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Recently, Radon transformation has been used to generate barcodes for tagging medical images. The under-sampled image is projected in certain directions, and each projection is binarized using a local threshold. The concatenation of the thresholded projections creates a barcode that can be used for tagging or annotating medical images. A small number of equidistant projections, e.g., 4 or 8, is generally...
The study of proteins and the prediction of their three-dimensional (3-D) structure is one of the most challenging problems in Structural Bioinformatics. Over the last years, several computational strategies have been proposed as a solution to this problem. As revealed by recent CASP experiments, the best results have been achieved by knowledge-based methods. Despite the advances in the development...
Breast cancer is known as the second largest cause of cancer deaths among women, but thankfully it can be cured if diagnosed early. There have been many investigations on methods to improve the accuracy of the diagnostic, and Machine Learning (ML) and Evolutionary Computation (EC) tools are among the most successfully employed modern methods. On the other hand, Logistic Regression (LR), a traditional...
Disease-gene association attempts to determine which genes are involved with genetic diseases. Various methodologies have been applied to this problem for different diseases. In earlier work, two evolutionary approaches were used to analyze the complex network of gene interaction. This paper presents an improvement upon the genetic programming approach using a variety of centrality measures to analyze...
To understand the emergent behavior of biochemical systems, computational analyses generally require the inference of unknown reaction kinetic constants, a problem known as parameter estimation (PE). In this work we propose a PE methodology that exploits Particle Swarm Optimization (PSO) to examine a set of candidate kinetic parameterizations, whose fitness is evaluated by comparing given target time-series...
A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This...
Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on parameter control is JADE. In JADE, the two parameter values are generated according to two probability...
In the modern manufacturing and operations management, on-time delivery is a critical factor towards realizing customer satisfaction. This paper focuses on job-shop scheduling problem to minimize total weighted tardiness and proposes a discrete differential evolution algorithm for this problem. In order to improve the search ability and efficiency, this paper hybrids the local search which is based...
Differential evolution (DE) is a high performance and easy to implement evolutionary algorithm. The DE algorithm with small population size (i.e., micro-DE) can further increase the efficiency of the algorithm. However, it also decreases its exploration capability, causing stagnation and pre-mature convergence. In this paper, the idea of exploration enhancement at the mutation level is proposed. The...
Opposition-based learning (OBL) is a recently proposed method, which is successfully used to accelerate the search process of some well-known techniques in soft computing, such as swarm and evolutionary algorithms, artificial neural networks, reinforcement learning, and fuzzy logic systems. Among these opposition-based algorithms, opposition-based differential evolution (ODE) is one of the most popular...
Differential Evolution (DE), a population-based stochastic search technique is adept at solving real-world optimization problems. Unlike most population based algorithms, the use of DE is usually inexpedient in solving expensive optimization problems as the computational costs of these simulations are excessively high. This problem can be resolved by commingling surrogate model in DE that approximates...
In this paper we present a new method for content-based searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system...
This paper proposes a new evolutionary algorithm (EA), which is called the natural aggregation algorithm (NAA). NAA is inspired by the collective decision making intelligence of the group-living animals. Distinguished from other EAs, NAA distributes individuals to several sub-populations (called ‘shelters’), and uses a stochastic migration model to dynamically mitigate the individuals among the shelters...
Due to the gradient-free mechanism, flexibility, high local optima avoidance, and simplicity, meta-heuristics have been reliable alternatives to conventional optimisation techniques over the course of last two decades. This has resulted in the application of such techniques in diverse branches of science and technology. Despite all the successful applications, meta-heuristics are less effective in...
The intercell scheduling problems arise due to the intercell transfers in cellular manufacturing systems. In this paper, the intercell scheduling problem with limited transportation capacity, which is essentially the coordination of part scheduling and intercell transportation, is addressed. Because it is a practical decision-making problem of high complexity and large problem size, an artificial...
With the explosively increase of information and products, recommender systems have played a more and more important role in the recent years. Various recommendation algorithms, such as content-based methods and collaborative filtering methods, have been proposed. There are a number of performance metrics for evaluating recommender systems, and considering only the precision or diversity might be...
There is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of parameters to be configured. Fuzzy logic has been used extensively in control applications and is known for its ability to handle uncertainty. In this study, we investigate the...
Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metaheuristics for solving combinatorial optimisation problems. A common issue with the application of a memetic algorithm is determining the best initial setting for the algorithmic parameters, but these can greatly influence its overall performance. Unlike traditional studies where parameters are tuned...
In recent years, a trend towards alternative random or more precisely pseudorandom number generators could have been observed, viz. non-uniform generators that are based on chaotic maps (e.g. Ikeda Map) and uniform generators that are based on linear recurrence (e.g. Xorshift). Especially, chaotic maps have shown their superiority over canonical pseudorandom number generators in different heuristics...
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