The Time-Dependent Orienteering Problem (TDOP) is a generalization of the Orienteering Problem where graph weights vary with time. It has many real life applications particularly associated with transport networks, in...
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ISBN:
(纸本)9783319591056;9783319591049
The Time-Dependent Orienteering Problem (TDOP) is a generalization of the Orienteering Problem where graph weights vary with time. It has many real life applications particularly associated with transport networks, in which travel time between two points depends on the moment of start. The paper presents an evolutionary algorithm with embedded local search operators and heuristic crossover, which solves TDOP. The algorithm was tested on TDOP benchmark instances and in most cases achieved optimal or near optimal results clearly outperforming other published methods.
at present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have bee...
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ISBN:
(纸本)9781479925483
at present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. Can evolutionary algorithms be designed automatically by computer? In this paper, a novel evolutionary algorithm based on automatically designing of genetic operators is presented to address this problem. The resulting algorithm not only explores solutions in the problem space, but also automatically generates genetic operators in the operator space for each generation. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted, and the results show that the proposed algorithm can outperform standard Differential Evolution (DE) algorithm.
Dense pixel matching is an essential step required by many computer vision applications. While a large body of work has addressed quite successfully the rectified scenario, accurate pixel correspondence between an ima...
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ISBN:
(纸本)9783662455234;9783662455227
Dense pixel matching is an essential step required by many computer vision applications. While a large body of work has addressed quite successfully the rectified scenario, accurate pixel correspondence between an image and a distorted version remains very challenging. Exploiting an analogy between sequences of genetic material and images, we propose a novel genetics inspired algorithm where image variability is treated as the product of a set of image mutations. As a consequence, correspondence for each scanline of the initial image is formulated as the optimisation of a path in the second image minimising a fitness function penalising mutations. This optimisation is performed by a evolutionary algorithm which, in addition to provide fast convergence, implicitly ensures consistency between successive scanlines. Performance evaluation on locally and globally distorted images validates our bio-inspired approach.
An evolutionary algorithm is proposed for obtainment of the matching domain blocks of fractal partition in image compression. It makes use of the partitioned iterated function system(IFS) and fractal image. The techni...
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ISBN:
(纸本)081944281X
An evolutionary algorithm is proposed for obtainment of the matching domain blocks of fractal partition in image compression. It makes use of the partitioned iterated function system(IFS) and fractal image. The technique described here utilizes the evolutionary algorithm, which greatly decreases the search space for finding the self-similarities in the given image. Considering the special properties of the problem, some genetic operators are designed and used in combination with the standard operators in order to improve the effectiveness of the evolutionary algorithm. Both theoretical analyses and experiments show that the algorithm is robust and higher compression ratio and image quality can be achieved.
algorithmically generating music using specialized algorithms is a growing focus in computer science. The success of these specialized algorithms in generating music, however, depends heavily on the fitness function t...
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ISBN:
(纸本)9781509027750
algorithmically generating music using specialized algorithms is a growing focus in computer science. The success of these specialized algorithms in generating music, however, depends heavily on the fitness function that is used to score the generated music and equally as important is how the fitness function is designed. Artificial intelligence in the computational composition can use certain feature set values derived from melodic analysis to serve as criteria for these fitness functions. This study explores two methods in defining the key features to be used as fitness criteria for algorithmic music generation of music that can be considered under a mix of two musical genres or hybrid-genre music. The jSymbolic tool was used to extract 101 features from musical pieces that fall under two genres. This was then reduced to a smaller feature set for use as fitness criteria. Two methods for feature reduction was explored;a decision-tree-based technique and a high-correlation-filtering technique. The study was able to confirm that each technique can be used to compose hybrid-genre music with 86% success-rate as confirmed by SVM when validated under the same dataset used in the study. This study does not claim to consistently result in a high success rate for all existing datasets.
P2P multicasting is a novel approach to provide cost effective streaming services over the Internet. This paper addresses the overlay network design for P2P multicasting. We assume that the P2P multicasting system is ...
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ISBN:
(纸本)9783642212185
P2P multicasting is a novel approach to provide cost effective streaming services over the Internet. This paper addresses the overlay network design for P2P multicasting. We assume that the P2P multicasting system is static with low membership change rate (e.g., corporate videoconferencing, distance learning, delivery of important messages, IPTV with STB). The problem consists in joint optimization of multicast flows and access link capacity. The objective is to minimize the network cost calculated as the cost of access links. An effective evolutionary algorithm is proposed. We report results of the algorithm with comparison against optimal results and other heuristic algorithms including Lagrangean relaxation algorithm and constructive algorithm. The proposed algorithm can be used to various problems related to overlay networks, e.g., streaming services, computing and storage systems.
Image segmentation by region growing method is robust fast and very easy to implemented. but it suffers. from: the threshold problem, initialization, and sensitivity to noise. evolutionary algorithms are particular me...
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ISBN:
(纸本)9781509008117
Image segmentation by region growing method is robust fast and very easy to implemented. but it suffers. from: the threshold problem, initialization, and sensitivity to noise. evolutionary algorithms are particular methods for optimizing functions: they have a great ability to find the global optimum of a problem. In this paper, we used evolutionary algorithms to get over the three problems. We have proposed a segmentation method based on region growing and evolutionary algorithms. The proposed approach is validated on four hundred synthetic Images and medical. The results show the good performance of this approach.
Microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed...
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ISBN:
(纸本)9783319091921;9783319091914
Microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions. This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.
Lossy image compression is ubiquitously used for storage and transmission at lower rates. Among the existing lossy image compression methods, the JPEG standard is the most widely used technique in the multimedia world...
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ISBN:
(纸本)9781728185798
Lossy image compression is ubiquitously used for storage and transmission at lower rates. Among the existing lossy image compression methods, the JPEG standard is the most widely used technique in the multimedia world. Over the years, numerous methods have been proposed to suppress the compression artifacts introduced in JPEG-compressed images. However, all current learning-based methods include deep convolutional neural networks (CNNs) that are manually-designed by researchers. The network design process requires extensive computational resources and expertise. Focusing on this issue, we investigate evolutionary search for finding the optimal residual block based architecture for artifact removal. We first define a residual network structure and its corresponding genotype representation used in the search. Then, we provide details of the evolutionary algorithm and the multi-objective function used to find the optimal residual block architecture. Finally, we present experimental results to indicate the effectiveness of our approach and compare performance with existing artifact removal networks. The proposed approach is scalable and portable to numerous low-level vision tasks.
This paper presents a new solution for solving continuous inventory problem in estimating the amount of purchase item and prediction on the maximization of profit in a restaurant. Particle swarm optimization (PSO) whi...
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ISBN:
(纸本)9781479911257
This paper presents a new solution for solving continuous inventory problem in estimating the amount of purchase item and prediction on the maximization of profit in a restaurant. Particle swarm optimization (PSO) which has the ability of better convergence and efficiency is employed. The solution focuses on a single item in inventory list and single-buyer single-vendor relationship where demand presents as stochastic problem in a restaurant. Result and findings was compared with genetic algorithm (GA). Several testing were conducted to access the performance of each algorithm based on parameters and computational times. The finding demonstrates that these algorithms are competitive in solving this particular problem. The outcome is beneficial to the restaurant in terms of making decision on inventory and subsequently able to sustain the business.
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