Automated Antenna Design with Evolutionary Algorithms
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Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space Matthew C. Fontaine Julian Togelius Viterbi School of Engineering Tandon School of Engineering University of Southern California New York University Los Angeles, CA New York City, NY [email protected] [email protected] Stefanos Nikolaidis Amy K. Hoover Viterbi School of Engineering Ying Wu College of Computing University of Southern California New Jersey Institute of Technology Los Angeles, CA Newark, NJ [email protected] [email protected] ABSTRACT We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diver- sity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse range of solutions, these algorithms require a large number of evaluations for exploration of continuous spaces. Meanwhile, variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are among the best-performing derivative-free optimizers in single- objective continuous domains. This paper proposes a new QD algo- rithm called Covariance Matrix Adaptation MAP-Elites (CMA-ME). Figure 1: Comparing Hearthstone Archives. Sample archives Our new algorithm combines the self-adaptation techniques of for both MAP-Elites and CMA-ME from the Hearthstone ex- CMA-ES with archiving and mapping techniques for maintaining periment. Our new method, CMA-ME, both fills more cells diversity in QD. Results from experiments based on standard con- in behavior space and finds higher quality policies to play tinuous optimization benchmarks show that CMA-ME finds better- Hearthstone than MAP-Elites. Each grid cell is an elite (high quality solutions than MAP-Elites; similarly, results on the strategic performing policy) and the intensity value represent the game Hearthstone show that CMA-ME finds both a higher overall win rate across 200 games against difficult opponents. -
Long Term Memory Assistance for Evolutionary Algorithms
mathematics Article Long Term Memory Assistance for Evolutionary Algorithms Matej Crepinšekˇ 1,* , Shih-Hsi Liu 2 , Marjan Mernik 1 and Miha Ravber 1 1 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; [email protected] (M.M.); [email protected] (M.R.) 2 Department of Computer Science, California State University Fresno, Fresno, CA 93740, USA; [email protected] * Correspondence: [email protected] Received: 7 September 2019; Accepted: 12 November 2019; Published: 18 November 2019 Abstract: Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50% more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F03 and F05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. -
A New Compact Wideband MIMO Antenna for Reverberation Chambers Master of Science Thesis
A New Compact Wideband MIMO Antenna for Reverberation Chambers Master of Science Thesis Ali Al-Rawi Department of Signals & Systems Antenna Group Chalmers University of Technology G¨oteborg, Sweden 2012 Master's Thesis 2012 Master of Science Thesis. Copyright c 2012 Ali Al-Rawi. Supervisors: Dr.Jian Yang.(SV1), Charlie Orlenius.(SV2), Magnus Franz´en.(SV2) Examiner: Prof. Dr. Per-Simon Kildal. Keywords: Antenna, MIMO, UWB, Reflection Coefficients, Mutual Couplings, Embed- ded Radiation efficiency,Genetic Algorithms Optimization, Reverberation Chamber. Series: ISSN 99-2747920-4; no EX065/2012. Antenna Group, Departments of Signals and Systems, Chalmers Tekniska H¨ogskola, SE-41296, G¨oteborg, Sweden. Telephone: +46 317721000 Press: Chalmers Reproservice, G¨oteborg-Sweden, Chalmers University of Technology, June 2012. Cover page: The manufactured four ports dual sided self grounded triangular antenna. Antenna Group, Chalmers University of Technology. Bluetest AB. Preface This is the technical report of the master degree project corresponding to the partial fulfillment of the master of science degree at Chalmers university of Technology. This project is the result of a one year research conducted at the department of Signals and Systems, Antenna Group, Chalmers University of Technology located in Sweden- Gothenburg. The project has been carried out by master student Ali Al-Rawi at Chalmers University of Technology. The project has been supervised by Dr.Jian Yang at Chalmers University of Technology as the first supervisor. The second supervisors are; Charlie Orlenius (CTO) and Magnus Franz´en(Manufacturing Director) both at Bluetest AB Sweden-Gothenburg. Prof.Dr.Per-Simon Kildal is the project examiner. This project has been financed by Bluetest AB (www.bluetest.se), and has been per- formed at Chalmers University of Technology. -
Table of Contents
SPECIAL ISSUE VOLUME 12 NUMBER- (6) NOVEMBER 2019 BBRC Print ISSN: 0974-6455 Bioscience Biotechnology Online ISSN: 2321-4007 Research Communications CODEN BBRCBA www.bbrc.in University Grants Commission (UGC) New Delhi, India Approved Journal Biosc Biotech Res Comm Special Issue Vol 12 No(6) November 2019 Recent Trends in Computing and Communication Technology Published By: Society For Science and Nature Bhopal, Post Box 78, GPO, 462001 India Indexed by Thomson Reuters, Now Clarivate Analytics USA ISI ESCI SJIF 2019=4.186 Online Content Available: Every 3 Months at www.bbrc.in Registered with the Registrar of Newspapers for India under Reg. No. 498/2007 Bioscience Biotechnology Research Communications SPECIAL ISSUE VOL 12 NO (6) NOV 2019 Framework for the Merging of Thermal and Visible Images in Real Time Recognition Applications 01-04 Dr. M. Kavitha and Dr. A. Kavitha Optimization Using Modified Grey Wolf Algorithm for Twitter Sentiment Analysis on Demonetization 05-13 Dr. M. Malathy, Dr. M.Sujatha and B. Manikandan Cloud Health Care Authentication Using Enhanced Merkle Hash Tree Approach with Threshold RSA 14-25 Dr. T. Shankar and Dr. G. Yamuna Pentagonal Ring Slot Antenna with SRR for Tri-Band Application 26-30 S. Prasad Jones Christydass and A. Manjunathan A Study on Information Sharing Framework for Industrial Internet of Things 31-36 Dr.D.Radha and Dr.M.G.Kavitha Load Management Strategy for Residential Microgrid Application with CCCV Charging of Storage Battery 37-44 Akash Sharma, Vedatroyee Ghosh, Dr.K. Ravi and Dr.Almoataz Y. Abdelaziz Review on 5G Healthcare Using IoT Based Sensor Devices 45-51 Dr. -
Automated Synthesis of a Fixed-Length Loaded Symmetric Dipole Antenna Whose Gain Exceeds That of a Commercial Antenna and Matches the Theoretical Maximum
Automated Synthesis of a Fixed-Length Loaded Symmetric Dipole Antenna Whose Gain Exceeds That of a Commercial Antenna and Matches the Theoretical Maximum John R. Koza Sameer H. Al-Sakran Stanford University Genetic Programming Inc. Stanford, California 94305 990 Villa Street PHONE: 650-960-8180 Mountain View, California 94041 [email protected] sameer@genetic- programming.com Lee W. Jones Greg Manassero Genetic Programming Inc. Electrical Engineering Consultant 990 Villa Street San Jose, California Mountain View, California 94041 [email protected] lee@genetic- programming.com ABSTRACT 1 INTRODUCTION This paper describes the use of genetic programming to An antenna is a device for receiving or transmitting automatically synthesize the design for a fixed-length loaded electromagnetic waves. An antenna may receive an symmetric dipole antenna whose gain at a specific wavelength electromagnetic wave and transform it into an electrical signal exceeds that of a commercially-marketed human-designed on a transmission line. Alternately, an antenna may transform a antenna and that reaches the theoretical maximum value for an signal from a transmission line into an electromagnetic wave antenna of its type. The run of genetic programming started that is then propagated in space. “from scratch”—that is, without starting from a pre-existing Maxwell’s equations describe the electromagnetic waves human-created design; did not employ any knowledge base of generated and received by antennas and the electrical currents in human design techniques or principles from the field of antenna the antenna. The task of synthesizing the design of an antenna design; and did not benefit from any human intervention during with specified behavior and characteristics is difficult. -
Evolutionary Algorithms
Evolutionary Algorithms Dr. Sascha Lange AG Maschinelles Lernen und Naturlichsprachliche¨ Systeme Albert-Ludwigs-Universit¨at Freiburg [email protected] Dr. Sascha Lange Machine Learning Lab, University of Freiburg Evolutionary Algorithms (1) Acknowlegements and Further Reading These slides are mainly based on the following three sources: I A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, corrected reprint, Springer, 2007 — recommendable, easy to read but somewhat lengthy I B. Hammer, Softcomputing,LectureNotes,UniversityofOsnabruck,¨ 2003 — shorter, more research oriented overview I T. Mitchell, Machine Learning, McGraw Hill, 1997 — very condensed introduction with only a few selected topics Further sources include several research papers (a few important and / or interesting are explicitly cited in the slides) and own experiences with the methods described in these slides. Dr. Sascha Lange Machine Learning Lab, University of Freiburg Evolutionary Algorithms (2) ‘Evolutionary Algorithms’ (EA) constitute a collection of methods that originally have been developed to solve combinatorial optimization problems. They adapt Darwinian principles to automated problem solving. Nowadays, Evolutionary Algorithms is a subset of Evolutionary Computation that itself is a subfield of Artificial Intelligence / Computational Intelligence. Evolutionary Algorithms are those metaheuristic optimization algorithms from Evolutionary Computation that are population-based and are inspired by natural evolution.Typicalingredientsare: I A population (set) of individuals (the candidate solutions) I Aproblem-specificfitness (objective function to be optimized) I Mechanisms for selection, recombination and mutation (search strategy) There is an ongoing controversy whether or not EA can be considered a machine learning technique. They have been deemed as ‘uninformed search’ and failing in the sense of learning from experience (‘never make an error twice’). -
A Small Patch Antenna with Ellipse Disc Loaded/Cut by Using Differential Evolution
Advances in Intelligent Systems Research, volume 154 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017) A Small Patch Antenna with Ellipse Disc Loaded/cut by Using Differential Evolution Qing Zhang 1, a, Sanyou Zeng 2,b 1 Huanggang Normal University, Huangzhou, Hubei, China 2 China University of Geosciences, Wuhan, Hubei, China [email protected], [email protected] Keywords: Multisim, computer hardware, virtual simulation Abstract. Computer hardware course is abstract, Multisim simulation software will be integrated into the computer hardware course teaching, promote the teaching to the development of information technology through the simulation play the role of technology, brings to the teachers and students professional and comprehensive virtual experiment environment. This paper first introduces the basic situation of Multisim software platform, combined with the practice of the current computer hardware curriculum system of computer composition principle of this course, analyzes the composition of the functional units of the computer virtual simulation design and implementation method, discusses the important role of the virtual simulation technology platform, a comprehensive system of value of the virtual simulation the hardware based on Multisim. Introduction Evolutionary antenna design has been investigated by researchers since the early 1990s, e.g., [1], [2], [3]. And it is still a hot topic up to date. For example, [4], [5], etc. designed wire antennas; [6], [7], [8], etc. designed planar antennas; [10], [11], [9], [12], [13], etc. designed antenna arrays. Once a new evolutionary algorithm is proposed, it is usually applied to design antenna soon. Particle swarm optimization (PSO) [14] is used to design antennas, e.g., [15], [16], [17]. -
Implementation of Microstrip Patch Antenna for Wi-Fi Applications
American Journal of Computer Science and Technology 2018; 1(3): 63-73 http://www.sciencepublishinggroup.com/j/ajcst doi: 10.11648/j.ajcst.20180103.12 ISSN: 2640-0111 (Print); ISSN: 2640-012X (Online) Implementation of Microstrip Patch Antenna for Wi-Fi Applications Swe Zin Nyunt Department of Electronic Engineering, Yangon Technological University, Yangon, Republic of the Union of Myanmar Email address: To cite this article: Swe Zin Nyunt. Implementation of Microstrip Patch Antenna for Wi-Fi Applications. American Journal of Computer Science and Technology . Vol. 1, No. 3, 2018, pp. 63-73. doi: 10.11648/j.ajcst.20180103.12 Received : November 3, 2018; Accepted : November 16, 2018; Published : December 26, 2018 Abstract: In recent years, the inventions in communication systems require the design of low cost, minimal weight, compact and low profile antennas which are capable of main-taining high performance. This research covers the study of basics and fundamentals of the microstrip patch antenna. The aim of this work is to design the microstrip patch antenna for Wi-Fi applications which operates at 2.4 GHz. The simulation of the proposed antenna was done with the aid of the computer simulation technology (CST) microwave studio student version 2017. The substrate used for the proposed antenna is the flame resistant four (FR-4) with a dielectric constant of 4.4 and a loss tangent of 0.025. The proposed MSA is fed by the coaxial probe. The proposed antenna may find applications in wireless local area network (Wi-Fi) and Bluetooth technology. And the work is the design of a Hexagonal shaped microstrip patch antenna which is presented for the wireless communication applications such as Wi-Fi in S-band. -
Evolutionary Algorithms in Intelligent Systems
mathematics Editorial Evolutionary Algorithms in Intelligent Systems Alfredo Milani Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy; [email protected] Received: 17 August 2020; Accepted: 29 August 2020; Published: 10 October 2020 Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally difficult optimization problems. Successful early applications of the evolutionary computational approach can be found in the field of numerical optimization, while they have now become pervasive in applications for planning, scheduling, transportation and logistics, vehicle routing, packing problems, etc. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, as components of intelligent systems for supporting tasks and decisions in the fields of machine vision, natural language processing, parameter optimization for neural networks, and feature selection in machine learning systems. Moreover, they are also applied in areas like complex network dynamics, evolution and trend detection in social networks, emergent behavior in multi-agent systems, and adaptive evolutionary user interfaces to mention a few. In these systems, the evolutionary components are integrated into the overall architecture and they provide services to the specific algorithmic solutions. This paper selection aims to provide a broad view of the role of evolutionary algorithms and metaheuristics in artificial intelligent systems. A first relevant issue discussed in the volume is the role of multi-objective meta-optimization of evolutionary algorithms (EA) in continuous domains. The challenging tasks of EA parameter tuning are the many different details that affect EA performance, such as the properties of the fitness function as well as time and computational constraints. -
A New Evolutionary System for Evolving Artificial Neural Networks
694 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 3, MAY 1997 A New Evolutionary System for Evolving Artificial Neural Networks Xin Yao, Senior Member, IEEE, and Yong Liu Abstract—This paper presents a new evolutionary system, i.e., constructive and pruning algorithms [5]–[9]. Roughly speak- EPNet, for evolving artificial neural networks (ANN’s). The evo- ing, a constructive algorithm starts with a minimal network lutionary algorithm used in EPNet is based on Fogel’s evolution- (i.e., a network with a minimal number of hidden layers, nodes, ary programming (EP). Unlike most previous studies on evolving ANN’s, this paper puts its emphasis on evolving ANN’s behaviors. and connections) and adds new layers, nodes, and connections This is one of the primary reasons why EP is adopted. Five if necessary during training, while a pruning algorithm does mutation operators proposed in EPNet reflect such an emphasis the opposite, i.e., deletes unnecessary layers, nodes, and con- on evolving behaviors. Close behavioral links between parents nections during training. However, as indicated by Angeline et and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN’s archi- al. [10], “Such structural hill climbing methods are susceptible tectures and connection weights (including biases) simultaneously to becoming trapped at structural local optima.” In addition, in order to reduce the noise in fitness evaluation. The parsimony they “only investigate restricted topological subsets rather than of evolved ANN’s is encouraged by preferring node/connection the complete class of network architectures.” deletion to addition. EPNet has been tested on a number of Design of a near optimal ANN architecture can be for- benchmark problems in machine learning and ANN’s, such as the parity problem, the medical diagnosis problems (breast mulated as a search problem in the architecture space where cancer, diabetes, heart disease, and thyroid), the Australian credit each point represents an architecture. -
Neuroevolution Strategies for Episodic Reinforcement Learning ∗ Verena Heidrich-Meisner , Christian Igel
J. Algorithms 64 (2009) 152–168 Contents lists available at ScienceDirect Journal of Algorithms Cognition, Informatics and Logic www.elsevier.com/locate/jalgor Neuroevolution strategies for episodic reinforcement learning ∗ Verena Heidrich-Meisner , Christian Igel Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany article info abstract Article history: Because of their convincing performance, there is a growing interest in using evolutionary Received 30 April 2009 algorithms for reinforcement learning. We propose learning of neural network policies Available online 8 May 2009 by the covariance matrix adaptation evolution strategy (CMA-ES), a randomized variable- metric search algorithm for continuous optimization. We argue that this approach, Keywords: which we refer to as CMA Neuroevolution Strategy (CMA-NeuroES), is ideally suited for Reinforcement learning Evolution strategy reinforcement learning, in particular because it is based on ranking policies (and therefore Covariance matrix adaptation robust against noise), efficiently detects correlations between parameters, and infers a Partially observable Markov decision process search direction from scalar reinforcement signals. We evaluate the CMA-NeuroES on Direct policy search five different (Markovian and non-Markovian) variants of the common pole balancing problem. The results are compared to those described in a recent study covering several RL algorithms, and the CMA-NeuroES shows the overall best performance. © 2009 Elsevier Inc. All rights reserved. 1. Introduction Neuroevolution denotes the application of evolutionary algorithms to the design and learning of neural networks [54,11]. Accordingly, the term Neuroevolution Strategies (NeuroESs) refers to evolution strategies applied to neural networks. Evolution strategies are a major branch of evolutionary algorithms [35,40,8,2,7]. -
A Hybrid Neural Network and Genetic Programming Approach to the Automatic Construction of Computer Vision Systems
AHYBRID NEURAL NETWORK AND GENETIC PROGRAMMING APPROACH TO THE AUTOMATIC CONSTRUCTION OF COMPUTER VISION SYSTEMS Cameron P.Kyle-Davidson A thesis submitted for the degree of Master of Science by Dissertation School of Computer Science and Electronic Engineering University of Essex October 2018 ii Abstract Both genetic programming and neural networks are machine learning techniques that have had a wide range of success in the world of computer vision. Recently, neural networks have been able to achieve excellent results on problems that even just ten years ago would have been considered intractable, especially in the area of image classification. Additionally, genetic programming has been shown capable of evolving computer vision programs that are capable of classifying objects in images using conventional computer vision operators. While genetic algorithms have been used to evolve neural network structures and tune the hyperparameters of said networks, this thesis explores an alternative combination of these two techniques. The author asks if integrating trained neural networks with genetic pro- gramming, by framing said networks as components for a computer vision system evolver, would increase the final classification accuracy of the evolved classifier. The author also asks that if so, can such a system learn to assemble multiple simple neural networks to solve a complex problem. No claims are made to having discovered a new state of the art method for classification. Instead, the main focus of this research was to learn if it is possible to combine these two techniques in this manner. The results presented from this research in- dicate that such a combination does improve accuracy compared to a vision system evolved without the use of these networks.