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Distributing evolutionary computation in a networked multi-agent environment

MATHEMATICAL AND COMPUTER MODELLING 2011年第5-6期53卷 1167-1181页

作者： Lee, Wei-Po Ke, Hong-Yi Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung 80424 Taiwan

It has become increasingly popular to employ evolutionary algorithms to solve problems in different domains, and parallel models have been widely used for performance enhancement. Instead of using parallel computing facilities or public computing systems to speed up the computation, we propose to implement parallel evolutionary computation models on networked personal computers (PCs) that are locally available and manageable. To realize the parallelism, a multi-agent system is presented in which mobile agents play the major roles to carry the code and move from machine to machine to complete the computation dynamically. To evaluate the proposed approach, we use our multi-agent system to solve two types of time-consuming applications. Different kinds of experiments were conducted to assess the developed system, and the preliminary results show its promise and efficiency. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Mobile agent evolutionary algorithm Distributed computing Autonomic computing Gene regulatory network evolutionary robotics

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Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents

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MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 2012年第1期18卷 101-129页

作者： Bredeche, Nicolas Montanier, Jean-Marc Liu, Wenguo Winfield, Alan F. T. TAO Univ Paris Sud INRIA CNRS F-91405 Orsay France Univ W England Bristol Robot Lab Bristol BS16 1QY Avon England

This article is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the implicit fitness function hidden in the environment so as to provide adaptation in the long run at the level of population. The proposed algorithm, termed MEDEA, is shown to be both efficient in unknown environments and robust to abrupt and unpredicted changes in the environment. The emergence of consensus towards specific behavioural strategies is examined, with a particular focus on algorithmic stability. Finally, a real-world implementation of the algorithm is described with a population of 20 real-world e-puck robots.

关键词： evolutionary robotics artificial life open-ended evolution swarm intelligence

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evolutionary design of soft-bodied animats with decentralized control

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ARTIFICIAL LIFE AND robotics 2013年第3-4期18卷 152-160页

作者： Joachimczak, Michal Kowaliw, Taras Doursat, Rene Wrobel, Borys Nagoya Univ Grad Sch Informat Sci Nagoya Aichi Japan Polish Acad Sci Syst Modeling Lab Inst Oceanol Sopot Poland CNRS ISC PIF Paris France Drexel Univ Sch Biomed Engn Philadelphia PA 19104 USA Ecole Polytech Erasmus Mundus Masters Complex Syst Sci Paris France Adam Mickiewicz Univ Evolutionary Syst Lab Poznan Poland

We show how a biologically inspired model of multicellular development combined with a simulated evolutionary process can be used to design the morphologies and controllers of soft-bodied virtual animats. An animat's morphology is the result of a developmental process that starts from a single cell and goes through many cell divisions, during which cells interact via simple physical rules. Every cell contains the same genome, which encodes a gene regulatory network (GRN) controlling its behavior. After the developmental stage, locomotion emerges from the coordinated activity of the GRNs across the virtual robot body. Since cells act autonomously, the behavior of the animat is generated in a truly decentralized fashion. The movement of the animat is produced by the contraction and expansion of parts of the body, caused by the cells, and is simulated using a physics engine. Our system makes possible the evolution and development of animats that can run, swim, and actively navigate toward a target in a virtual environment.

关键词： Artificial development evolutionary robotics Virtual animats Body-brain coevolution

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evolutionary controller design for area search using multiple UAVs with minimum altitude maneuver

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2013年第2期27卷 541-548页

作者： Oh, Soo-Hun Suk, Jinyoung Korea Aerosp Res Inst Unmanned Aircraft Syst Div Taejon 305806 South Korea Chungnam Natl Univ Dept Aerosp Engn Taejon 305764 South Korea

The efficient accomplishment of missions can often be enhanced by the simultaneous operation of multiple unmanned aerial vehicles (UAVs). Easily scalable control algorithms are crucial for the implementation of such operational concept. One promising practical option is swarm intelligence, which is based on a behavioral model and boasts of characteristics such as flexibility, robustness, decentralized control, and self-organization. In this paper, a neural net controller that evolved via evolutionary robotics is applied to the control of multiple UAVs with the mission to search a bound area as thoroughly as possible. By applying incremental evolution techniques, a neural net controller that minimizes energy consumption without sacrificing performance in area coverage and collision avoidance can be developed. A much higher survival rate of UAVs can be achieved by applying a three-dimensional (3-D) maneuver for collision avoidance with an efficient algorithm for minimizing fuel consumption by suppressing excessive altitude maneuver. Numerical demonstrations are shown to validate the effectiveness of the proposed 3-D area search algorithm with minimum altitude maneuver.

关键词： Behavioral model evolutionary robotics Genetic algorithm Multiple unmanned aerial vehicles Swarm intelligence

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From wheels to wings with evolutionary spiking circuits

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ARTIFICIAL LIFE 2005年第1-2期11卷 121-138页

作者： Floreano, D Zufferey, JC Nicoud, JD Swiss Fed Inst Technol EPFL Inst Syst Engn Autonomous Syst Lab CH-1015 Lausanne Switzerland DIDEL SA CH-1092 Belmont Lausanne Switzerland

We give an overview of the EPFL indoor flying project, whose goal is to evolve neural controllers for autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and control of nonlinear flight dynamics. This ongoing project consists of developing a flying vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital microcontrollers, and a method to evolve such a neural controller without human intervention. This article describes the motivation and methodology used to reach our goal as well as the results of a number of preliminary experiments on vision-based wheeled and flying, robots.

关键词： evolutionary robotics spiking neurons flying robots micro-robots vision

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evolutionary behavior learning for action-based environment modeling by a mobile robot

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APPLIED SOFT COMPUTING 2005年第2期5卷 245-257页

作者： Yamada, S Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

This paper describes an evolutionary way to acquire behaviors of a mobile robot for recognizing environments. We have proposed Action-based Environment Modeling (AEM) approach for a simple mobile robot to recognize environments. In AEM, a behavior-based mobile robot acts in each environment and action sequences are obtained. The action sequences are transformed into vectors characterizing the environments, and the robot identifies the environments with similarity between the vectors. The suitable behaviors like wall-following for AEM have been designed by a human. However the design is very difficult for him/her because the search space is huge and intuitive understanding is hard. Thus we apply evolutionary robotics approach to design of such behaviors using genetic algorithm and make simulations in which a robot recognizes the environments with different structures. As results, we find out suitable behaviors are learned even for environments in which human hardly designs them, and the learned behaviors are more efficient than hand-coded ones. (C) 2004 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics action-based environment modeling a mobile robot genetic algorithm

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evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2018年第5期22卷 621-628页

作者： Hiraga, Motoaki Yasuda, Toshiyuki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan Univ Toyama Grad Sch Sci & Engn 3190 Gofuku Toyama 9308555 Japan

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

关键词： evolutionary robotics task allocation division of labor task specialization

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Exploratory analysis of an on-line evolutionary algorithm in simulated robots

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evolutionary INTELLIGENCE 2012年第4期5卷 213-230页

作者： Haasdijk, Evert Smit, S. K. Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands TNO The Hague Netherlands

In traditional evolutionary robotics, robot controllers are evolved in a separate design phase preceding actual deployment;we call this off-line evolution. Alternatively, robot controllers can evolve while the robots perform their proper tasks, during the actual operational phase;we call this on-line evolution. In this paper we describe three principal categories of on-line evolution for developing robot controllers (encapsulated, distributed, and hybrid), present an evolutionary algorithm belonging to the first category (the (mu + 1) ON-LINE algorithm), and perform an extensive study of its behaviour. In particular, we use the Bonesa parameter tuning method to explore its parameter space. This delivers near-optimal settings for our algorithm in a number of tasks and, even more importantly, it offers profound insights into the impact of our algorithm's parameters and features. Our experimental analysis of (mu + 1) ON-LINE shows that it seems preferable to try many alternative solutions and spend little effort on refining possibly faulty assessments;that there is no single combination of parameters that performs well on all problem instances and that the most influential parameter of this algorithm-and therefore the prime candidate for a control scheme-is the evaluation length tau.

关键词： evolutionary robotics On-lineevolution Scientific testing Parameter tuning Bonesa

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evolutionary body building: Adaptive physical designs for robots

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ARTIFICIAL LIFE 1998年第4期4卷 337-357页

作者： Funes, P Pollack, J Brandeis Univ Volen Ctr Complex Syst Dept Comp Sci Waltham MA 02454 USA

Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of coevolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities that are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in addressing the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components that stick together. Evolution takes place in a simulator that computes forces and stresses and predicts stability of three-dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.

关键词： evolutionary robotics body and brain coevolution adaptive bodies genetic algorithms evolutionary design evolvable structures

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evolutionary active vision system: from 2D to 3D

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ADAPTIVE BEHAVIOR 2021年第1期29卷 3-24页

作者： Lanihun, Olalekan Tiddernan, Bernie Shaw, Patricia Tuci, Elio Abeystwyth Univ Abeystwyth SY23 3DB Wales Abeystwyth Univ Dept Comp Sci Abeystwyth Wales Abeystwyth Univ Comp Sci Abeystwyth Wales Univ Namur Fac Comp Sci Namur Belgium

Biological vision incorporates intelligent cooperation between the sensory and the motor systems, which is facilitated by the development of motor skills that help to shape visual information that is relevant to a specific vision task. In this article, we seek to explore an approach to active vision inspired by biological systems, which uses limited constraints for motor strategies through progressive adaptation via an evolutionary method. This type of approach gives freedom to artificial systems in the discovery of eye-movement strategies that may be useful to solve a given vision task but are not known to us. In the experiment sections of this article, we use this type of evolutionary active vision system for more complex natural images in both two-dimensional (2D) and three-dimensional (3D) environments. To further improve the results, we experiment with the use of pre-processing the visual input with both the uniform local binary patterns (ULBP) and the histogram of oriented gradients (HOG) for classification tasks in the 2D and 3D environments. The 3D experiments include application of the active vision system to object categorisation and indoor versus outdoor environment classification. Our experiments are conducted on the iCub humanoid robot simulator platform.

关键词： Active vision system neural network evolutionary robotics uniform local binary patterns histogram of oriented gradients humanoid robot

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