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...
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ISBN:
(纸本)9781509046010
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 process are pointed out and analyzed. The definition of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals (PEACh) is proposed. Another objective of the paper is to start a discussion concerning the computation load measurement in the evolutionary computation field. As shown, the Fitness Function Evaluation number is not always a fair measure and may be significantly affected by the quality of method implementation.
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of ...
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ISBN:
(纸本)9781509047802
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.
Previous running time analyses of evolutionary algorithms (EAs) in noisy environments often studied the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation. In this paper, we study a...
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ISBN:
(纸本)9781450349208
Previous running time analyses of evolutionary algorithms (EAs) in noisy environments often studied the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results.
This paper focuses on an operation optimisation problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzl...
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This paper focuses on an operation optimisation problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzle types as well as components to heads, assigning feeders to slots and determining component pickup and placement sequences. According to the depth of making decisions, the sub-problems are first classified into two layers. Based on the classification, a two-stage mixed-integer linear programming (MILP) is developed to describe it and a two-stage problem-solving frame with a hybrid evolutionary algorithm (HEA) is proposed. In the first stage, a constructive heuristic is developed to determine the set of nozzle types assigned to each head and the total number of assembly cycles;in the second stage, constructive heuristics, an evolutionary algorithm with two evolutionary operators and a tabu search (TS) with multiple neighbourhoods are combined to solve all the sub-problems simultaneously, where the results obtained in the first stage are taken as constraints. Computational experiments show that the HEA can obtain good near-optimal solutions for small size instances when compared with an optimal solver, Cplex, and can provide better results when compared with a TS and an EA for actual instances.
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 searc...
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ISBN:
(纸本)9781509046010
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 evolutionary algorithms to tackle such problems in an efficient manner with better solutions. For this reason, this work proposes a new crossover technique based on covariance learning with Euclidean neighborhood which has been incorporated in the basic L-SHADE algorithm. The goal of this new technique is to help L-SHADE establish a suitable coordinate system for the crossover operator. This helps enhance L-SHADE capability to solve real world problems with difficult characteristics and nonlinear constraints. The proposed algorithm, namely L-covnSHADE, is tested on one of the challenging benchmarks which is the IEEE CEC' 11 on real-world numerical optimization problems. This set consists of 22 real-world problems with diverse stimulating characteristics and a dimensionality ranging from 1 to 240 dimensions. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to the L-SHADE algorithm and other state-of-the-art algorithms including the winner of the CEC2011 competition.
This paper presents a study on optimized control for a magnetically levitated (MAGLEV) suspension system. Unstable magnetically levitated system is modelled and integer order PID (IOPID) and fractional order PID (FOPI...
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Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automat...
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ISBN:
(纸本)9781450349390
Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automatic ML (Auto-ML) area has emerged to solve this task. With this issue in mind, in this work we are interested in a specific type of classification problem, called multi-label classification (MLC). In MLC, each example in the dataset can be associated to one or more class labels, making the task considerably harder than traditional, single-label classification. In addition, the cost of learning raises due to the higher complexity of the data. Although the literature has proposed some methods to solve the Auto-ML task, those methods address only the traditional, single-label classification problem. By contrast, this work proposes the first method (an evolutionary algorithm) for solving the Auto-ML task in MLC, i.e., the first method for automatically selecting and configuring the best MLC algorithm for a given input dataset. The proposed evolutionary algorithm is evaluated on three MLC datasets, and compared against two baseline methods according to four different multi-label predictive accuracy measures. The results show that the proposed evolutionary algorithm is competitive against the baselines, but there is still room for improvement.
Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the ...
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ISBN:
(数字)9781510609921
ISBN:
(纸本)9781510609914;9781510609921
Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can'tt accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. As a matter of fact, the increasing interactions amongst different CPS are sta...
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ISBN:
(纸本)9781450344876
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. As a matter of fact, the increasing interactions amongst different CPS are starting to generate unpredictable behaviors and emerging properties, often leading to unforeseen and/or undesired results. Rather than being an unwanted byproduct, these interactions could, however, become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project, presented in this paper, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the basis of local policies and exhibit a collective behavior capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry b...
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New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry by using an ad hoc search space and genetic operators. The new OsC2 and Os2C structures have a lower enthalpy than any known so far. Moreover, the layered pattern of OsC2 serves as a blueprint for building new crystals by adding or removing layers of carbon and/or osmium and generating many other Os + C structures like Os2C, OsC, OsC2 and OsC4. These again have a lower enthalpy than all the investigated structures, including those of the present work. The mechanical, vibrational and electronic properties are discussed as well.
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