DOCSLIB.ORG

Creativity and Machine Learning: a Survey

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Creativity and Machine Learning: a Survey Creativity and Machine Learning: A Survey GIORGIO FRANCESCHELLI, Alma Mater Studiorum Università di Bologna, Italy MIRCO MUSOLESI, University College London, United Kingdom, The Alan Turing Institute, United Kingdom, and Alma Mater Studiorum Università di Bologna, Italy There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, machine learning techniques, including generative deep learning, and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field. 1 INTRODUCTION In 1842, Lady Lovelace, an English mathematician and writer recognized by many as the first computer programmer, wrote that the Analytical Engine - the digital programmable machine proposed by Charles Babbage [3] a hundred years before the Turing machine [114]- “has no pretensions to originate anything, since it can only do whatever we know how to order it to perform” [72]. This consideration, which Alan Turing referred to as “Lovelace’s objection” [115] was just the first fundamental meeting between computer science and creativity. In particular, the last thirty years of the Twentieth century have been marked by many attempts of building machines able to “originate something”. From the beginning of the Seventies of the past century with the AARON Project by Harold Cohen, a program designed to autonomously draw images provided by a domain knowledge and a knowledge of representational strategy [16] and the Computerized Haiku by Margaret Masterman1, we have witnessed several examples of applications of artificial intelligence to a variety of artistic fields. Examples include the storyteller TALE- SPIN [71], RACTER and its poems’ book [84], and MEXICA and its short narratives [82]. Applications were not limited to novels and paintings: BACON was used to simulate human thought processes and discover scientific laws61 [ ] and with the COPYCAT Project Douglas Hofstadter and Melanie Mitchell proposed a computer program to discover insightful analogies [46]. The themes have also been extensively examined by Douglas Hofstadter in the Pulitzer Prize Gödel, Escher, Bach: an Eternal Golden Braid [45], in which the author explains the idea of self-reference in the production of creative work and its implications for artificial intelligence2. In this context, we have witnessed the emergence of the Computational Creativity field [13]. We will adopt the definition by Colton and Wiggins [21], according to whom, Computational Creativity is the philosophy, science and engineering of behaviors that unbiased observers would deem to be creative. It is important to highlight the use of the terms “responsibilities” (so, not considering tools but really independent systems) and, of course, “unbiased observers” (so, without any kind of prejudice about what a machine can or cannot do; our hope is that the reader can be considered arXiv:2104.02726v2 [cs.LG] 20 Apr 2021 as an “unbiased observer”; if not, we will make, in any case, our best). Computational Creativity can also be defined as the study and support, through computational means and methods, of behaviors exhibited by natural and artificial systems, which would be deemed creative if exhibited by humans,as proposed by Wiggins [124]. In this work, the author studies the links between creativity models and search algorithms of AI from a theoretical perspective. The author shows that by understanding them in depth, it might be possible 1http://www.in-vacua.com/cgi-bin/haiku.pl 2Quite interestingly, after Hofstadter’s classic, many AI projects focused on Bach’s music, from Bach by Design, the first recording produced by the artificial composer developed by David Cope [22] to COCONET [48] and DeepBach [44]. 1 Franceschelli and Musolesi to design solutions for both exploratory and transformational creativity. The definition of exploratory creativity and transformational creativity is provided by Margaret Boden in [8], another seminal work about Computational Creativity. Boden identifies three forms of creativity: combinatorial, exploratory and transformational creativity. Combinatorial creativity is about making unfamiliar combinations of familiar ideas; the definition of the other two, instead, is based on conceptual spaces, i.e., structured styles of thought (any disciplined way of thinking that is familiar to a certain culture or peer group): exploratory creativity involves the exploration of the structured conceptual space, while transformational creativity involves changing the conceptual space in a way that new thoughts, previously inconceivable, become possible. This is not the only interesting definition Boden provides. In fact, Boden also states that “creativity isthe ability to generate ideas or artifacts that are new, surprising and valuable”, where three sorts of surprise are considered. The first is the one that goes against statistics; the second is the one when you had not realized that this particularidea was part of it; and the third is when the idea is (or, better, was) apparently impossible. We will consider this definition, that we will call Boden’s three criteria, as one of the cornerstones of the rest of the research. Finally, Boden provides one last definition, linked with the very beginning of this Introduction. In fact, Boden states that, when talking about Computational Creativity, it is possible to distinguish four different questions, called as Lovelace questions, because many people would respond to them by using the argument cited above. The first Lovelace question is whether computational ideas can help us understand how human creativity is possible; the second is whether computers could ever do things which at least appear to be creative; the third is whether a computer could ever appear to recognize creativity; and the fourth is whether computer themselves could ever really be creative (the originality of the products can be attributed only to the machine) [8]. The first one is studied in Boden’s work in detail; from now on, after discussing the scope of the survey, we will provide the reader with an overview of the techniques that can answer second question and, possibly, generate insights about how machine learning can be used to measure creativity, trying to answer the third question. Before starting the description of techniques for generation and evaluation, we need to explain why this survey is not about artificial intelligence in general, defined as the design and implementation of intelligent agents that receive perception information from the environment and take actions that affect that environment (according to the definition contained in [95], which is shared by many researchers in this field). The survey focuses instead on machine learning, which is a subset of artificial intelligence defined as the study of computer algorithms that improve automatically through experience [74]. In order to consider a machine as potentially creative, there is the need of assigning more and more responsibilities to the generative system [21], and machine learning seems more appropriate since it avoids the need of a human behind it to specify all the required knowledge [35]; as pointed out in [17], the use of evolutionary or machine learning methods makes the behavior of the software unpredictable for the programmer, who is, in a sense, left out from the generative process. The fact that artificial intelligence and machine learning will be the enabling technologies for supporting creativity has been apparent since Turing’s seminal work. We can find this intuition in particular in the section “Learning Machines” [115] in which Turing, trying to find a way to overcome Lovelace’s objection, suggested that probably the best possible way to simulate human mind and pass the Turing Test is not to produce a program, but instead to simulate a child’s mind and then make it grows by learning. The relevance of this kind of systems, and in particular of neural networks, in creativity study is well-explained also by Boden, who notices that connectionist ideas of neural networks can help answer the first Lovelace question and might answer (and maybe have already answered) the second question, also suggesting that, by using them, computers might recognize certain aspects of creativity (answering the third question) [8]. It is worth noting that, according to some, computers not only might do things which appear to be creative, but already are expressing some forms of creativity (see, for example Goodfellow’s 2 Creativity and Machine Learning: A Survey interview by [73], where he said that “if creativity means producing something new and beneficial, machine learning algorithms are already at that point”). Following these considerations, to the best of our knowledge, this survey is the first to explore if and how machine learning algorithms have been used as a basis for (computational) creativity in both the generation and evaluation paths. Instead, for a reader interested in a survey concerning AI and creativity in general, we recommend [93]; for a more technical view of generative deep learning and for a view of other related techniques (for example, style transfer or machine translation), we suggest [30]; finally, for a view of artistic AI works (also in human-computer co-creations [43]), [73] is a very comprehensive source of information. 2 GENERATIVE
Load more

Recommended publications
  • 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.
    [Show full text]
  • Combinational Creativity and Computational Creativity
    Combinational Creativity and Computational Creativity By Ji HAN Dyson School of Design Engineering Imperial College London A thesis submitted for the degree of Doctor of Philosophy April 2018 Originality Declaration The coding of the Combinator and the Retriever was accomplished in collaboration with Feng SHI, a PhD colleague in the Creativity Design and Innovation Lab at the Dyson School of Design Engineering, Imperial College London, and co-author with me of relevant publications. Except where otherwise stated, this thesis is the result of my own research. This research was conducted in the Dyson School of Design Engineering at Imperial College London, between October 2014 and December 2017. I certify that this thesis has not been submitted in whole or in parts as consideration for any other degree or qualification at this or any other institute of learning. Ji HAN April 2018 II Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commericial No Derivitives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the license terms of this work. Ji HAN April 2018 III List of Publications The following journal and conference papers were published during this PhD research. Journal Publications 1. Han J., Shi F., Chen L., Childs P. R. N., 2018. The Combinator – A computer-based tool for creative idea generation based on a simulation approach.
    [Show full text]
  • 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%.
    [Show full text]
  • ARCHITECTS of INTELLIGENCE for Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS of INTELLIGENCE
    MARTIN FORD ARCHITECTS OF INTELLIGENCE For Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS OF INTELLIGENCE THE TRUTH ABOUT AI FROM THE PEOPLE BUILDING IT MARTIN FORD ARCHITECTS OF INTELLIGENCE Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Acquisition Editors: Ben Renow-Clarke Project Editor: Radhika Atitkar Content Development Editor: Alex Sorrentino Proofreader: Safis Editing Presentation Designer: Sandip Tadge Cover Designer: Clare Bowyer Production Editor: Amit Ramadas Marketing Manager: Rajveer Samra Editorial Director: Dominic Shakeshaft First published: November 2018 Production reference: 2201118 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK ISBN 978-1-78913-151-2 www.packt.com Contents Introduction ........................................................................ 1 A Brief Introduction to the Vocabulary of Artificial Intelligence .......10 How AI Systems Learn ........................................................11 Yoshua Bengio .....................................................................17 Stuart J.
    [Show full text]
  • Computational Creativity
    Computational Creativity Hannu Toivonen University of Helsinki [email protected] www.cs.helsinki.fi/hannu.toivonen ECSS, Gothenburg, 9 Oct 2018 8.10.2018 1 The following video clips, pictures and audio files have been removed from this file to save space: – Video: Poemcatcher tests ”Brain Poetry” machine at Frankfurt Book Fair: https://www.youtube.com/watch?v=cNnbTQL8j B4 – Images produced by Deep Dream, see e.g. https://en.wikipedia.org/wiki/DeepDream – Audio clip: music produced by a programme by Turing and his colleagues www.helsinki.fi/yliopisto 9.10.2018 2 Remote Associates Test (RAT) – What word relates to all of these three words? – coin – silver coin – quick – quick silver – spoon – silver spoon – Measures the ability to discover relationships between remotely associated concepts – A (controversial) psychometrical test of creativity – Correlates with IQ and originality in brain storming www.helsinki.fi/yliopisto 8.10.2018 3 Modeling RATs computationally – A single RAT question is a quadruple – A probabilistic approach: find that maximizes – Maximize (cf. naïve Bayes) www.helsinki.fi/yliopisto 8.10.2018 4 Modeling RATs computationally – Learn word frequencies from a large corpus – Use Google 1 and 2-grams to estimate probabilities and – (Google n-grams: a large, publically available collection of word sequences and their probabilities) – A lot more could be done, but we want to keep things as simple as possible – Creative behavior without an explicit semantic resource (such as WordNet) www.helsinki.fi/yliopisto 8.10.2018
    [Show full text]
  • Much Has Been Written About the Turing Test in the Last Few Years, Some of It
    1 Much has been written about the Turing Test in the last few years, some of it preposterously off the mark. People typically mis-imagine the test by orders of magnitude. This essay is an antidote, a prosthesis for the imagination, showing how huge the task posed by the Turing Test is, and hence how unlikely it is that any computer will ever pass it. It does not go far enough in the imagination-enhancement department, however, and I have updated the essay with a new postscript. Can Machines Think?1 Can machines think? This has been a conundrum for philosophers for years, but in their fascination with the pure conceptual issues they have for the most part overlooked the real social importance of the answer. It is of more than academic importance that we learn to think clearly about the actual cognitive powers of computers, for they are now being introduced into a variety of sensitive social roles, where their powers will be put to the ultimate test: In a wide variety of areas, we are on the verge of making ourselves dependent upon their cognitive powers. The cost of overestimating them could be enormous. One of the principal inventors of the computer was the great 1 Originally appeared in Shafto, M., ed., How We Know (San Francisco: Harper & Row, 1985). 2 British mathematician Alan Turing. It was he who first figured out, in highly abstract terms, how to design a programmable computing device--what we not call a universal Turing machine. All programmable computers in use today are in essence Turing machines.
    [Show full text]
  • Race in Academia
    I, SOCIETY AI AND INEQUALITY: NEW DECADE, MORE CHALLENGES Mona Sloane, Ph.D. New York University [email protected] AI TECHNOLOGIES are becoming INTEGRAL to SOCIETY © DotEveryone SURVEILLANCE CAPITALISM TRADING in BEHAVIOURAL FUTURES © The Verge © The Innovation Scout TOTALIZING NARRATIVE of AI AI as a master technology = often either utopia or dystopia AI = human hubris? ”In 2014, the eminent scientists Stephen Hawking, Stuart Russell, Max Tegmark and Frank Wilczek wrote: ‘The potential benefits are huge; everything that civilisation has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools that AI may provide, but the eradication of war, disease, and poverty would be high on anyone's list. Success in creating AI would be the biggest event in human history’.” Artificial General Intelligence? CORRELATION CAUSATION Race Race Gender Class INTERSECTIONAL INEQUALITY ETHICS EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE ETHICS as POLICY APPROACH or as TECHNOLOGY APPROACH The ‘SOCIAL’ Problem ETHICS IS A SMOKESCREEN WHOSE HEAD – OR “INTELLIGENCE” – ARE TRYING TO MODEL, ANYWAY? THE FOUNDERS OF THE ”BIG 5 TECH COMPANIES”: AMAZON, FACEBOOK, ALPHABET, APPLE, MICROSOFT BIG TECH, BIG BUSINESS https://www.visualcapitalist.com/how-big-tech-makes-their-billions-2020/ 1956 DARTMOUTH CONFRENCE THE DIVERSITY ARGUMENT IS IMPORTANT. BUT IT IS INSUFFICIENT. EXTRACTION as a social super structure Prediction Dependency Extraction EXTRACTION Capitalism Racism White Supremacy Settler Colonialism Carcerality Heteronormativity … SCIENCE = ONTOLOGICAL AND EPISTEMOLOGICAL ENGINE Real world consequences of classification systems: racial classification in apartheid South Africa Ruha Benjamin, Alondra Nelson, Charlton McIlwain, Meredith Broussard, Andre Brock, Safiya Noble, Mutale Nkonde, Rediet Abebe, Timnit Gebru, Joy Buolamwini, Jessie Daniels, Sasha Costanza- Chock, Marie Hicks, Anna Lauren Hoffmann, Laura Forlano, Danya Glabau, Kadija Ferryman, Vincent Southerland – and more.
    [Show full text]
  • Turing Test Does Not Work in Theory but in Practice
    Int'l Conf. Artificial Intelligence | ICAI'15 | 433 Turing test does not work in theory but in practice Pertti Saariluoma1 and Matthias Rauterberg2 1Information Technology, University of Jyväskylä, Jyväskylä, Finland 2Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands Abstract - The Turing test is considered one of the most im- Essentially, the Turing test is an imitation game. Like any portant thought experiments in the history of AI. It is argued good experiment, it has two conditions. In the case of control that the test shows how people think like computers, but is this conditions, it is assumed that there is an opaque screen. On actually true? In this paper, we discuss an entirely new per- one side of the screen is an interrogator whose task is to ask spective. Scientific languages have their foundational limita- questions and assess the nature of the answers. On the other tions, for example, in their power of expression. It is thus pos- side, there are two people, A and B. The task of A and B is to sible to discuss the limitations of formal concepts and theory answer the questions, and the task of the interrogator is to languages. In order to represent real world phenomena in guess who has given the answer. In the case of experimental formal concepts, formal symbols must be given semantics and conditions, B is replaced by a machine (computer) and again, information contents; that is, they must be given an interpreta- the interrogator must decide whether it was the human or the tion. They key argument is that it is not possible to express machine who answered the questions.
    [Show full text]
  • The Time for a National Study on AI Is NOW
    The Time For a National Study On AI is NOW Subscribe Past Issues Translate RSS View this email in your browser "I think we should be very careful about artificial intelligence. If I were to guess at what our biggest existential threat is, it’s probably that. With artificial intelligence, we are summoning the demon." Elon Musk, CEO, Tesla Motors & SpaceX ARTIFICIAL INTELLIGENCE STUDY: AI TODAY, AI TOMORROW We are following up on our last message about a proposed National Study on Artificial Intelligence. We very much appreciate the responses and encouragement we have received, and welcome input and questions from any League and/or its members on this subject. http://mailchi.mp/35a449e1979f/the-time-for-a-national-study-on-ai-is-now?e=[UNIQID][12/20/2017 6:29:08 PM] The Time For a National Study On AI is NOW We believe that the pace of development of AI and the implications of its advancement make this an extremely timely subject. AI is becoming an important part of our society - from medical uses to transportation to banking to national defense. Putting off this study will put us behind in the progress being made in AI. We again request that your League consider supporting this study at your program planning meeting. The reason to propose this study now is because there are currently no positions that address many of the issues that fall under the umbrella of this relatively new and powerful technology. For several years now private groups and even countries (like Russia, China and Korea) have made it a priority to develop mechanical brains/computers that can function intelligently http://mailchi.mp/35a449e1979f/the-time-for-a-national-study-on-ai-is-now?e=[UNIQID][12/20/2017 6:29:08 PM] The Time For a National Study On AI is NOW and command robots or other machines to perform some task.
    [Show full text]
  • THE TURING TEST RELIES on a MISTAKE ABOUT the BRAIN For
    THE TURING TEST RELIES ON A MISTAKE ABOUT THE BRAIN K. L. KIRKPATRICK Abstract. There has been a long controversy about how to define and study intel- ligence in machines and whether machine intelligence is possible. In fact, both the Turing Test and the most important objection to it (called variously the Shannon- McCarthy, Blockhead, and Chinese Room arguments) are based on a mistake that Turing made about the brain in his 1948 paper \Intelligent Machinery," a paper that he never published but whose main assumption got embedded in his famous 1950 paper and the celebrated Imitation Game. In this paper I will show how the mistake is a false dichotomy and how it should be fixed, to provide a solid foundation for a new understanding of the brain and a new approach to artificial intelligence. In the process, I make an analogy between the brain and the ribosome, machines that translate information into action, and through this analogy I demonstrate how it is possible to go beyond the purely information-processing paradigm of computing and to derive meaning from syntax. For decades, the metaphor of the brain as an information processor has dominated both neuroscience and artificial intelligence research and allowed the fruitful appli- cations of Turing's computation theory and Shannon's information theory in both fields. But this metaphor may be leading us astray, because of its limitations and the deficiencies in our understanding of the brain and AI. In this paper I will present a new metaphor to consider, of the brain as both an information processor and a producer of physical effects.
    [Show full text]
  • Arxiv:1202.4545V2 [Physics.Hist-Ph] 23 Aug 2012
    The Relativity of Existence Stuart B. Heinrich [email protected] October 31, 2018 Abstract Despite the success of modern physics in formulating mathematical theories that can predict the outcome of experiments, we have made remarkably little progress towards answering the most fundamental question of: why is there a universe at all, as opposed to nothingness? In this paper, it is shown that this seemingly mind-boggling question has a simple logical answer if we accept that existence in the universe is nothing more than mathematical existence relative to the axioms of our universe. This premise is not baseless; it is shown here that there are indeed several independent strong logical arguments for why we should believe that mathematical existence is the only kind of existence. Moreover, it is shown that, under this premise, the answers to many other puzzling questions about our universe come almost immediately. Among these questions are: why is the universe apparently fine-tuned to be able to support life? Why are the laws of physics so elegant? Why do we have three dimensions of space and one of time, with approximate locality and causality at macroscopic scales? How can the universe be non-local and non-causal at the quantum scale? How can the laws of quantum mechanics rely on true randomness? 1 Introduction can seem astonishing that anything exists” [73, p.24]. Most physicists and cosmologists are equally perplexed. Over the course of modern history, we have seen advances in Richard Dawkins has called it a “searching question that biology, chemistry, physics and cosmology that have painted rightly calls for an explanatory answer” [26, p.155], and Sam an ever-clearer picture of how we came to exist in this uni- Harris says that “any intellectually honest person will admit verse.
    [Show full text]
  • Automated Antenna Design with Evolutionary Algorithms
    Automated Antenna Design with Evolutionary Algorithms Gregory S. Hornby∗ and Al Globus University of California Santa Cruz, Mailtop 269-3, NASA Ames Research Center, Moffett Field, CA Derek S. Linden JEM Engineering, 8683 Cherry Lane, Laurel, Maryland 20707 Jason D. Lohn NASA Ames Research Center, Mail Stop 269-1, Moffett Field, CA 94035 Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present automated antenna design and optimization methods based on evolutionary algorithms. We have evolved efficient antennas for a variety of aerospace applications and here we describe one proof-of-concept study and one project that produced fight antennas that flew on NASA's Space Technology 5 (ST5) mission. I. Introduction The current practice of designing and optimizing antennas by hand is limited in its ability to develop new and better antenna designs because it requires significant domain expertise and is both time and labor intensive. As an alternative, researchers have been investigating evolutionary antenna design and optimiza- tion since the early 1990s,1{3 and the field has grown in recent years as computer speed has increased and electromagnetics simulators have improved. This techniques is based on evolutionary algorithms (EAs), a family stochastic search methods, inspired by natural biological evolution, that operate on a population of potential solutions using the principle of survival of the fittest to produce better and better approximations to a solution.
    [Show full text]