Room temperature Fourier transform infrared spectra of the four-membered heterocycle trimethylene sulfide were collected with a resolution of 0.00096 cm(-1) using synchrotron radiation from the Canadian Light Source f...
详细信息
Room temperature Fourier transform infrared spectra of the four-membered heterocycle trimethylene sulfide were collected with a resolution of 0.00096 cm(-1) using synchrotron radiation from the Canadian Light Source from 500 to 560 cm(-1). The in-plane ring deformation mode (nu(13)) at similar to 529 cm(-1) exhibits dense rotational structure due to the presence of ring inversion tunneling and leads to a doubling of all transitions. Preliminary analysis of the experimental spectrum was pursued via traditional methods involving assignment of quantum numbers to individual transitions in order to conduct least squares fitting to determine the spectroscopic parameters. Following this approach, the assignment of 2358 transitions led to the experimental determination of an effective Hamiltonian. This model describes transitions in the P and R branches to J' = 60 and K-a' = 10 that connect the tunneling split ground and vibrationally excited states of the nu(13) band although a small number of low intensity features remained unassigned. The use of evolutionary algorithms (EA) for automated assignment was explored in tandem and yielded a set of spectroscopic constants that re-create this complex experimental spectrum to a similar degree. The EA routine was also applied to the previously well-understood ring puckering vibration of another four-membered ring, azetidine (Zaporozan et al., 2010). This test provided further evidence of the robust nature of the EA method when applied to spectra for which the underlying physics is well understood. (C) 2015 Elsevier Inc. All rights reserved.
In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm...
详细信息
In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm optimization and the principles of an evolutionary process. We then propose a hybrid fuzzy rough with K-nearest neighbor (K-NN)-based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bio-inspired algorithm search. While exploring other possible hybrid evolutionary processes, we then conducted experiments considering (i) same feature selection algorithm with support vector machine (SVM) and random forest (RF) classifier;(ii) instance based selection using synthetic minority over-sampling technique with fuzzy rough K-nearest neighbor (K-NN), SVM and RF classifier. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.
Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by ...
详细信息
Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by employing a population-based approach in mutual communication and information sharing and processing, including randomness. In this paper, history of swarm and evolutionary algorithms are discussed in general as well as their dynamics, structure and behavior. The core of this paper is an overview of an alternative way how dynamics of arbitrary swarm and evolutionary "algorithms can be visualized, analyzed and controlled. Also selected representative applications are discussed at the end. Both subtopics are based on interdisciplinary intersection of two interesting research areas: swarm and evolutionary algorithms and complex dynamics of nonlinear systems that usually exhibit very complex behavior. (C) 2015 Elsevier B.V. All rights reserved.
Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existi...
详细信息
Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existing individuals in order to generate new potentially better fit offspring leading to more promising areas of the search space. In this paper, we describe and analyze the best-order recombination operator for permutation-based encoding. The proposed operator uses genetic information from the two parents and from the best individual obtained up to the current generation. These sources of information are integrated to determine the best order of values in the new permutation. In order to evaluate the performance of best-order crossover, we address three well-known -hard optimization problems i.e. Travelling Salesman Problem, Vehicle Routing Problem and Resource-Constrained Project Scheduling Problem. For each of these problems, a set of benchmark instances is considered in a comparative analysis of the proposed operator with eight other crossover schemes designed for permutation representation. All crossover operators are integrated in the same standard evolutionary framework and using the same parameter setting to allow a comparison focused on the recombination process. Numerical results emphasize a good performance of the proposed crossover scheme which is able to lead to overall better quality solutions.
The ubiquitous presence of proteins in chemical pathways in the cell and their key role in many human disorders motivates a growing body of protein modeling studies to unravel the relationship between protein structur...
详细信息
The ubiquitous presence of proteins in chemical pathways in the cell and their key role in many human disorders motivates a growing body of protein modeling studies to unravel the relationship between protein structure and function. The foundation of such studies is the realization that knowledge of the structures a protein accesses under physiological conditions is key to a detailed understanding of its biological function and the design of therapeutic compounds for the purpose of altering misfunction in aberrant variants of a protein. Dry laboratory investigations promise a holistic treatment of the relationship between protein sequence, structure, and function. Significant efforts are made in the dry laboratory to map protein conformation spaces and underlying energy landscapes of proteins. The majority of such efforts employ well-studied computational templates, such as Molecular Dynamics and Monte Carlo. The focus of this review is on a third emerging template, stochastic optimization under the umbrella of evolutionary computation. algorithms based on such a template, also known as evolutionary algorithms, are showing promise in addressing fundamental computational challenges in protein structure modeling and are opening up new avenues in protein modeling research. This review summarizes evolutionary algorithms for novice readers, while highlighting recent developments that showcase current, state-of-the-art capabilities for *** ubiquitous presence of proteins in chemical pathways in the cell and their key role in many human disorders motivates a growing body of protein modeling studies to unravel the relationship between protein structure and function. The foundation of such studies is the realization that knowledge of the structures a protein accesses under physiological conditions is key to a detailed understanding of its biological function and the design of therapeutic compounds for the purpose of altering misfunction in aberrant variants of a prot
Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach ...
详细信息
Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach to manage runtime variability, by means of late binding the variation points at runtime. During the system's execution, the DRS deploys different configurations to satisfy the changing requirements according to a multiobjective criterion (e.g., insufficient battery level, requested quality of service). Search-based software engineering and, in particular, multiobjective evolutionary algorithms (MOEAs), can generate valid configurations of a DSPL at runtime. Several approaches use MOEAs to generate optimum configurations of a Software Product Line, but none of them consider DSPLs for mobile devices. In this paper, we explore the use of MOEAs to generate at runtime optimum configurations of the DSPL according to different criteria. The optimization problem is formalized in terms of a Feature Model (FM), a variability model. We evaluate six existing MOEAs by applying them to 12 different FMs, optimizing three different objectives (usability, battery consumption and memory footprint). The results are discussed according to the particular requirements of a DRS for mobile applications, showing that PAES and NSGA-II are the most suitable algorithms for mobile environments. (C) 2015 Elsevier Inc. All rights reserved.
The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-po...
详细信息
The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-point format can improve performance and reduce energy consumption, the process of tuning the right bit length is known as a time-consuming task, since it is a combinatorial optimisation problem guided by the accumulative arithmetic computation error. A novel evolutionary approach to accelerate the process of converting algorithms from the floating-point to fixed-point format is presented. Results are demonstrated by converting three computing-intensive algorithms from the mobile robotic scenario, where data error accumulated during execution is influenced by external factors, such as sensor noise and navigation environment characteristics. The proposed evolutionary algorithm accelerated the conversion process by up to 2.5 x against the state-of-the-art methods, allowing even further bit-length optimisations.
In this paper a methodology for the delineation of local labour markets (LLMs) using evolutionary algorithms is proposed. This procedure, based on that in Florez-Revuelta et al. [13,14], introduces three modifications...
详细信息
In this paper a methodology for the delineation of local labour markets (LLMs) using evolutionary algorithms is proposed. This procedure, based on that in Florez-Revuelta et al. [13,14], introduces three modifications. First, initial groups of municipalities with a minimum size requirement are built using the travel time between them. Second, a not fully random initiation algorithm is proposed. And third, as a final stage of the procedure, a contiguity step is implemented. These modifications significantly decrease the computational times of the algorithm (up to a 99%) without any deterioration of the quality of the solutions. The optimization algorithm may give a set of potential solutions with very similar values with respect to the objective function what would lead to different partitions, both in terms of number of markets and their composition. In order to capture their common aspects an algorithm based on a cluster partitioning of k-means type is presented. This stage of the procedure also provides a ranking of LLMs foci useful for planners and administrations in decision-making processes on issues related to labour activities. Finally, to evaluate the performance of the algorithm a toy example with artificial data is analysed. The full methodology is illustrated through a real commuting data set of the region of Aragon (Spain).
In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-call...
详细信息
In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-called grey box identification where we search for the characteristic of the system's valve, the second problem is black box identification where we search the model of the system with the usage of system's measurements and the third one is a system's controller design. We solved these problems with the usage of genetic algorithms, differential evolution, evolutionary strategies, genetic programming and a developed approach called AMEBA algorithm. All methods have been proven to be very useful for solving problems of the grey box identification and design of the controller for the mentioned system but AMEBA algorithm have also been successfully used in black box identification problem where it generated a suitable model.
暂无评论