In simulation-based evolutionary Multi-objective Optimization the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, t...
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ISBN:
(纸本)9781509006229
In simulation-based evolutionary Multi-objective Optimization the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space, for example with the R-NSGA-II algorithm [9], which uses a reference point specified by the decision maker. When stochastic systems are simulated, the uncertainty of the objective values might degrade the optimization performance. By sampling the solutions multiple times this uncertainty can be reduced. However, resampling methods reduce the overall number of evaluated solutions which potentially worsens the optimization result. In this article, a Dynamic Resampling strategy is proposed which identifies the solutions closest to the reference point which guides the population of the evolutionary Algorithm. We apply a single-objective Ranking and Selection resampling algorithm in the selection step of R-NSGA-II, which considers the stochastic reference point distance and its variance to identify the best solutions. We propose and evaluate different ways to integrate the sampling allocation method into the evolutionary Algorithm. On the one hand, the Dynamic Resampling algorithm is made adaptive to support the EA selection step, and it is customized to be used in the time-constrained optimization scenario. Furthermore, it is controlled by other resampling criteria, in the same way as other hybrid DR algorithms. On the other hand, R-NSGA-II is modified to rely more on the scalar reference point distance as fitness function. The results are evaluated on a benchmark problem with variable noise landscape.
The paper addresses the problem of power distribution grids optimization in terms of reducing costs related to undelivered energy due to network failures. Grid optimization is realized by allocating the cut points tha...
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ISBN:
(纸本)9781509026678
The paper addresses the problem of power distribution grids optimization in terms of reducing costs related to undelivered energy due to network failures. Grid optimization is realized by allocating the cut points that may limit the effects of the failure on the network operation. A possible method of allocating these points, explored in this work, is the optimization of the grid structure using evolutionary algorithms, where the cost function to be minimized is the amount of undelivered energy.
evolutionary Algorithm is a well-known meta-heuristics para-digm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to ...
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ISBN:
(纸本)9783319503493;9783319503486
evolutionary Algorithm is a well-known meta-heuristics para-digm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for generating the training samples. We evaluate the efficacy and robustness of our proposed approach with benchmark instances of Quadratic Assignment Problem.
Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. For new questions they have to decide ...
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ISBN:
(纸本)9783319490045;9783319490038
Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. For new questions they have to decide which datasets are suitable and in which terminology and modelling style to phrase the SPARQL query. In this work we present an evolutionary algorithm to help with this challenging task. Given a training list of source-target node-pair examples our algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The learned patterns can be visualised to form the basis for further investigation, or they can be used to predict target nodes for new source nodes. Amongst others, we apply our algorithm to a dataset of several hundred human associations (such as "circle-square") to find patterns for them in DBpedia. We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with > 7.9 billion triples. Further, we use the resulting SPARQL queries to mimic human associations with a Mean Average Precision (MAP) of 39.9% and a Recall@10 of 63.9%.
We consider the task of determining the pose of a depth camera based on a single target depth image and a 3D model of the indoor environment that the image was taken in. We identify the quality of a pose estimate with...
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ISBN:
(纸本)9781509006229
We consider the task of determining the pose of a depth camera based on a single target depth image and a 3D model of the indoor environment that the image was taken in. We identify the quality of a pose estimate with summed differences between depth values in the target depth image and a depth image generated synthetically by using that pose estimate in the 3D model. We then propose an evolutionary algorithm for optimizing pose estimates. The performance of that algorithm is evaluated in two artificial test environments, and perspectives for use of the algorithm in real environments are discussed.
This paper illustrates a parallel implementation of evolutionary induction of model trees. An objective is to demonstrate that such evolutionary evolved trees, which are emerging alternatives to the greedy top-down so...
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ISBN:
(纸本)9783319393780;9783319393773
This paper illustrates a parallel implementation of evolutionary induction of model trees. An objective is to demonstrate that such evolutionary evolved trees, which are emerging alternatives to the greedy top-down solutions, can be successfully applied to large scale data. The proposed approach combines message passing (MPI) and shared memory (OpenMP) paradigms. This hybrid approach is based on a classical master-slave model in which the individuals from the population are evenly distributed to available nodes and cores. The most time consuming operations like recalculation of the regression models in the leaves as well as the fitness evaluation and genetic operators are executed in parallel on slaves. Experimental validation on artificial and real-life datasets confirms the efficiency of the proposed implementation.
The discovery of communities in complex networks is a challenging problem with various applications in the real world. Classic examples of networks include transport networks, the immune system, human brain and social...
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ISBN:
(纸本)9781509038985
The discovery of communities in complex networks is a challenging problem with various applications in the real world. Classic examples of networks include transport networks, the immune system, human brain and social networks. Given a certain grouping of nodes into communities, a good measure is needed to evaluate the quality of the community structure based on the definition that a strong community has dense intra-connections and sparse outside community links. This paper investigates several fitness functions in an evolutionary approach to community detection in complex networks. Moreover, these fitness functions are used to study dynamic networks using an extended evolutionary algorithm designed to handle changes in the network structure. Computational experiments are performed for several real-world networks which have a known community structure and thus can be evaluated. The obtained results confirm the ability of the proposed method to efficiently detect communities for both static and dynamic complex networks.
This paper presents a new hybrid method, which integrates the structured laser light, decoupled dynamic models, and evolutionary optimization strategy, to identify dynamic parameters of autonomous underwater vehicles....
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ISBN:
(纸本)9780791857199
This paper presents a new hybrid method, which integrates the structured laser light, decoupled dynamic models, and evolutionary optimization strategy, to identify dynamic parameters of autonomous underwater vehicles. This is the first research that investigates the utilization of laser scanned images for the system identification of underwater vehicles. The AUV's equations of motions and the dynamic parameter identification using the AUV's decoupled motions will be illustrated. The working principles to calculate the AUV's positons using the structured laser light will be explained with respect to the surge and pitch motions. The evolutionary optimization strategy used to generate the AUV's dynamic parameters will be presented.
The use of anti-virus software has become something of an act of faith. A recent study showed that more than 80% of all personal computers have anti-virus software installed. However, the protection mechanisms in plac...
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ISBN:
(纸本)9783319311531
The use of anti-virus software has become something of an act of faith. A recent study showed that more than 80% of all personal computers have anti-virus software installed. However, the protection mechanisms in place are far less effective than users would expect. Malware analysis is a classical example of cat-and-mouse game: as new antivirus techniques are developed, malware authors respond with new ones to thwart analysis. Every day, anti-virus companies analyze thousands of malware that has been collected through honeypots, hence they restrict the research to only already existing viruses. This article describes a novel method for malware obfuscation based an evolutionary opcode generator and a special ad-hoc packer. The results can be used by the security industry to test the ability of their system to react to malware mutations.
In order to take advantages of evolutionary algorithms inspired by different biological evolutions, varieties of approaches have been proposed to combine them together. One of them is the portfolio approach, which kee...
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ISBN:
(纸本)9781509006229
In order to take advantages of evolutionary algorithms inspired by different biological evolutions, varieties of approaches have been proposed to combine them together. One of them is the portfolio approach, which keeps choosing a component algorithm from a portfolio of evolutionary algorithms (EAs) to run during the optimizing process. In our approach, each component algorithm has its own population and runs independently without information exchange. At the beginning of each generation, only the component algorithm with the best predicted performance is allowed to run. The proposed portfolio approach is tested on the CEC2016 real-parameter single objective optimization benchmarks. The results show that it is competitive.
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