different problems. Although the problems are very different at the low level, many of the long-range properties exhibit a remarkable degree of similarity

Nonlinear regression in tax evasion with uncertainty: a variational approach

One of the major problems in today's economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate

Design, synthesis and biological evaluation of novel coumarin-based benzamides as potent histone deacetylase inhibitors and anticancer agents

Histone deacetylases (HDACs) are attractive therapeutic targets for the treatment of cancer and other diseases. It has four classes (I-IV), among them especially class I isozyme are involved in promoting tumor cells proliferation, angiogenesis, differentiation, invasion and metastasis and also viable targets for cancer therapeutics. A novel series of coumarin-based benzamides was designed and synthesized as HDAC inhibitors. The cytotoxic activity of the synthesized compounds (8a-u) was evaluated against six human cancer cell lines including HCT116, A2780, MCF7, PC3, HL60 and A549 and a single normal cell line (Huvec). We evaluated their inhibitory activities against pan HDAC and HDAC1 isoform. Four compounds (8f, 8q, 8r and 8u) showed significant cytotoxicity with IC50 in the range of 0.53–57.59 μM on cancer cells and potent pan-HDAC inhibitory activity (consists of HDAC isoenzymes) (IC50 = 0.80–14.81 μM) and HDAC1 inhibitory activity (IC50 = 0.47–0.87 μM and also, had no effect on Huvec (human normal cell line) viability (IC50 > 100 μM). Among them, 8u displayed a higher potency for HDAC1 inhibition with IC50 value of 0.47 ± 0.02 μM near equal to the reference drug Entinostat (IC50 = 0.41 ± 0.06 μM). Molecular docking studies and Molecular dynamics simulation of compound 8a displayed possible mode of interaction between this compound and HDAC1enzym

A Novel Magnetic Update Operator for Quantum Evolutionary Algorithms

Quantum Evolutionary Algorithms (QEA) are novel algorithms proposed for class of combinatorial optimization problems. The probabilistic representation of possible solutions in QEA helps the q-individuals to represent all the search space simultaneously. In QEA, Q-Gate plays the role of update operator and moves qindividuals toward better parts of search space to represent better possible solutions with higher probability. This paper proposes an alternative magnetic update operator for QEA. In the proposed update operator the q-individuals are some magnetic particles attracting each other. The force two particles apply to each other depends on their fitness and their distance. The population has a cellular structure and each q-individual has four neighbors. Each q-individual is attracted by its four binary solution neighbors. The proposed algorithm is tested on Knapsack Problems, Trap problem and fourteen numerical function optimization problems. Experimental results show better performance for the proposed update operator than Q-Gate

An evolutionary ensemble learning for diagnosing COVID-19 via cough signals

Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose the COVID-19 disease via cough signals.Methods The proposed algorithm was an ensemble scheme that consists of a number of base learners, where each base learner used a different feature extractor method, including statistical approaches and convolutional neural networks (CNNs) for automatic feature extraction. Features were extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners were aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposed a memetic algorithm for training the CNNs in the base-learners, which combined the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms.Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion COVID-19 may be diagnosed via cough signals and CNNs may be employed to process these signals and it may be further improved by the optimization of CNN architecture

A Sinusoid Size Ring Structure Quantum Evolutionary Algorithm

This paper proposes a dynamic ring architecture of interaction among members of population in a Quantum Evolutionary Algorithms (QEA). The ring is allowed to expand/collapse based on a sinusoidal population size and partial re-initialization of new members in the population. The study shows that the ring structure can be an efficient architecture for an effective Exploration/Exploitation tradeoff, and the partial re-initialization of the proposed algorithm can improve the diversity of the algorithm. The proposed approach is tested on Knapsack Problem, Trap Problem as well as 14 numerical optimization functions. Experimental results show that the proposed Structure consistently improves the performance of QEA