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Zeolite structure determination is an interesting challenge even with the progress in terms of structural resolution from X-rays and electron diffraction. The infinite number of potential solutions and the computational cost of this problem make the use of an evolutionary algorithm significant for this challenge. In this paper, we propose a new parallel and distributed hybrid genetic algorithm called...
In our research, we consider the measuring of machine intelligence based on the intelligence (ability to solve various tasks in high efficiency, with a grade of flexibility and robustness) in solving difficult problems/tasks (NP-hard and/or have different types of uncertainties). An intelligent cooperative coalition of agents (could be a whole cooperative multiagent system or a part of it) have variability...
The transcription factor NF-κB is a biological component that is central to the regulation of genes involved in the innate immune system. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects...
Mass Spectrometry (Surface Enhanced Laser Desorption Time of Flight (SELDI-TOF) assay technique) for proteomics is based on the consistency and reproducibility of protein/peptide expressions. In this study, we opine that mining collections of mass spectra data instead of detailed study of individual ions generated in the course of Mass Spectrometer assay process, will generate discriminative factors...
Multimodal optimization has attracted increasing interest recently. Despite the emergence of various multimodal optimization algorithms during the last decade, little work has been dedicated to the development of benchmark tools. In this paper, we propose a visualization method for benchmark studies of multimodal optimization, called parallel peaks. Inspired by parallel coordinates, the proposed parallel...
In this paper, we study a simple hyper-heuristic that functions by sampling solution chains. A solution chain in this algorithm is formed by successively applying a randomly chosen heuristic to the previous solution to generate the next solution. Operating in this way, the algorithm can benefit from the accumulated effect of applying multiple heuristics. A key factor in this algorithm is the strategy...
This paper investigates the evolution of two types of simple Genotype Phenotype Mappings (GPMs): a many-to-one mapping and a one-to-many mapping. Both GPMs are under genetic control. For both types of mappings different Regions Of Maximum Adaptability (ROMAs) are found. These ROMAs are the regions - in a paramterized space of GPMs - evolution leads to. The attraction towards these ROMAs increases...
Feature selection is an important task in machine learning, which aims to reduce the dataset dimensionality while at least maintaining the classification performance. Particle Swarm Optimisation (PSO) has been widely applied to feature selection because of its effectiveness and efficiency. However, since feature selection is a challenging task with a complex search space, PSO easily gets stuck at...
Accurate prediction of the traffic state can help to solve the problem of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm, which is based on Multi-kernel Support Vector Machine (MSVM) and Adaptive Particle Swarm Optimization (APSO). Firstly, we explore both the nonlinear and...
Estimation of distribution algorithms (EDAs) are a special class of model-based evolutionary algorithms (EAs). To improve the performance of traditional EDAs, many remedies were suggested, which mainly focused on estimating a suitable probability distribution model with superior solutions. Different from existing research ideas, this paper tries to enhance EDA by exploiting the potential value of...
In the Evolutionary Computation field, it is frequent to assume that a computation load necessary for fitness value computation is, at least, similar for all possible cases. The main objective of this paper is to show that the above assumption is frequently false. Therefore, the examples of evolutionary methods that use problem encoding which allows for significant optimization of the fitness computation...
Single objective evolutionary constrained optimization has been widely researched by plethora of researchers in the last two decades whereas multi-objective constraint handling using evolutionary algorithms has not been actively proposed. However, real-world multi-objective optimization problems consist of one or many non-linear and non-convex constraints. In the present work, we develop an evolutionary...
Participatory sensing is a rising paradigm which utilizes mobile phones to collect data and build application on the cloud. But there are many problems to be resolved, poor quality of received information caused by task executors has been one of them. So incentive mechanism is essential for attracting users to participate in and submit high-quality data. Inspired by contract theory, we model participatory...
In this paper, two multi-objective clustering ensemble algorithms are proposed named MOCLED and MOCNCD. MOCLED is different from MOCLE on three points. First, different clustering algorithms are used to produce some new individuals in evolutionary process. Second, a new screening mechanism is added. In each generation, the worst individual is replaced by the best individual. Third, a new objective...
Solving dynamic multi-objective optimisation problem means to search adaptively for the Pareto optimal solutions when the environment changes. It is important to find out the changing pattern for the efficiency of the evolutionary search. Learning techniques are thus widely used to explore the dependence structure of the changing for population re-initialisation in the evolutionary search paradigm...
Particle swarm optimization (PSO) is a stochastic search algorithm based on the social dynamics of a flock of birds. The performance of the PSO algorithm is known to be sensitive to the values assigned to its control parameters. While many studies have provided reasonable ranges in which to initialize the parameters based on their long-term behaviours, such previous studies fail to quantify the empirical...
In many-objective optimization, visualization of population in the high-dimensional objective space provides a critical understanding of the Pareto front. First, visualization throughout the evolutionary process can be exploited in developing effective many-objective evolutionary algorithms. Furthermore, visualization is a crucial component of multi-criteria decision making. By directly observing...
Dynamic Job shop scheduling (DJSS) is a complex and hard problem in real-world manufacturing systems. In practice, the parameters of a job shop like processing times, due dates, etc. are uncertain. But most of the current research on scheduling consider only deterministic scenarios. In a typical dynamic job shop, once the information about a job becomes available it is considered unchanged. In this...
Many Differential Evolution algorithms are introduced in the literature to solve optimization problems with diverse set of characteristics. In this paper, we propose an extension of the previously published paper LSHADE-EpSin that was ranked as the joint winner in the real-parameter single objective optimization competition, CEC 2016. The contribution of this work constitutes two major modifications...
Solving real-world optimization problems is considered a challenging task. This is due to the variability of the characteristics in objective functions, the presence of enormous number of local optima within the search space and highly nonlinear constraints with large number of variables. The advances on this type of problems are of capital importance for many researchers to develop new efficient...
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