10. Lecture Stochastic Optimization
|
|
- Melissa Mitchell
- 6 years ago
- Views:
Transcription
1 Soft Control (AT 3, RMA) 10. Lecture Stochastic Optimization Genetic Algorithms
2 10. Structure of the lecture 1. Soft control: the definition and limitations, basics of epert" systems 2. Knowledge representation and knowledge processing (Symbolic AI) application: epert systems 3. Fuzzy Systems: Dealing with Fuzzy knowledge application: Fuzzy Control 4. Connective systems: neural networks application: Identification and neural controller 5. Genetic Algorithms: Stochastic Optimization Genetic Algorithms Simulated Annealing Differential Evolution Application: Optimization 6. Summary and Literarture reference 271
3 Contents of 10 th Lecture Classification in the lecture Conjunction with the other methods Overview of Evolutionary Algorithms The basic idea of genetic algorithms Idea Properties Genetic algorithms in detail Development Elements Sample Applications in automation technology Genetic Programming Summary 272
4 Classification in the lecture Natural development Looking at the considered systems in the past lectures we can say that we have had intelligent top-down views : Technical Development and procedures in the lecture Epert systems (abstract mathematical thinking) are a further development of Fuzzy-Systems ( natural Fuzzy-Close) This could only be developed on the basis of the neural structure of the brain Neural Networks (Learning and adaptation) Originated from the course of evolution of simpler structures Genetic Algorithms ( survival of the fittest ) 273
5 Overview of evolutionary algorithms Evolutionäre Algorithmen Genetische Programmierung Genetische Algorithmen Evolutionsstrategien Typical features of the different algorithms: Representation of individuals Operators used Size of individuals Selection Mechanism Genetic Algorithms Bit-String Recombination*, Mutation Genetic Synta trees Recombination*, Programming Mutation Evolutions Floating point vector Mutation*, strategies Recombination Operators marked with an * play the biggest role Constant Variable Constant Probabilistic Probabilistic Deterministic 274
6 The basic idea of Genetic algorithms Genetic algorithms are numerical optimization algorithms on the basis of two concepts of nature : Genetic Natural selection Initial ideas in 1950, a breakthrough in the 1960s with John Holland Basic concepts of GA There are a large number population of possible solutions to a problem There is a method to determine how well or bad a solution is There is a recombination method, the elements of the good solutions connects to generate new or better solutions There is a mutation operator, to prevent the permanent loss of diversity within the solutions 275
7 Properties of GA Analogous to the evolution theory in biology Evolution is a successful, robust method for adaptation of biological systems GA can search premises of hypotheses The comple, interacting elements where the influence of each part on the overall hypothesis is unclear GA can be easily parallelized GA are not deterministic GA does not optimize a single individual, but always a whole population. It is possible to find several local optima and finish with the selection of global optimum 276
8 Cycle of GA 1. Define the coding 2. Defining a fitness function 3. Initialization of a population 4. Calculation of fitness for the population 5. Selection of elements for the recombination 6. Recombination 7. Mutation 8. Composition of the new generation of 1. The Offspring 2. Elements of the parent generation(not always Elitism) 9. Net up-to 4 to a termination criterion is reached 277
9 Coding Most will use a binary encoding Bit strings are easy to manipulate (simple implementation of the genetic operators) If there are problems in several variables, the bit strings hanged together One speaks in analogy to the biology of genotype and phenotype 278
10 Fitness function Describes the goodness of a solution Fitness function should differ well between individuals, otherwise the only possibility of the search more or less randomly and converges to bad genetic algorithm It would be desirable fitness function that individuals with significantly similar characteristics also have similar fitness levels Should be easy to calculate, since they very often applied 279
11 Selection methods Monte-Carlo (Roulette-Wheel-Selection) Each individual will be allocated sector of Roulettrads proportional to fitness Selection Ranking-Selection Selection on the basis of seniority (the fitness value) within the population Tournament-Selection Random choice of 2 (or more) individuals, with predefined takeover of better likely 280
12 Roulette-Wheel-Selection Build the sum of the individuals of all fitness levels F sum Generate a random number R between 0 and F sum Add the fitness values of individuals one by one until the sum eceeds the value of R Select the most recently added individual 281
13 Rank based Selection N individuals in the population will be sorted in accordance with their fitness levels The best individual receives N Score, the net N-1, the worst 1 rating point With those rating points instead of the actual fitness will be assessed in accordance with the roulette wheel selection procedures Advantage in comparison to the roulette-wheel selection: Strong preference for less capable individuals Weaker deprivation of the most vulnerable individuals Simplified procedures: it is randomly chosen from individuals with a high rank (fied number) selected Only the best % allowed to participate in the recombination 282
14 Eample for comparison 1000-mal Roulette-Wheel-Selection mal Rank based Seletion 283
15 Recombination or crossover From two previously selected individuals (parents) are two new crossover individuals generated (descendants) It is coincidentally a certain position on the selected bit string At this point, the strings cut and the ends are swapped Variations with several crossover points are possible 284
16 Mutation From the newly formed individuals; candidates with a low probability are selected for mutations Mutation: There will be a randomly chosen bit inverted Caution: depending on the chosen coding different mutation has high influence Take into consideration that variations are possible in the selection of bits to be mutated 285
17 Composition of the New Generation Basically you can specify whether a certain percentage of parents' generation takes over to the net generation Often it renounces : the detriment that can happen is that the maimum fitness in the new generation is lower Possible methods: Elitism A fied proportion of the new generation consists of the best representatives of parents' generation, the rest being regenerated (selection, crossover, mutation) Another way: After the recombination the offspring are selected not automatically but the best individuals are selected 286
18 Applications in automation technology Optimization of controller parameters Optimization of parameters in models (approimation of curves) Optimization of controller structures (encoding is difficult Genetic Programming) Optimization of many parameters in a fuzzy controller Rules Membership functions Optimization of many parameters in a neural network Weights Structure 287
19 Genetic Programming Special case of a genetic algorithm Instead of bit strings the individuals are represented by trees The trees are synta trees and provide programs which + + si n ^ 8 * 2 + y Represents the function: f (, y) sin 2 y Represents the function: f ( )
20 Genetic Programming Mutation: * becomes 8 + f ( ) 2 8 becomes f ( )
21 Genetic Programming Recombination: E1: E2: + + si n ^ K1: + K2: + si n + 8 ^ 2 290
22 Genetic Programming with Block diagrarms Recombination Mutation 291
23 Summary and learning from the 10th Lecture Basic idea of GA Comparison of GA s with other optimization methods Individual elements of the GA and know their significance and may illustrate eemplary: Selection Crossing Mutation Possible applications relating to automation technology Relation to Neuro-fuzzy Approach of genetic programming 292
GENETIC ALGORITHMS. Narra Priyanka. K.Naga Sowjanya. Vasavi College of Engineering. Ibrahimbahg,Hyderabad.
GENETIC ALGORITHMS Narra Priyanka K.Naga Sowjanya Vasavi College of Engineering. Ibrahimbahg,Hyderabad mynameissowji@yahoo.com priyankanarra@yahoo.com Abstract Genetic algorithms are a part of evolutionary
More informationEvolutionary Algorithms
Evolutionary Algorithms Evolutionary Algorithms What is Evolutionary Algorithms (EAs)? Evolutionary algorithms are iterative and stochastic search methods that mimic the natural biological evolution and/or
More informationWhat is Evolutionary Computation? Genetic Algorithms. Components of Evolutionary Computing. The Argument. When changes occur...
What is Evolutionary Computation? Genetic Algorithms Russell & Norvig, Cha. 4.3 An abstraction from the theory of biological evolution that is used to create optimization procedures or methodologies, usually
More informationIntroduction To Genetic Algorithms
1 Introduction To Genetic Algorithms Dr. Rajib Kumar Bhattacharjya Department of Civil Engineering IIT Guwahati Email: rkbc@iitg.ernet.in References 2 D. E. Goldberg, Genetic Algorithm In Search, Optimization
More informationIntro. ANN & Fuzzy Systems. Lecture 36 GENETIC ALGORITHM (1)
Lecture 36 GENETIC ALGORITHM (1) Outline What is a Genetic Algorithm? An Example Components of a Genetic Algorithm Representation of gene Selection Criteria Reproduction Rules Cross-over Mutation Potential
More informationEvolutionary Algorithms - Population management and popular algorithms Kai Olav Ellefsen
INF3490 - Biologically inspired computing Lecture 3: Eiben and Smith, chapter 5-6 Evolutionary Algorithms - Population management and popular algorithms Kai Olav Ellefsen Repetition: General scheme of
More informationThe Metaphor. Individuals living in that environment Individual s degree of adaptation to its surrounding environment
Genetic Algorithms Sesi 14 Optimization Techniques Mathematical Programming Network Analysis Branch & Bound Simulated Annealing Tabu Search Classes of Search Techniques Calculus Base Techniqes Fibonacci
More informationVISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY. A seminar report on GENETIC ALGORITHMS.
VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY A seminar report on GENETIC ALGORITHMS Submitted by Pranesh S S 2SD06CS061 8 th semester DEPARTMENT OF COMPUTER SCIENCE
More informationMachine Learning. Genetic Algorithms
Machine Learning Genetic Algorithms Genetic Algorithms Developed: USA in the 1970 s Early names: J. Holland, K. DeJong, D. Goldberg Typically applied to: discrete parameter optimization Attributed features:
More informationMachine Learning. Genetic Algorithms
Machine Learning Genetic Algorithms Genetic Algorithms Developed: USA in the 1970 s Early names: J. Holland, K. DeJong, D. Goldberg Typically applied to: discrete parameter optimization Attributed features:
More informationIntelligent Techniques Lesson 4 (Examples about Genetic Algorithm)
Intelligent Techniques Lesson 4 (Examples about Genetic Algorithm) Numerical Example A simple example will help us to understand how a GA works. Let us find the maximum value of the function (15x - x 2
More informationJournal of Global Research in Computer Science PREMATURE CONVERGENCE AND GENETIC ALGORITHM UNDER OPERATING SYSTEM PROCESS SCHEDULING PROBLEM
Volume, No. 5, December 00 Journal of Global Research in Computer Science RESEARCH PAPER Available Online at www.jgrcs.info PREMATURE CONVERGENCE AND GENETIC ALGORITHM UNDER OPERATING SYSTEM PROCESS SCHEDULING
More informationGenetic Algorithm: A Search of Complex Spaces
Genetic Algorithm: A Search of Complex Spaces Namita Khurana, Anju Rathi, Akshatha.P.S Lecturer in Department of (CSE/IT) KIIT College of Engg., Maruti Kunj, Sohna Road, Gurgaon, India ABSTRACT Living
More informationTIMETABLING EXPERIMENTS USING GENETIC ALGORITHMS. Liviu Lalescu, Costin Badica
TIMETABLING EXPERIMENTS USING GENETIC ALGORITHMS Liviu Lalescu, Costin Badica University of Craiova, Faculty of Control, Computers and Electronics Software Engineering Department, str.tehnicii, 5, Craiova,
More informationCEng 713 Evolutionary Computation, Lecture Notes
CEng 713 Evolutionary Computation, Lecture Notes Introduction to Evolutionary Computation Evolutionary Computation Elements of Evolution: Reproduction Random variation Competition Selection of contending
More informationEvolutionary Algorithms - Introduction and representation Jim Tørresen
INF3490 - Biologically inspired computing Lecture 2: Eiben and Smith, chapter 1-4 Evolutionary Algorithms - Introduction and representation Jim Tørresen Evolution Biological evolution: Lifeforms adapt
More informationGenetic Algorithm. Presented by Shi Yong Feb. 1, 2007 Music McGill University
Genetic Algorithm Presented by Shi Yong Feb. 1, 2007 Music Tech @ McGill University Outline Background: Biological Genetics & GA Two Examples Some Applications Online Demos* (if the time allows) Introduction
More informationPARALLEL LINE AND MACHINE JOB SCHEDULING USING GENETIC ALGORITHM
PARALLEL LINE AND MACHINE JOB SCHEDULING USING GENETIC ALGORITHM Dr.V.Selvi Assistant Professor, Department of Computer Science Mother Teresa women s University Kodaikanal. Tamilnadu,India. Abstract -
More informationDeterministic Crowding, Recombination And Self-Similarity
Deterministic Crowding, Recombination And Self-Similarity Bo Yuan School of Information Technology and Electrical Engineering The University of Queensland Brisbane, Queensland 4072 Australia E-mail: s4002283@student.uq.edu.au
More informationEvolutionary Algorithms and Simulated Annealing in the Topological Configuration of the Spanning Tree
Evolutionary Algorithms and Simulated Annealing in the Topological Configuration of the Spanning Tree A. SADEGHEIH Department of Industrial Engineering University of Yazd, P.O.Box: 89195-741 IRAN, YAZD
More informationGenetic Algorithms in Matrix Representation and Its Application in Synthetic Data
Genetic Algorithms in Matrix Representation and Its Application in Synthetic Data Yingrui Chen *, Mark Elliot ** and Joe Sakshaug *** * ** University of Manchester, yingrui.chen@manchester.ac.uk University
More informationRecessive Trait Cross Over Approach of GAs Population Inheritance for Evolutionary Optimisation
Recessive Trait Cross Over Approach of GAs Population Inheritance for Evolutionary Optimisation Amr Madkour, Alamgir Hossain, and Keshav Dahal Modeling Optimization Scheduling And Intelligent Control (MOSAIC)
More informationGENETIC ALGORITHM CHAPTER 2
CHAPTER 2 GENETIC ALGORITHM Genetic algorithm is basically a method for solving constrained and unconstrained optimization problems. GA is based on the Darwin s theory of natural evolution specified in
More informationEFFECT OF CROSS OVER OPERATOR IN GENETIC ALGORITHMS ON ANTICIPATORY SCHEDULING
24th International Symposium on on Automation & Robotics in in Construction (ISARC 2007) Construction Automation Group, I.I.T. Madras EFFECT OF CROSS OVER OPERATOR IN GENETIC ALGORITHMS ON ANTICIPATORY
More informationEnergy management using genetic algorithms
Energy management using genetic algorithms F. Garzia, F. Fiamingo & G. M. Veca Department of Electrical Engineering, University of Rome "La Sapienza", Italy Abstract An energy management technique based
More informationImprovement of Control System Responses Using GAs PID Controller
International Journal of Industrial and Manufacturing Systems Engineering 2017; 2(2): 11-18 http://www.sciencepublishinggroup.com/j/ijimse doi: 10.11648/j.ijimse.20170202.12 Case Report Improvement of
More informationLogistics. Final exam date. Project Presentation. Plan for this week. Evolutionary Algorithms. Crossover and Mutation
Logistics Crossover and Mutation Assignments Checkpoint -- Problem Graded -- comments on mycourses Checkpoint --Framework Mostly all graded -- comments on mycourses Checkpoint -- Genotype / Phenotype Due
More informationGenetically Evolved Solution to Timetable Scheduling Problem
Genetically Evolved Solution to Timetable Scheduling Problem Sandesh Timilsina Department of Computer Science and Engineering Rohit Negi Department of Computer Science and Engineering Jyotsna Seth Department
More informationIntroduction to Information Systems Fifth Edition
Introduction to Information Systems Fifth Edition R. Kelly Rainer Brad Prince Casey Cegielski Appendix D Intelligent Systems Copyright 2014 John Wiley & Sons, Inc. All rights reserved. 1. Explain the potential
More informationEvolutionary Computation
Evolutionary Computation Dean F. Hougen w/ contributions from Pedro Diaz-Gomez & Brent Eskridge Robotics, Evolution, Adaptation, and Learning Laboratory (REAL Lab) School of Computer Science University
More informationGenetic Algorithms and Sensitivity Analysis in Production Planning Optimization
Genetic Algorithms and Sensitivity Analysis in Production Planning Optimization CECÍLIA REIS 1,2, LEONARDO PAIVA 2, JORGE MOUTINHO 2, VIRIATO M. MARQUES 1,3 1 GECAD Knowledge Engineering and Decision Support
More informationTRAINING FEED FORWARD NEURAL NETWORK USING GENETIC ALGORITHM TO PREDICT MEAN TEMPERATURE
IJRRAS 29 (1) October 216 www.arpapress.com/volumes/vol29issue1/ijrras_29_1_3.pdf TRAINING FEED FORWARD NEURAL NETWORK USING GENETIC ALGORITHM TO PREDICT MEAN TEMPERATURE Manal A. Ashour 1,*, Somia A.
More informationA Genetic Algorithm on Inventory Routing Problem
A Genetic Algorithm on Inventory Routing Problem Artvin Çoruh University e-mail: nevin.aydin@gmail.com Volume 3 No 3 (2014) ISSN 2158-8708 (online) DOI 10.5195/emaj.2014.31 http://emaj.pitt.edu Abstract
More informationGenetic Algorithms. Lecture Notes in Transportation Systems Engineering. Prof. Tom V. Mathew
Genetic Algorithms Lecture Notes in Transportation Systems Engineering Prof. Tom V. Mathew Contents 1 Introduction 1 1.1 Background..................................... 2 1.2 Natural Selection..................................
More informationKeywords Genetic, pseudorandom numbers, cryptosystems, optimal solution.
Volume 6, Issue 8, August 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Apply Genetic
More informationAn Improved Genetic Algorithm for Generation Expansion Planning
916 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 15, NO. 3, AUGUST 2000 An Improved Genetic Algorithm for Generation Expansion Planning Jong-Bae Park, Young-Moon Park, Jong-Ryul Won, and Kwang Y. Lee Abstract
More informationDerivative-based Optimization (chapter 6)
Soft Computing: Derivative-base Optimization Derivative-based Optimization (chapter 6) Bill Cheetham cheetham@cs.rpi.edu Kai Goebel goebel@cs.rpi.edu used for neural network learning used for multidimensional
More informationRule Minimization in Predicting the Preterm Birth Classification using Competitive Co Evolution
Indian Journal of Science and Technology, Vol 9(10), DOI: 10.17485/ijst/2016/v9i10/88902, March 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Rule Minimization in Predicting the Preterm Birth
More informationEvolutionary Algorithms:
GEATbx Introduction Evolutionary Algorithms: Overview, Methods and Operators Hartmut Pohlheim Documentation for: Genetic and Evolutionary Algorithm Toolbox for use with Matlab version: toolbox 3.3 documentation
More informationPublic Key Cryptography Using Genetic Algorithm
International Journal of Recent Technology and Engineering (IJRTE) Public Key Cryptography Using Genetic Algorithm Swati Mishra, Siddharth Bali Abstract Cryptography is an imperative tool for protecting
More informationDesigning High Thermal Conductive Materials Using Artificial Evolution MICHAEL DAVIES, BASKAR GANAPATHYSUBRAMANIAN, GANESH BALASUBRAMANIAN
Designing High Thermal Conductive Materials Using Artificial Evolution MICHAEL DAVIES, BASKAR GANAPATHYSUBRAMANIAN, GANESH BALASUBRAMANIAN The Problem Graphene is one of the most thermally conductive materials
More informationImmune Programming. Payman Samadi. Supervisor: Dr. Majid Ahmadi. March Department of Electrical & Computer Engineering University of Windsor
Immune Programming Payman Samadi Supervisor: Dr. Majid Ahmadi March 2006 Department of Electrical & Computer Engineering University of Windsor OUTLINE Introduction Biological Immune System Artificial Immune
More informationGenetic Algorithms using Populations based on Multisets
Genetic Algorithms using Populations based on Multisets António Manso 1, Luís Correia 1 1 LabMAg - Laboratório de Modelação de Agentes Faculdade de Ciências da Universidade de Lisboa Edifício C6, Piso
More informationArtificial Life Lecture 14 EASy. Genetic Programming. EASy. GP EASy. GP solution to to problem 1. EASy. Picturing a Lisp program EASy
Artificial Life Lecture 14 14 Genetic Programming This will look at 3 aspects of Evolutionary Algorithms: 1) Genetic Programming GP 2) Classifier Systems 3) Species Adaptation Genetic Algorithms -- SAGA
More informationCSE 590 DATA MINING. Prof. Anita Wasilewska SUNY Stony Brook
CSE 590 DATA MINING Prof. Anita Wasilewska SUNY Stony Brook 1 References D. E. Goldberg, Genetic Algorithm In Search, Optimization And Machine Learning, New York: Addison Wesley (1989) John H. Holland
More informationUsing evolutionary techniques to improve the multisensor fusion of environmental measurements
Using evolutionary techniques to improve the multisensor fusion of environmental measurements A.L. Hood 1*, V.M.Becerra 2 and R.J.Craddock 3 1 Technologies for Sustainable Built Environments Centre, University
More informationProceeding, Seminar of Intelligent Technology and Its Applications (SITIA 2000) Graha Institut Teknologi Sepuluh Nopember, Surabaya, 19 April 2000
Proceeding, Seminar of Intelligent Technology and Its Applications (SITIA ) Graha Institut Teknologi Sepuluh Nopember, Surabaya, 19 April Experimental Approach of Mutation Probability Selection of Floating-point-based
More informationOptimizing Genetic Algorithms for Time Critical Problems
Master Thesis Software Engineering Thesis no: MSE-2003-09 June 2003 Optimizing Genetic Algorithms for Time Critical Problems Christian Johansson Gustav Evertsson Department of Software Engineering and
More informationSimulation-Based Analysis and Optimisation of Planning Policies over the Product Life Cycle within the Entire Supply Chain
From the SelectedWorks of Liana Napalkova June, 2009 Simulation-Based Analysis and Optimisation of Planning Policies over the Product Life Cycle within the Entire Supply Chain Galina Merkuryeva Liana Napalkova
More informationStorage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach
Storage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach Z.X. Wang, Felix T.S. Chan, and S.H. Chung Abstract Storage allocation and yard trucks scheduling
More informationParameter identification in the activated sludge process
Parameter identification in the activated sludge process Päivi Holck, Aki Sorsa and Kauko Leiviskä Control Engineering Laboratory, University of Oulu P.O.Box 4300, 90014 Oulun yliopisto, Finland e-mail:
More informationEconomic Design of Control Chart
A Project Report on Economic Design of Control Chart In partial fulfillment of the requirements of Bachelor of Technology (Mechanical Engineering) Submitted By Debabrata Patel (Roll No.10503031) Session:
More informationForecasting Euro United States Dollar Exchange Rate with Gene Expression Programming
Forecasting Euro United States Dollar Exchange Rate with Gene Expression Programming Maria Α. Antoniou 1, Efstratios F. Georgopoulos 1,2, Konstantinos A. Theofilatos 1, and Spiridon D. Likothanassis 1
More informationAn Improved Immune Genetic Algorithm for Capacitated Vehicle Routing Problem
Send Orders for Reprints to reprints@benthamscience.ae 560 The Open Cybernetics & Systemics Journal, 2014, 8, 560-565 Open Access An Improved Immune Genetic Algorithm for Capacitated Vehicle Routing Problem
More informationFacePrints, Maze Solver and Genetic algorithms
Machine Learning CS579 FacePrints, Maze Solver and Genetic algorithms by Jacob Blumberg Presentation Outline Brief reminder genetic algorithms FacePrints a system that evolves faces Improvements and future
More information9 Genetic Algorithms. 9.1 Introduction
9 Genetic Algorithms Genetic algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you might not otherwise find
More informationChanging Mutation Operator of Genetic Algorithms for optimizing Multiple Sequence Alignment
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 11 (2013), pp. 1155-1160 International Research Publications House http://www. irphouse.com /ijict.htm Changing
More informationOptimization of Shell and Tube Heat Exchangers Using modified Genetic Algorithm
Optimization of Shell and Tube Heat Exchangers Using modified Genetic Algorithm S.Rajasekaran 1, Dr.T.Kannadasan 2 1 Dr.NGP Institute of Technology Coimbatore 641048, India srsme@yahoo.co.in 2 Director-Research
More informationUsing Chaos in Genetic Algorithms
Using Chaos in Genetic Algorithms John Determan Idaho National Engineering and Environmental Laboratory P.O. Box 1625 Idaho Falls, ID 83415-2107 jcd@inel.gov James A. Foster University of Idaho Department
More informationA Comparison between Genetic Algorithms and Evolutionary Programming based on Cutting Stock Problem
Engineering Letters, 14:1, EL_14_1_14 (Advance online publication: 12 February 2007) A Comparison between Genetic Algorithms and Evolutionary Programming based on Cutting Stock Problem Raymond Chiong,
More informationClassification and Learning Using Genetic Algorithms
Sanghamitra Bandyopadhyay Sankar K. Pal Classification and Learning Using Genetic Algorithms Applications in Bioinformatics and Web Intelligence With 87 Figures and 43 Tables 4y Spri rineer 1 Introduction
More informationNEUROEVOLUTION AND AN APPLICATION OF AN AGENT BASED MODEL FOR FINANCIAL MARKET
City University of New York (CUNY) CUNY Academic Works Master's Theses City College of New York 2014 NEUROEVOLUTION AND AN APPLICATION OF AN AGENT BASED MODEL FOR FINANCIAL MARKET Anil Yaman CUNY City
More informationINFLUENCE OF DATA QUANTITY ON ACCURACY OF PREDICTIONS IN MODELING TOOL LIFE BY THE USE OF GENETIC ALGORITHMS
International Journal of Industrial Engineering, 21(2), 14-21, 2014 INFLUENCE OF DATA QUANTITY ON ACCURACY OF PREDICTIONS IN MODELING TOOL LIFE BY THE USE OF GENETIC ALGORITHMS Pavel Kovac, Vladimir Pucovsky,
More informationImproved Real Coded Genetic Algorithm and Its Simulation
JOURNAL OF SOFTWARE, VOL. 9, NO. 2, FEBRUARY 2014 389 Improved Real Coded Genetic Algorithm and Its Simulation Jianmin Zhu College of Mechanical Engineering, University of Shanghai for Science and Technology,
More informationOn Naïve Crossover Biases with Reproduction for Simple Solutions to Classification Problems
On Naïve Crossover Biases with Reproduction for Simple Solutions to Classification Problems M. David Terrio and Malcolm I. Heywood Dalhousie University, Faculty of Computer Science 6040 University Avenue,
More informationEVOLUTIONARY ALGORITHMS CT20A6300. Timo Mantere. Lecturer, Ph.D. (Econ. & BA) Department of Electrical engineering and automation University of Vaasa
EVOLUTIONARY ALGORITHMS CT20A6300 Timo Mantere Lecturer, Ph.D. (Econ. & BA) Department of Electrical engineering and automation University of Vaasa tmantere@lut.fi http://www.uwasa.fi/~timan 1 Lecture
More informationPermutation Free Encoding Technique for Evolving Neural Networks
Permutation Free Encoding Technique for Evolving Neural Networks Anupam Das, Md. Shohrab Hossain, Saeed Muhammad Abdullah, and Rashed Ul Islam Department of Computer Science and Engineering, Bangladesh
More informationResearch and Applications of Shop Scheduling Based on Genetic Algorithms
1 Engineering, Technology and Techniques Vol.59: e16160545, January-December 2016 http://dx.doi.org/10.1590/1678-4324-2016160545 ISSN 1678-4324 Online Edition BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY
More informationA Fast Genetic Algorithm with Novel Chromosome Structure for Solving University Scheduling Problems
2013, TextRoad Publication ISSN 2090-4304 Journal of Basic and Applied Scientific Research www.textroad.com A Fast Genetic Algorithm with Novel Chromosome Structure for Solving University Scheduling Problems
More informationImproved Crossover and Mutation Operators for Genetic- Algorithm Project Scheduling
Improved Crossover and Mutation Operators for Genetic- Algorithm Project Scheduling M. A. Abido and A. Elazouni Abstract In Genetic Algorithms (GAs) technique, offspring chromosomes are created by merging
More informationGenetic Algorithms and Protein Folding
Genetic Algorithms and Protein Folding Last Revised Mon Jun 5 17:36:17 MET DST 1995 by Dr. Steffen Schulze Kremer Westfälische Strasse 56, D 10711 Berlin, FRG E mail: steffen@chemie.fu berlin.de Table
More informationImplementation of Genetic Algorithm for Agriculture System
Implementation of Genetic Algorithm for Agriculture System Shweta Srivastava Department of Computer science Engineering Babu Banarasi Das University,Lucknow, Uttar Pradesh, India Diwakar Yagyasen Department
More informationOptimizing Online Auction Bidding Strategies Using Genetic Programming
Optimizing Online Auction Bidding Strategies Using Genetic Programming Ekaterina Smorodkina December 8, 2003 Abstract The research presented in this paper is concerned with creating optimal bidding strategies
More informationOptimization of Riser in Casting Using Genetic Algorithm
IAAST ONLINE ISSN 2277-1565 PRINT ISSN 0976-4828 CODEN: IAASCA International Archive of Applied Sciences and Technology IAAST; Vol 4 [2] June 2013: 21-26 2013 Society of Education, India [ISO9001: 2008
More informationA Study of Crossover Operators for Genetic Algorithms to Solve VRP and its Variants and New Sinusoidal Motion Crossover Operator
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 7 (2017), pp. 1717-1733 Research India Publications http://www.ripublication.com A Study of Crossover Operators
More informationChapter 5: ENCODING. 5.1 Prologue
Chapter 5: ENCODING 5.1 Prologue In real world applications, the search space is defined by a set of objects, each of which has different parameters. The objective of optimisation problem working on these
More informationChapter 6: SELECTION. 6.1 Introduction
Chapter 6: SELECTION 6.1 Introduction Selection is the first genetic operation in the reproductive phase of genetic algorithm. The objective of selection is to choose the fitter individuals in the population
More informationProduct Assembly Sequence Optimization Based on Genetic Algorithm
Product Assembly Sequence Optimization Based on Genetic Algorithm Azman Yasin 1, Nurnasran Puteh 2 College of Arts and Sciences University Utara Malaysia Sintok, Kedah, Malaysia 1 yazman@uum.edu.my, 2
More informationInvestigation of Constant Creation Techniques in the Context of Gene Expression Programming
Investigation of Constant Creation Techniques in the Context of Gene Expression Programming Xin Li 1, Chi Zhou 2, Peter C. Nelson 1, Thomas M. Tirpak 2 1 Artificial Intelligence Laboratory, Department
More informationA Systematic Study of Genetic Algorithms with Genotype Editing
A Systematic Study of Genetic Algorithms with Genotype Editing Chien-Feng Huang and Luis M. Rocha Modeling, Algorithms, and Informatics Group (CCS-3), Computer and Computational Sciences, Los Alamos National
More informationGenotype Editing and the Evolution of Regulation and Memory
Genotype Editing and the Evolution of Regulation and Memory Luis M. Rocha and Jasleen Kaur School of Informatics, Indiana University Bloomington, IN 47406, USA rocha@indiana.edu http://informatics.indiana.edu/rocha
More informationReproduction Strategy Based on Self-Organizing Map for Real-coded Genetic Algorithms
Neural Information Processing - Letters and Reviews Vol. 5, No. 2, November 2004 LETTER Reproduction Strategy Based on Self-Organizing Map for Real-coded Genetic Algorithms Ryosuke Kubota Graduate School
More informationGenetic drift. 1. The Nature of Genetic Drift
Genetic drift. The Nature of Genetic Drift To date, we have assumed that populations are infinite in size. This assumption enabled us to easily calculate the expected frequencies of alleles and genotypes
More informationINTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 3, 2011
Minimization of Total Weighted Tardiness and Makespan for SDST Flow Shop Scheduling using Genetic Algorithm Kumar A. 1 *, Dhingra A. K. 1 1Department of Mechanical Engineering, University Institute of
More informationA GENETIC ALGORITHM FOR POLYTECHNIC TIME TABLING (EEPIS Timetabling Case Study)
A GENETIC ALGORITHM FOR POLYTECHNIC TIME TABLING (EEPIS Timetabling Case Study) Son Kuswadi 1, Achmad Basuki 1,Mohammad NUH 1,2, Osami Saito 3 1 Electronic Engineering Polytechnic Institute of Surabaya
More informationBiological immune systems
Immune Systems 1 Introduction 2 Biological immune systems Living organism must protect themselves from the attempt of other organisms to exploit their resources Some would-be exploiter (pathogen) is much
More informationA Genetic Algorithm Based Pattern Matcher
International Journal of Scientific & Engineering Research, Volume 3, Issue 11, November-2012 A Genetic Algorithm Based Pattern Matcher Sagnik Banerjee, Tamal Chakrabarti, Devadatta Sinha Abstract Pattern
More informationAdaptive Genetic Programming applied to Classification in Data Mining
Adaptive Genetic Programming applied to Classification in Data Mining Nailah Al-Madi and Simone A. Ludwig Department of Computer Science North Dakota State University Fargo, ND, USA nailah.almadi@my.ndsu.edu,
More informationGenetic drift. [Not to be confused with antigenic drift or antigenic shift.]
Genetic drift [Not to be confused with antigenic drift or antigenic shift.] Genetic drift or allelic drift is the change in the frequency of a gene variant (allele) in a population due to random sampling.
More informationApplying Computational Intelligence in Software Testing
www.stmjournals.com Applying Computational Intelligence in Software Testing Saumya Dixit*, Pradeep Tomar School of Information and Communication Technology, Gautam Buddha University, Greater Noida, India
More informationGenetic Algorithms For Protein Threading
From: ISMB-98 Proceedings. Copyright 1998, AAAI (www.aaai.org). All rights reserved. Genetic Algorithms For Protein Threading Jacqueline Yadgari #, Amihood Amir #, Ron Unger* # Department of Mathematics
More informationPLANNING OF ORDER PICKING PROCESSES USING SIMULATION AND A GENETIC ALGORITHM IN MULTI-CRITERIA SCHEDULING OPTIMIZATION
PLANNING OF ORDER PICKING PROCESSES USING SIMULATION AND A GENETIC ALGORITHM IN MULTI-CRITERIA SCHEDULING OPTIMIZATION Balázs Molnár Budapest University of Technology and Economics Department of Transportation
More informationMetaheuristics and Cognitive Models for Autonomous Robot Navigation
Metaheuristics and Cognitive Models for Autonomous Robot Navigation Raj Korpan Department of Computer Science The Graduate Center, CUNY Second Exam Presentation April 25, 2017 1 / 31 Autonomous robot navigation
More informationEvolutionary Algorithms. LIACS Natural Computing Group Leiden University
Evolutionary Algorithms Overview Introduction: Optimization and EAs Genetic Algorithms Evolution Strategies Theory Examples 2 Background I Biology = Engineering (Daniel Dennett) 3 Background II DNA molecule
More informationMINIMIZE THE MAKESPAN FOR JOB SHOP SCHEDULING PROBLEM USING ARTIFICIAL IMMUNE SYSTEM APPROACH
MINIMIZE THE MAKESPAN FOR JOB SHOP SCHEDULING PROBLEM USING ARTIFICIAL IMMUNE SYSTEM APPROACH AHMAD SHAHRIZAL MUHAMAD, 1 SAFAAI DERIS, 2 ZALMIYAH ZAKARIA 1 Professor, Faculty of Computing, Universiti Teknologi
More informationDecision Support and Business Intelligence Systems
Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall) Chapter 4: Modeling and Analysis Learning Objectives Understand the basic concepts of management support system (MSS) modeling
More informationOptimization in Allocating Goods to Shop Shelves. for Cup Noodles
Journal of Computations & Modelling, vol.5, no.4, 2015, 1-25 ISSN: 1792-7625 (print), 1792-8850 (online) Scienpress Ltd, 2015 Optimization in Allocating Goods to Shop Shelves for Cup Noodles Koumei Suzui
More informationEvolving free-form stick ski jumpers and their neural control systems
Evolving free-form stick ski jumpers and their neural control systems Maciej Komosinski and Jan Polak Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland,
More informationEvolving Bidding Strategies for Multiple Auctions
Evolving Bidding Strategies for Multiple Auctions Patricia Anthony and Nicholas R. Jennings 1 Abstract. Due to the proliferation of online auctions, there is an increasing need to monitor and bid in multiple
More informationEvolutionary Computation
Evolutionary Computation Evolutionary Computation A Unified Approach Kenneth A. De Jong ABradfordBook The MIT Press Cambridge, Massachusetts London, England c 2006 Massachusetts Institute of Technology
More information