Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are difficult to develop because of factors like UAV motion, scene complexity and so on. In this paper, we propose a new fram...
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The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social f...
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Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming if all infeasible ...
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ISBN:
(纸本)9781479914869
Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming if all infeasible individuals are repaired. Therefore, so far the infeasible individuals to be repaired are randomly selected from the population and the strategy of choosing individuals to be repaired has not been studied yet. In this paper, the Species-based Repair Strategy (SRS) is proposed to select representative infeasible individuals instead of the random selection for gradient-based repair. The proposed SRS strategy has been applied to εDEag which repairs the random selected individuals using the gradient-based repair. The new algorithm is named SRS-εDEag. Experimental results show that SRS-εDEag outperforms εDEag in most benchmarks. Meanwhile, the number of repaired individuals is reduced markedly.
Clonal selection algorithms (CSAs) are a kind of Artificial Immune Algorithms (AIAs). In this paper, recent advances in clonal selection algorithms are summarized and reviewed. First, the basic framework of clonal sel...
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Clonal selection algorithms (CSAs) are a kind of Artificial Immune Algorithms (AIAs). In this paper, recent advances in clonal selection algorithms are summarized and reviewed. First, the basic framework of clonal selection algorithms is given. Second, various types of applications using clonal selection algorithms are summarized, including global optimization, constrained optimization, combinatorial optimization, multiobjective optimization, dynamic optimization and other applications. Last, a brief conclusion and some remarks about clonal selection algorithms are given.
String stretched tightly along a sequence of fixed grasp points takes the shape of a polygonal arc. In this work, we investigate how many points are necessary and sufficient to grasp and tie arbitrary knots while main...
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ISBN:
(纸本)9781467380270
String stretched tightly along a sequence of fixed grasp points takes the shape of a polygonal arc. In this work, we investigate how many points are necessary and sufficient to grasp and tie arbitrary knots while maintaining tension, so that the string remains polygonal. This approach allows reasoning that is entirely geometric, which does not rely on potentially inaccurate dynamic models of the string or detailed knowledge of physical characteristics of the string. Algorithms are proposed to determine the contact locations, and generate the motions needed to tie arbitrary knots. This work shows that a number of grasp points that is linear in the number of crossings in a knot diagram is sufficient to immobilize string in a polygonal shape with the topology of an arbitrary knot, or to fold or unfold the knot from a straight configuration.
The Shortest Path (SP) problems are conventional combinatorial optimization problems. There are many deterministic algorithms for solving the shortest path problems in static topologies. However, in dynamic topologies...
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The Shortest Path (SP) problems are conventional combinatorial optimization problems. There are many deterministic algorithms for solving the shortest path problems in static topologies. However, in dynamic topologies, these deterministic algorithms are not efficient due to the necessity of restart. In this paper, an improved Genetic Algorithm (GA) with four local search operators for Dynamic Shortest Path (DSP) problems is proposed. The local search operators are inspired by Dijkstra's Algorithm and carried out when the topology changes to generate local shortest path trees, which are used to promote the performance of the individuals in the population. The experimental results show that the proposed algorithm could obtain the solutions which adapt to new environments rapidly and produce high-quality solutions after environmental changes.
The main goal in proteomics is to describe the proteome in a comprehensive and accurate way, and enzymolysis is a key step in the process of large-scale proteomics experiments. For the same protein samples, using diff...
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Evolutionary clustering is a hot research topic that clusters the time-stamped data and it is essential to some important applications such as data streams clustering and social network analysis. An evolutionary clust...
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ISBN:
(纸本)9781479914869
Evolutionary clustering is a hot research topic that clusters the time-stamped data and it is essential to some important applications such as data streams clustering and social network analysis. An evolutionary clustering should accurately reflect the current data at any time step while simultaneously not deviate too drastically from the recent past. In this paper, the differential evolution (DE) is applied to deal with the evolutionary clustering problem. Comparing with the typical k-means, evolutionary clustering based on DE (deEC) could perform a global search in the solution space. Experimental results over synthetic and real-world data sets demonstrate that the deEC provides robust and adaptive solutions.
In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Sur...
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In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Surprise Abstraction tree （TSA-tree）. The second step of the approach is to combine the exacted global feature and 1 nearest neighbor to classify time series. The proposed approach is compared with a number of known classifiers by experiments in artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.
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