Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation最新文献_第5页

Directional communication in evolved multiagent teams 进化的多智能体团队中的定向通信

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598299

Justin K. Pugh, Skyler Goodell, Kenneth O. Stanley

{"title":"Directional communication in evolved multiagent teams","authors":"Justin K. Pugh, Skyler Goodell, Kenneth O. Stanley","doi":"10.1145/2576768.2598299","DOIUrl":"https://doi.org/10.1145/2576768.2598299","url":null,"abstract":"The question of how to best design a communication architecture is becoming increasingly important for evolving autonomous multiagent systems. Directional reception of signals, a design feature of communication that appears in most animals, is present in only some existing artificial communication systems. This paper hypothesizes that such directional reception benefits the evolution of communicating autonomous agents because it simplifies the language required to express positional information, which is critical to solving many group coordination tasks. This hypothesis is tested by comparing the evolutionary performance of several alternative communication architectures (both directional and non-directional) in a multiagent foraging domain designed to require a basic \"come here\" type of signal for the optimal solution. Results confirm that directional reception is a key ingredient in the evolutionary tractability of effective communication. Furthermore, the real world viability of directional reception is demonstrated through the successful transfer of the best evolved controllers to real robots. The conclusion is that directional reception is important to consider when designing communication architectures for more complicated tasks in the future.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Efficient identification of improving moves in a ball for pseudo-boolean problems 伪布尔问题中球的改进步的有效识别

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598304

F. Chicano, L. D. Whitley, Andrew M. Sutton

引用次数: 39

An improved immune inspired hyper-heuristic for combinatorial optimisation problems 一种用于组合优化问题的改进免疫启发超启发式算法

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598241

Kevin Sim, E. Hart

引用次数: 29

Static vs. dynamic populations in genetic algorithms for coloring a dynamic graph 动态图着色的遗传算法中的静态种群与动态种群

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598233

C. Monical, F. Stonedahl

{"title":"Static vs. dynamic populations in genetic algorithms for coloring a dynamic graph","authors":"C. Monical, F. Stonedahl","doi":"10.1145/2576768.2598233","DOIUrl":"https://doi.org/10.1145/2576768.2598233","url":null,"abstract":"We studied the performance of genetic algorithms for coloring dynamic graphs under a variety of experimental conditions, focusing on the relationship between the dynamics of the graph and that of the algorithm. Graph coloring is a well-studied NP-hard problem, while dynamic graphs are a natural way to model a diverse range of dynamic systems. Dynamic graph coloring can be applied to online scheduling in a changing environment, such as the online scheduling of conflicting tasks. As genetic algorithms (GAs) have been effective for graph coloring and are adaptable to dynamic environments, they are a promising choice for this problem. Thus, we compared the performance of three algorithms: a GA that maintained a single population adapting to the dynamic graph (DGA), a GA that restarted with a fresh population for the static graph of each time-step (SGA), and DSATUR, a well-known heuristic graph coloring algorithm re-applied at each time-step. We examined the relative performance of these algorithms for dynamic graphs of different sizes, edge densities, structures, and change rates, using different amounts of evolution between time-steps. Overall, the DGA consistently outperformed the SGA, being particularly dominant at low change rates, and under certain conditions was able to outperform DSATUR.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Multi-objective routing optimisation for battery-powered wireless sensor mesh networks 电池供电无线传感器网状网络的多目标路由优化

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598311

A. Rahat, R. Everson, J. Fieldsend

{"title":"Multi-objective routing optimisation for battery-powered wireless sensor mesh networks","authors":"A. Rahat, R. Everson, J. Fieldsend","doi":"10.1145/2576768.2598311","DOIUrl":"https://doi.org/10.1145/2576768.2598311","url":null,"abstract":"Mesh network topologies are becoming increasingly popular in battery powered wireless sensor networks, primarily due to the extension of network range and resilience against routing failures. However, multi-hop mesh networks suffer from higher energy costs, and the routing strategy directly affects the lifetime of nodes with limited energy sources. Hence while planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a novel multi-objective routing optimisation approach using evolutionary algorithms to approximate the optimal trade-off between minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly and thus permit dynamic optimisation for self-healing networks, our approach uses novel $k$-shortest paths based search space pruning in conjunction with a new edge metric, which associates the energy cost at a pair of nodes with the link between them. We demonstrate our solution on a real network, deployed in the Victoria & Albert Museum, London. We show that this approach provides better trade-off solutions in comparison to the minimum energy option, and how a combination of solutions over the lifetime of the network can enhance the overall minimum lifetime.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117336575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Generalized asymmetric partition crossover (GAPX) for the asymmetric TSP 非对称TSP的广义非对称分割交叉

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598245

R. Tinós, L. D. Whitley, G. Ochoa

引用次数: 26

Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach 云计算中基于蚁群优化方法的能量感知虚拟机布局调度

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598265

X. Liu, Zhi-hui Zhan, Ke-Jing Du, Wei-neng Chen

{"title":"Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach","authors":"X. Liu, Zhi-hui Zhan, Ke-Jing Du, Wei-neng Chen","doi":"10.1145/2576768.2598265","DOIUrl":"https://doi.org/10.1145/2576768.2598265","url":null,"abstract":"Cloud computing provides resources as services in pay-as-you-go mode to customers by using virtualization technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in data center. More physical servers means more energy consumption and more money cost. Therefore, the VM placement (VMP) problem is significant in cloud computing. This paper proposes an approach based on ant colony optimization (ACO) to solve the VMP problem, named as ACO-VMP, so as to effectively use the physical resources and to reduce the number of running physical servers. The number of physical servers is the same as the number of the VMs at the beginning. Then the ACO approach tries to reduce the physical server one by one. We evaluate the performance of the proposed ACO-VMP approach in solving VMP with the number of VMs being up to 600. Experimental results compared with the ones obtained by the first-fit decreasing (FFD) algorithm show that ACO-VMP can solve VMP more efficiently to reduce the number of physical servers significantly, especially when the number of VMs is large.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 101

A two-leveled hybrid dendritic cell algorithm under imprecise reasoning 非精确推理下的两级混合树突状细胞算法

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598217

Zeineb Chelly, Zied Elouedi

{"title":"A two-leveled hybrid dendritic cell algorithm under imprecise reasoning","authors":"Zeineb Chelly, Zied Elouedi","doi":"10.1145/2576768.2598217","DOIUrl":"https://doi.org/10.1145/2576768.2598217","url":null,"abstract":"The Dendritic Cell Algorithm(DCA) is a bio-inspired algorithm based on the behavior of Dendritic Cells(DCs). The DCA performance relies on its data pre-processing phase where feature extraction and signal categorization are performed and which are based on the use of the Principal Component Analysis(PCA) technique. However, using PCA presents a limitation as it destroys the underlying semantics of the features after reduction. To overcome this limitation, Rough Set Theory(RST) was applied as a pre-processor; but, still the developed rough approach presents an information loss as data should be discretized beforehand. Indeed, DCA was known to be sensitive to the input class data order. This is due to the crisp separation between the two DCs contexts; semi-mature and mature. Thus, the aim of this paper is to develop a novel DCA version based on a two-leveled hybrid model handling the mentioned DCA shortcomings. In the top-level, our proposed algorithm applies a more adequate feature extraction technique based on Fuzzy Rough Set Theory(FRST) to build a solid data pre-processing phase. At the bottom level, our algorithm applies Fuzzy Set Theory to smooth the crisp separation between the DCs contexts. Results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115832693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Runtime analysis for maximizing population diversity in single-objective optimization 单目标优化中种群多样性最大化的运行时分析

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598251

Wanru Gao, F. Neumann

引用次数: 18

A modified XCS classifier system for sequence labeling 一种改进的序列标记XCS分类器系统

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598352

Masaya Nakata, T. Kovacs, K. Takadama

{"title":"A modified XCS classifier system for sequence labeling","authors":"Masaya Nakata, T. Kovacs, K. Takadama","doi":"10.1145/2576768.2598352","DOIUrl":"https://doi.org/10.1145/2576768.2598352","url":null,"abstract":"This paper introduces XCS-SL, an extension of XCS for sequence labeling, a form of time-series classification where every input has a class label. Specifically, we consider sequence labeling tasks where on each time step we receive an input/class pair. In sequence labeling the correct class of an input may depend on data received on previous time stamps, so a learner may need to refer to data at previous time stamps. That is, some classification rules (called classifiers' here) must include conditions on previous inputs (a kind of memory). We assume the agent does not know how many conditions on previous inputs are needed to classify the current input, and the number of conditions/memories needed may be different for each input. Hence, using a fixed number of conditions is not a good solution. A novel idea we introduce is classifiers that have a variable-length condition to refer back to data at previous times. The condition can grow and shrink to find a suitable memory size. On a benchmark problem XCS-SL can learn optimal classifiers, and on a real-world sequence labeling task, it derived high classification accuracy and discovered interesting knowledge that shows dependencies between inputs at different times.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125872053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17