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检索条件"主题词=evolutionary computation"

共 14926 条记录，以下是4871-4880 订阅

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Performance Evaluation of Sensor-Based Detection Schemes on Dynamic Optimization Problems

Performance Evaluation of Sensor-Based Detection Schemes on ...

引用

IEEE Symposium on computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Altin, Lokman Topcuoglu, Haluk Rahmi Marmara Univ Dept Comp Engn TR-34722 Istanbul Turkey

ISBN: (纸本)9781479945153

Most of the real world optimization problems in different domains demonstrate dynamic behavior, which can be in the form of changes in the objective function, problem parameters and/or constraints for different time periods. Detecting the points in time where a change occurs in the landscape is a critical issue for a large number of evolutionary dynamic optimization techniques in the literature. In this paper, we present an empirical study whose focus is the performance evaluation of various sensor-based detection schemes by using two well known dynamic optimization problems, which are moving peaks benchmark (MPB) and dynamic knapsack problem (DKP). Our experimental evaluation by using two dynamic optimization problem validates the sensor-based detection schemes considered, where the effectiveness of each scheme is measured with the average rate of correctly identified changes and the average number of sensors invoked to detect a change.

关键词： dynamic programming evolutionary computation knapsack problems sensors DKP MPB dynamic knapsack problem dynamic optimization problems evolutionary dynamic optimization techniques moving peaks benchmark performance evaluation point detection sensor-based detection schemes Benchmark testing Detectors Optimization Sociology Statistics Vehicle dynamics Performance Evaluation knapsack problems Benchmark testing Androgenetic Alopecia Sociology Vehicle dynamics evolutionary algorithm dynamic programming Sensors Dynamical Problem

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Exploring the Kuramoto model of coupled oscillators in minimally cognitive evolutionary robotics tasks

Exploring the Kuramoto model of coupled oscillators in minim...

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2010 IEEE World Congress on computational Intelligence

作者： Moioli, Renan C. Vargas, Patricia A. Husbands, Phil Univ Sussex Dept Informat CCNR Brighton BN1 9QH E Sussex England Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9781424481262

This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuramoto model of coupled oscillators as its nervous system. The main objectives are to shed new light into the role of neuronal synchronisation and phase towards the generation of cognitive behaviours and to initiate an investigation on the efficacy of such systems as practical robot controllers. The first experiment is an active categorical perception task in which the robot has to discriminate between moving circles and squares. In the second task, the robotic agent has to approach moving circles with both normal and inverted vision thus adapting to both scenarios. These tasks were chosen for being considered as benchmarks in the evolutionary robotics and adaptive behaviour communities. The results obtained indicate the feasibility of the framework in the analysis and generation of embodied cognitive behaviours.

关键词： Kuramoto model cognition cognitive evolutionary robotic task coupled oscillator evolutionary computation neural nets neuronal synchronisation oscillators robot dynamic robot dynamics robot vision robot vision synchronisation

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A Hybrid Multi-Objective evolutionary Algorithm for the Team Orienteering Problem 4

A Hybrid Multi-Objective Evolutionary Algorithm for the Team...

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4th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Bederina, Hiba Hifi, Mhand Univ Picardie Jules Verne EPROAD F-80000 Amiens France

ISBN: (纸本)9781509064656

The Team Orienteering Problem (namely TOP) consists in finding the routings for a set of vehicles that maximize the total profit reached by visiting a series of customers. In this paper, a hybrid multi-objective evolutionary algorithm based on a special genetic algorithm and local search operators is proposed for approximately solving the TOP. Two conflicting objectives are considered: to minimize the total travel cost and to maximize the profit linked to the visited customers. The performance of the proposed method is evaluated on a set of benchmark instances extracted from Chao et al. [2] and its provided results are compared to those reached by the best methods available in the literature. Encouraging results have been obtained.

关键词： Sociology Statistics Optimization Biological cells Information technology Genetic algorithms evolutionary computation

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Learning the Designer's Preferences to Drive Evolution 1

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23rd European International Conference on the Applications of evolutionary computation (EvoApplications) Held as Part of EvoStar Conference

作者： Alvarez, Alberto Font, Jose Malmo Univ Nordenskioldsgatan 1 S-21119 Malmo Sweden

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.

关键词： Procedural Content Generation Machine Learning Mixed-Initiative Co-Creativity evolutionary computation

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evolutionary Trajectory Optimization for Suborbital Spaceplane by Combining Direct and Inverse Trajectory Design Approach

Evolutionary Trajectory Optimization for Suborbital Spacepla...

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Asia-Pacific International Symposium on Aerospace Technology (APISAT)

作者： Murakami, Masaaki Fujikawa, Takahiro Yonemoto, Koichi Tokyo Univ Sci Chiba Japan

ISBN: (纸本)9789819740123;9789819740109;9789819740093

This paper proposes evolutionary trajectory optimization methodology for suborbital spaceplanes that combines direct and inverse trajectory design approach. Guidance of suborbital spaceplane has attracted attention as a method to generate and update new trajectories in flight, and trajectory optimization by evolutionary computation enables global search. The trajectory of a spaceplane is subject to several strict constraints including flight path, aerodynamic loads, and longitudinal trim, making it difficult to find feasible solutions. The proposed methodology focuses on the dynamics of the trajectory, and flight constraints are handled efficiently. In the region where the angle of attack constraint changes drastically from transonic to hypersonic due to longitudinal trim constraints, the time history control input is directly designed to obtain the trajectory. In the end of the flight after deceleration to subsonic speed, the inverse designmethod is applied to satisfy the path constraints. The proposed method was validated by simulation, and it was confirmed that a diversity of solutions that fully satisfy the constraints can be obtained with a relatively small number of generations and individuals.

关键词： Real-time trajectory generation evolutionary computation Quaternion Spaceplane

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Evolution of Probabilistic Consensus in Digital Organisms

Evolution of Probabilistic Consensus in Digital Organisms

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3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2009)

作者： Knoester, David B. McKinley, Philip K. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781424448906

The complexity of distributed computing systems and their increasing interaction with the physical world impose challenging requirements in terms of adaptation, robustness, and resilience to attack. Based on their reliance on heuristics, algorithms for consensus, where members of a group agree on a course of action, are particularly sensitive to these conditions. Given the ability of natural organisms to respond to adversity, many researchers have investigated biologically-inspired approaches to designing robust distributed systems. In this paper, we describe a study in the use of digital evolution, a type of artificial life system, to produce a distributed behavior for reaching consensus. The evolved algorithm employs a novel mechanism for probabilistically reaching consensus based on the frequency of messaging. Moreover, this design approach enables us to change parameters based on the specifics of the desired system, with evolution producing corresponding flavors of consensus algorithms. Our results demonstrate that artificial life systems can be used to discover solutions to engineering problems, and that experiments in artificial life can inspire new studies in distributed protocol development.

关键词： distributed algorithm consensus evolutionary computation digital evolution self-organization

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Game Theory and Biology Inspired Flight Control for Autonomous UAVs Operating in Contested Environments 40

Game Theory and Biology Inspired Flight Control for Autonomo...

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IEEE 40th Sarnoff Symposium

作者： Kusyk, Janusz Uyar, M. Umit Ma, Kelvin Budhu, Kevin Samoylov, Eltan Plishka, Joseph Bertoli, Giorgio Boksiner, Jeffrey Guha, Dilip K. CUNY NYC Coll Technol New York NY 11201 USA CUNY City Coll New York New York NY 10031 USA USA RDECOM CERDEC I2WD Aberdeen Proving Ground MD 21005 USA

ISBN: (纸本)9781728124872

A self-organizing swarm of autonomous unmanned areal vehicles (UAVs) can provide a quick response to cyber-attacks in austere civilian and military environments. However, if a UAV swarm relies on centralized control, synchronization of swarm members, pre-planned actions or rule-based systems, it may not provide adequate and timely response against cyber attacks. We introduce game theory (GT) and biology inspired flight control algorithms to be run by each autonomous UAV to detect, localize and counteract rogue electromagnetic signal emitters. Each UAV positions itself such that the swarm tracks mobile adversaries while maintaining uniform node distribution and connectivity of the mobile ad-hoc network (MANET). UAVs use only their respective local neighbor information to determine their individual actions. Simulation experiments in OPNET show that our algorithms can provide an adequate area coverage over mobile interference sources. Our solution can be employed for civilian and military applications that require agile responses in dynamic environments.

关键词： Autonomous UAV swarm MANET evolutionary computation game theory cybersecurity

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Associative Classification in Big Data through a G3P Approach 4

Associative Classification in Big Data through a G3P Approac...

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4th International Conference on Internet of Things, Big Data and Security (IoTBDS)

作者： Luna, J. M. Padillo, F. Ventura, S. Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain

ISBN: (纸本)9789897583698

The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building). This number of phases is a handicap when big datasets are analysed. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): 1) mining reliable association rules;2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on Apache Spark to take advantage of the distributed computing. The experimental analysis was performend on 40 well-known datasets and considering 13 algorithms taken from literature. A series of non-parametric tests has also been carried out to determine statistical differences. Results are quite promising in terms of reliability and efficiency on high-dimensional data.

关键词： Big Data Associative Classification evolutionary computation

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Evolving Robot Software and Hardware 15

Evolving Robot Software and Hardware

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IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

作者： Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450379625

This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research.

关键词： evolutionary computation evolutionary Robotics Learning

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On the Performance of Master-Slave Parallelization Methods for Multi-Objective evolutionary Algorithms

On the Performance of Master-Slave Parallelization Methods f...

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12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013

作者： Zavoianu, Alexandru-Ciprian Lughofer, Edwin Koppelstaetter, Werner Weidenholzer, Guenther Amrhein, Wolfgang Klement, Erich Peter Johannes Kepler Univ Linz Dept Knowledge Based Math Syst Fuzzy Log Lab Linz Hagenberg Linz Austria Johannes Kepler Univ Linz Inst Elect Drives & Power Elect Linz Austria Austrian Ctr Competence Mech Linz Austria Linz Ctr Mechatron Linz Austria

ISBN: (纸本)9783642386091

This paper is focused on a comparative analysis of the performance of two master-slave parallelization methods, the basic generational scheme and the steady-state asynchronous scheme. Both can be used to improve the convergence speed of multi-objective evolutionary algorithms (MOEAs) that rely on time-intensive fitness evaluation functions. The importance of this work stems from the fact that a correct choice for one or the other parallelization method can lead to considerable speed improvements with regards to the overall duration of the optimization. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which master-slave parallelization option is better when dealing with a time-constrained optimization process.

关键词： evolutionary computation multi-objective optimization performance comparison master-slave parallelization steady-state evolution

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