Computational Creativity: Three Generations of Research and Beyond
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Metaheuristics1
METAHEURISTICS1 Kenneth Sörensen University of Antwerp, Belgium Fred Glover University of Colorado and OptTek Systems, Inc., USA 1 Definition A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, To appear). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. A problem-specific implementation of a heuristic optimization algorithm according to the guidelines expressed in a metaheuristic framework is also referred to as a metaheuristic. The term was coined by Glover (1986) and combines the Greek prefix meta- (metá, beyond in the sense of high-level) with heuristic (from the Greek heuriskein or euriskein, to search). Metaheuristic algorithms, i.e., optimization methods designed according to the strategies laid out in a metaheuristic framework, are — as the name suggests — always heuristic in nature. This fact distinguishes them from exact methods, that do come with a proof that the optimal solution will be found in a finite (although often prohibitively large) amount of time. Metaheuristics are therefore developed specifically to find a solution that is “good enough” in a computing time that is “small enough”. As a result, they are not subject to combinatorial explosion – the phenomenon where the computing time required to find the optimal solution of NP- hard problems increases as an exponential function of the problem size. Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed- integer optimization such as branch and bound and dynamic programming. -
University of Navarra Ecclesiastical Faculty of Philosophy
UNIVERSITY OF NAVARRA ECCLESIASTICAL FACULTY OF PHILOSOPHY Mark Telford Georges THE PROBLEM OF STORING COMMON SENSE IN ARTIFICIAL INTELLIGENCE Context in CYC Doctoral dissertation directed by Dr. Jaime Nubiola Pamplona, 2000 1 Table of Contents ABBREVIATIONS ............................................................................................................................. 4 INTRODUCTION............................................................................................................................... 5 CHAPTER I: A SHORT HISTORY OF ARTIFICIAL INTELLIGENCE .................................. 9 1.1. THE ORIGIN AND USE OF THE TERM “ARTIFICIAL INTELLIGENCE”.............................................. 9 1.1.1. Influences in AI................................................................................................................ 10 1.1.2. “Artificial Intelligence” in popular culture..................................................................... 11 1.1.3. “Artificial Intelligence” in Applied AI ............................................................................ 12 1.1.4. Human AI and alien AI....................................................................................................14 1.1.5. “Artificial Intelligence” in Cognitive Science................................................................. 16 1.2. TRENDS IN AI........................................................................................................................... 17 1.2.1. Classical AI .................................................................................................................... -
Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions
Genetic Programming: Theory, Implementation, and the Evolution of Unconstrained Solutions Alan Robinson Division III Thesis Committee: Lee Spector Hampshire College Jaime Davila May 2001 Mark Feinstein Contents Part I: Background 1 INTRODUCTION................................................................................................7 1.1 BACKGROUND – AUTOMATIC PROGRAMMING...................................................7 1.2 THIS PROJECT..................................................................................................8 1.3 SUMMARY OF CHAPTERS .................................................................................8 2 GENETIC PROGRAMMING REVIEW..........................................................11 2.1 WHAT IS GENETIC PROGRAMMING: A BRIEF OVERVIEW ...................................11 2.2 CONTEMPORARY GENETIC PROGRAMMING: IN DEPTH .....................................13 2.3 PREREQUISITE: A LANGUAGE AMENABLE TO (SEMI) RANDOM MODIFICATION ..13 2.4 STEPS SPECIFIC TO EACH PROBLEM.................................................................14 2.4.1 Create fitness function ..........................................................................14 2.4.2 Choose run parameters.........................................................................16 2.4.3 Select function / terminals.....................................................................17 2.5 THE GENETIC PROGRAMMING ALGORITHM IN ACTION .....................................18 2.5.1 Generate random population ................................................................18 -
AI, Robots, and Swarms: Issues, Questions, and Recommended Studies
AI, Robots, and Swarms Issues, Questions, and Recommended Studies Andrew Ilachinski January 2017 Approved for Public Release; Distribution Unlimited. This document contains the best opinion of CNA at the time of issue. It does not necessarily represent the opinion of the sponsor. Distribution Approved for Public Release; Distribution Unlimited. Specific authority: N00014-11-D-0323. Copies of this document can be obtained through the Defense Technical Information Center at www.dtic.mil or contact CNA Document Control and Distribution Section at 703-824-2123. Photography Credits: http://www.darpa.mil/DDM_Gallery/Small_Gremlins_Web.jpg; http://4810-presscdn-0-38.pagely.netdna-cdn.com/wp-content/uploads/2015/01/ Robotics.jpg; http://i.kinja-img.com/gawker-edia/image/upload/18kxb5jw3e01ujpg.jpg Approved by: January 2017 Dr. David A. Broyles Special Activities and Innovation Operations Evaluation Group Copyright © 2017 CNA Abstract The military is on the cusp of a major technological revolution, in which warfare is conducted by unmanned and increasingly autonomous weapon systems. However, unlike the last “sea change,” during the Cold War, when advanced technologies were developed primarily by the Department of Defense (DoD), the key technology enablers today are being developed mostly in the commercial world. This study looks at the state-of-the-art of AI, machine-learning, and robot technologies, and their potential future military implications for autonomous (and semi-autonomous) weapon systems. While no one can predict how AI will evolve or predict its impact on the development of military autonomous systems, it is possible to anticipate many of the conceptual, technical, and operational challenges that DoD will face as it increasingly turns to AI-based technologies. -
A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data
processes Article A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data Rami Al-Hajj 1,* , Ali Assi 2 , Mohamad Fouad 3 and Emad Mabrouk 1 1 College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; [email protected] 2 Independent Researcher, Senior IEEE Member, Montreal, QC H1X1M4, Canada; [email protected] 3 Department of Computer Engineering, University of Mansoura, Mansoura 35516, Egypt; [email protected] * Correspondence: [email protected] or [email protected] Abstract: The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP Citation: Al-Hajj, R.; Assi, A.; Fouad, in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the M.; Mabrouk, E. -
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%. -
Iaj 10-3 (2019)
Vol. 10 No. 3 2019 Arthur D. Simons Center for Interagency Cooperation, Fort Leavenworth, Kansas FEATURES | 1 About The Simons Center The Arthur D. Simons Center for Interagency Cooperation is a major program of the Command and General Staff College Foundation, Inc. The Simons Center is committed to the development of military leaders with interagency operational skills and an interagency body of knowledge that facilitates broader and more effective cooperation and policy implementation. About the CGSC Foundation The Command and General Staff College Foundation, Inc., was established on December 28, 2005 as a tax-exempt, non-profit educational foundation that provides resources and support to the U.S. Army Command and General Staff College in the development of tomorrow’s military leaders. The CGSC Foundation helps to advance the profession of military art and science by promoting the welfare and enhancing the prestigious educational programs of the CGSC. The CGSC Foundation supports the College’s many areas of focus by providing financial and research support for major programs such as the Simons Center, symposia, conferences, and lectures, as well as funding and organizing community outreach activities that help connect the American public to their Army. All Simons Center works are published by the “CGSC Foundation Press.” The CGSC Foundation is an equal opportunity provider. InterAgency Journal FEATURES Vol. 10, No. 3 (2019) 4 In the beginning... Special Report by Robert Ulin Arthur D. Simons Center for Interagency Cooperation 7 Military Neuro-Interventions: The Lewis and Clark Center Solving the Right Problems for Ethical Outcomes 100 Stimson Ave., Suite 1149 Shannon E. -
An Investigation of the Search Space of Lenat's AM Program Redacted for Privacy Abstract Approved: Thomas G
AN ABSTRACT OF THE THESIS OF Prafulla K. Mishra for the degree of Master of Sciencein Computer Science presented on March 10,1988. Title: An Investigation of the Search Space of Lenat's AM Program Redacted for Privacy Abstract approved:_ Thomas G. Dietterich. AM is a computer program written by Doug Lenat that discoverselementary mathematics starting from some initial knowledge of set theory. Thesuccess of this program is not clearly understood. This work is an attempt to explore the searchspace of AM in order to un- derstand the success and eventual failure of AM. We show thatoperators play an important role in AM. Operators of AMcan be divided into critical and unneces- sary. The critical operators can be further divided into implosive and explosive. Heuristics are needed only for the explosive operators. Most heuristics and operators in AM can be removed without significant ef- fects. Only three operators (Coalesce, Invert, Repeat) andone initial concept (Bag Union) are enough to discover most of the elementary math concepts that AM discovered. An Investigation of the Search Space of Lenat's AM Program By Prafulla K. Mishra A THESIS submitted to Oregon State University in partial fulfillment of the requirements for the degree of Master of Science Completed March 10, 1988 Commencement June 1988 APPROVED: Redacted for Privacy Professor of Computer Science in charge of major Redacted for Privacy Head of Department of Computer Science Redacted for Privacy Dean of GradtiaUe School Date thesis presented March 10, 1988 Typed by Prafulla K. Mishra for Prafulla K. Mishra ACKNOWLEDGEMENTS I am indebted to Tom Dietterich for his help, guidanceand encouragement throughout this work. -
A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle
entropy Article A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle Juan Romero 1,2,* , Antonino Santos 3 , Adrian Carballal 1,3 , Nereida Rodriguez-Fernandez 1,2 , Iria Santos 1,2 , Alvaro Torrente-Patiño 3 , Juan Tuñas 3 and Penousal Machado 4 1 CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain; [email protected] (A.C.); [email protected] (N.R.-F.); [email protected] (I.S.) 2 Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain 3 Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, Spain; [email protected] (A.S.); [email protected] (A.T.-P.); [email protected] (J.T.) 4 Centre for Informatics and Systems of the University of Coimbra (CISUC), DEI, University of Coimbra, 3030-790 Coimbra, Portugal; [email protected] * Correspondence: [email protected] Received: 26 November 2020; Accepted: 30 November 2020; Published: 30 November 2020 Abstract: RealTimeBattle is an environment in which robots controlled by programs fight each other. Programs control the simulated robots using low-level messages (e.g., turn radar, accelerate). Unlike other tools like Robocode, each of these robots can be developed using different programming languages. Our purpose is to generate, without human programming or other intervention, a robot that is highly competitive in RealTimeBattle. To that end, we implemented an Evolutionary Computation technique: Genetic Programming. -
Arxiv:1404.7828V4 [Cs.NE] 8 Oct 2014 to Those Who Contributed to the Present State of the Art
Deep Learning in Neural Networks: An Overview Technical Report IDSIA-03-14 / arXiv:1404.7828 v4 [cs.NE] (88 pages, 888 references) Jurgen¨ Schmidhuber The Swiss AI Lab IDSIA Istituto Dalle Molle di Studi sull’Intelligenza Artificiale University of Lugano & SUPSI Galleria 2, 6928 Manno-Lugano Switzerland 8 October 2014 Abstract In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distin- guished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit arXiv:1404.7828v4 [cs.NE] 8 Oct 2014 to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using “local search” to follow citations of citations backwards in time. -
Geometric Semantic Genetic Programming Algorithm and Slump Prediction
Geometric Semantic Genetic Programming Algorithm and Slump Prediction Juncai Xu1, Zhenzhong Shen1, Qingwen Ren1, Xin Xie2, and Zhengyu Yang2 1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China 2 Department of Electrical and Engineering, Northeastern University, Boston, MA 02115, USA ABSTRACT Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely obtain satisfactory results. Based on theory of nonlinear prediction method, we propose a recycled concrete slump prediction model based on geometric semantic genetic programming (GSGP) and combined it with recycled concrete features. Tests show that the model can accurately predict the recycled concrete slump by using the established prediction model to calculate the recycled concrete slump with different mixing ratios in practical projects and by comparing the predicted values with the experimental values. By comparing the model with several other nonlinear prediction models, we can conclude that GSGP has higher accuracy and reliability than conventional methods. Keywords: recycled concrete; geometric semantics; genetic programming; slump 1. Introduction The rapid development of the construction industry has resulted in a huge demand for concrete, which, in turn, caused overexploitation of natural sand and gravel as well as serious damage to the ecological environment. Such demand produces a large amount of waste concrete in construction, entailing high costs for dealing with these wastes 1-3. In recent years, various properties of recycled concrete were validated by researchers from all over the world to protect the environment and reduce processing costs. -
A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis
processes Article A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis José A. Castellanos-Garzón 1,2,*, Yeray Mezquita Martín 1, José Luis Jaimes Sánchez 3, Santiago Manuel López García 3 and Ernesto Costa 2 1 Department of Computer Science and Automatic, Faculty of Sciences, BISITE Research Group, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain; [email protected] 2 CISUC, Department of Computer Engineering, ECOS Research Group, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030-290 Coimbra, Portugal; [email protected] 3 Instituto Universitario de Estudios de la Ciencia y la Tecnología, University of Salamanca, 37008 Salamanca, Spain; [email protected] (J.L.J.S.); [email protected] (S.M.L.G.) * Correspondence: [email protected] Received: 31 October 2020; Accepted: 26 November 2020; Published: 27 November 2020 Abstract: This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality.