Hybridization EVOLUTIONARY COMPUTING. Reasons for Hybridization - 1. Naming. Reasons for Hybridization - 3. Reasons for Hybridization - 2
|
|
- Oswald Carroll
- 5 years ago
- Views:
Transcription
1 Hybridization EVOLUTIONARY COMPUTING Hybrid Evolutionary Algorithms hybridization of an EA with local search techniques (commonly called memetic algorithms) EA+LS=MA constructive heuristics exact methods approximation algorithms... Naming hybrid EAs Baldwinian EAs Lamarckian EAs genetic local search algorithms memetic algorithms Reasons for Hybridization - 1 complex problems may be decomposed into sub-prolems for which there are existing exact methods EA as a pre/post processor for other algrithms using knowledge in greedy operators in Eas use as local search in EAs Reasons for Hybridization - 2 (NFL theorem) the success of an EA in a given problem domain depends on amount of domain specific info available used for specialized operators used for initialization Reasons for Hybridization - 3 EAs are very good at exploration but less good at exploitation (e.g. think of the One- Max problem) for handling constraints, i.e. repair operators or mapping of search space 1
2 Michalewicz s View on EAs in Context Hybridization Methods using parallel populations each population uses same / different heuristic each population uses same / different metaheuristic each population uses different parameter settings each population uses different fitness functions Hybridization Methods using approximation used when the fitness evaluation is costly by using an approximate model for some evaluations by using different levels of approximation for sub-populations by using a hierarchical model Hybridization Methods by modifying the problem instance e.g. decreasing the search space size by partitioning into sub-problems through interactive evolution for local tuning of solutions for handling the constraints Parts of an EA to Hybridize Where to Hybridise for creation of initial solutions for local improvement of candidate solutions as intelligent decoders as intelligent / heuristic variation operators 2
3 Hybridization during Initialization initialize population with previously known good solutions good solutions found by other technique inject initial population with good solutions found during previous runs good solutions found by other algorithms Heuristics for Initializing a Population n-way tournament among randomly created solutions multi-start local search: pick popsize points randomly to climb from constructive heuristics often exist domain specific info e.g. tightness ratio in MKP Heuristics for Initializing a Population if elitism used, EA solution cannot be worse than the solution given by a heuristic diversity is important advantage: good solutions found quickly disadvantage: higher possibility to get stuck at local optima (strong bias) Intelligent Operators incorporating problem or instance specific knowledge within operators selection cross-over mutation other special operators usually used with problem specific representations usually fast Intelligent Decoders used with indirect representations a decoding function used for obtaining the phenotype from the genotype decoding function uses problem specific info representations permutations random keys weight codings... Intelligent Decoders usually good for handling constraints time consuming locality problem 3
4 Local Search on Offspring usually known as Memetic Algorithms (MA) EAs with one or more local search phases within the evolutionary cycle usually local search applied to refine individuals inspired by adaptation in natural systems evolutionary adaptation individual learning during lifetime Memetic Algorithms more efficient and effective than traditional EAs for some domains combines exploration abilities of the EA exploitation abilities of local search fast local optimizer needed smoothes fitness landscape introduces redundancy and plateaus Local Search neighbourhood concept depends on representation and operators N(x): set of points that can be reached from x with one application of a move operator e.g. bit flipping search on binary problems g c b f d h a e N(d) = {a,c,h} Standard Local Search standard_local_search(x) begin produce starting solution s repeat until (locally optimal) do generate neighbor n if (f(n) better than f(s)) s<-n od end. Two Models of Lifetime Adaptation Lamarckian traits acquired by individual during lifetime transmitted to offspring e.g. replace individual with fitter neighbour Darwinian or Baldwinian traits acquired by individual during lifetime not transmitted to offspring e.g. individual receives fitness (but not genotype) of fitter neighbour Design Issues for MAs choice of local search operator? changes the fitness landscape and the local optima the MA sees integration into EA cycle when, where and how often to apply local search Lamarckian or Darwinian (Baldwinian)? managing global-local search tradeoff to which individual local search is applied how to avoid large neutral plateaus how much CPU time will be allowed for kocal search 4
5 Choice of Local Search Operator problem dependent sometimes even instance dependent also time-dependent representation + operators landscape as seen by the EA / MA landscape local optima Choice of Local Search Operator possible to use more than one local search heuristic schedule them based on current search status (e.g. convergence) time also possible to use search history multimeme algorithms meta-lamarckian MAs hyper-heuristics Integration into EA Cycle apply before or after crossover and mutation? Lamarckian or Baldwinian? Lamarckian can be applied anywhere in the EA cycle no sense in applying Baldwinian after parent selection but before crossover and mutation Integration into EA Cycle local search in representation or solution space? is neighbourhood searched randomly, systematically or exhaustively? does search stop as soon as a fitter neighbour is found (Greedy Ascent) or is whole set of neighbours examined and the best chosen (Steepest Ascent)... Integration into EA Cycle apply local search for how long? if Lamarckian applied to optimality loss of diversity on average operators will reduce fitness of previously locally optimal solutions these may be in basins of attraction of better local optima so good idea to perform local search on these not to loose in selection more possibility of getting stuck at local optima convergence should be monitored Managing the Global-Local Search Tradeoff local search applied to whole population? or just the best? or just the worst? how to integrate local search with the genetic operators possible to do partial local search to all individuals generated by genetic operators then do more local search on promising solutions 5
6 Managing the Global-Local Search Tradeoff to deal with large neutral plateaus, monitor convergence and change behavior of heuristic accordingly to avoid being trapped in local optima, use multiple local searchers simultaneously Hybrid Algorithms Summary hybridize EA s especially for real world problems hybridization may involve use of operators from other algorithms incorporation of domain-specific knowledge many design issues memetic algorithms shown to be much faster and more accurate than GAs in some problem domains the state of the art on many problems more problem specific usually requires setting of more parameter possible loss of creativity References - 1 A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Springer, E. K. Burke, G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer, N. Krasnogor, J. Smith, A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 5, pp , IEEE Press, W. E. Hart, N. Krasnogor, J. E. Smith, Memetic Evolutionary Algorithms, Recent Advances in Memetic Algoritms, Part 1, pp. 3-27, Eds. W. E. Hart, N. Krasnogor, J. E. Smith, Studies in Fuzziness and Soft Computing Series, Springer, References - 2 Y.-S. Ong, A. J. Keane, Meta-Lamarckian Learning in Memetic Algorithms, IEEE Transactions on Evolutionary Computation, Vol. 8, No. 2, pp , IEEE Press, Y. S. Ong, M. H. Lim, N. Zhu, K. W. Wong, Classification of Adaptive Memetic Algorithms: A Comparative Study, IEEE Transactions on Systems, Man and Cybernetics-Part B:Cybernetis, Vol. 36, No. 1, pp , J. E. Smith, Coevolving Memetic Algorithms: A Review and Progress Report, IEEE Transactions on Systems, Man and Cybernetics-Part B:Cybernetis, Vol. 37, No. 1, pp. 6-17, Case Study: S. N. Jat, S. Yang, A Memetic Agorithm for the University Course Timetabling Problem, 20th IEEE International Conference on Tools with Artificial Intelligence, pp , IEEE, university course timetabling problem UCTP a multi-dimensional assignment problem students and teachers assigned to courses classes assigned to classrooms and timeslots with some hard and soft constraints hard constraints: no student attends more than one event at the same time room capacity is sufficient and has features required by event only obe event is schedules in a room at a time soft constraints a student should not have a class at the last time slot of a day a student should not have more than two classes in a row a student should not have a single class in a day goal: minimize soft constraint violations (no hard constraint violations feasible solution) 6
7 the memetic algorithm integrates two local search techniques into GAs based on three neighborhood structures: N1: an operator taht moves an event from one time slot to another N2: an operator that swaps timeslots of two events N3: an operator that permutes three events in three distinct time slots in on of two possible ways other than the existing permutation of the events the memetic algorithm uses a steady-state GA with one offspring per mating created through binary tournament selection uniform crossover: each event is assigned a time slot from either arent with equal probability (then room assignment is done) mutation randomly chooses one of N1, N2 or N3 and makes a move using it the MA: initialize population for all individuals: apply LS1 apply LS2 while!end_of_iterations do select two parents through tournament selection apply crossover apply mutation apply LS1 apply LS2 child replaces worst in population end. LS1 considers all events first looks at hard constraint violations fixes violations by applying N1, N2, N3 in order to all violating events until a termination condition is achieved (e.g. an improvement occurs, max no of steps reached,...) applies a matching algorithm to effected time slots to resolve room allocation issues if no feasible solution possible, LS1 stops if hard constraint violations are fixed, considers soft constraint volations fixes them in the same way (if possible) without violating hard constraints matching algorithm applied for room allocation LS2 chooses a time slot which has a high penalty value involving a large number of events for computational complexity considerations, worst from a random subset is chosen for each event, uses N1 move and checks the penalty value if moves for all events cause an improvement, moves are accepted experiments done using benchmark intances comparisons done against 9 other approaches failed to give feasible solutions on large instances (so did some others) neighborhood structures should be improved or better genetic operators should be used overall successful 7
Evolutionary Computation for Combinatorial Optimization
Evolutionary Computation for Combinatorial Optimization Günther Raidl Vienna University of Technology, Vienna, Austria raidl@ads.tuwien.ac.at EvoNet Summer School 2003, Parma, Italy August 25, 2003 Evolutionary
More informationCS5401 FS2015 Exam 1 Key
CS5401 FS2015 Exam 1 Key This is a closed-book, closed-notes exam. The only items you are allowed to use are writing implements. Mark each sheet of paper you use with your name and the string cs5401fs2015
More informationOutline of the module
Evolutionary and Heuristic Optimisation (ITNPD8) Lecture 2: Heuristics and Metaheuristics Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ Computing Science and Mathematics, School of Natural Sciences University
More informationIntroduction to Optimization
Introduction to Optimization Approximation Algorithms and Heuristics November 21, 2016 École Centrale Paris, Châtenay-Malabry, France Dimo Brockhoff Inria Saclay Ile-de-France 2 Exercise: The Knapsack
More informationIntroduction to Optimization
Introduction to Optimization Approximation Algorithms and Heuristics November 6, 2015 École Centrale Paris, Châtenay-Malabry, France Dimo Brockhoff INRIA Lille Nord Europe 2 Exercise: The Knapsack Problem
More informationA Steady-State Genetic Algorithm for Traveling Salesman Problem with Pickup and Delivery
A Steady-State Genetic Algorithm for Traveling Salesman Problem with Pickup and Delivery Monika Sharma 1, Deepak Sharma 2 1 Research Scholar Department of Computer Science and Engineering, NNSS SGI Samalkha,
More informationNon-deterministic Search techniques. Emma Hart
Non-deterministic Search techniques Emma Hart Why do local search? Many real problems are too hard to solve with exact (deterministic) techniques Modern, non-deterministic techniques offer ways of getting
More informationAn Evolutionary Algorithm with Stochastic Hill-Climbing for the Edge-Biconnectivity Augmentation Problem
An Evolutionary Algorithm with Stochastic Hill-Climbing for the Edge-Biconnectivity Augmentation Problem Ivana Ljubić and Günther R. Raidl Institute for Computer Graphics and Algorithms, Vienna University
More informationEvolutionary Non-Linear Great Deluge for University Course Timetabling
Evolutionary Non-Linear Great Deluge for University Course Timetabling Dario Landa-Silva and Joe Henry Obit Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, The
More informationEscaping Local Optima: Genetic Algorithm
Artificial Intelligence Escaping Local Optima: Genetic Algorithm Dae-Won Kim School of Computer Science & Engineering Chung-Ang University We re trying to escape local optima To achieve this, we have learned
More informationGenetic Algorithms. Kang Zheng Karl Schober
Genetic Algorithms Kang Zheng Karl Schober Genetic algorithm What is Genetic algorithm? A genetic algorithm (or GA) is a search technique used in computing to find true or approximate solutions to optimization
More informationGenetic Algorithms for Vision and Pattern Recognition
Genetic Algorithms for Vision and Pattern Recognition Faiz Ul Wahab 11/8/2014 1 Objective To solve for optimization of computer vision problems using genetic algorithms 11/8/2014 2 Timeline Problem: Computer
More informationEvolutionary Non-linear Great Deluge for University Course Timetabling
Evolutionary Non-linear Great Deluge for University Course Timetabling Dario Landa-Silva and Joe Henry Obit Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, The
More informationarxiv: v1 [cs.ne] 5 Jan 2013
Hybridization of Evolutionary Algorithms Iztok Fister, Marjan Mernik, and Janez Brest Abstract Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge.
More informationResearch Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding
e Scientific World Journal, Article ID 746260, 8 pages http://dx.doi.org/10.1155/2014/746260 Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding Ming-Yi
More informationAlgorithm Design (4) Metaheuristics
Algorithm Design (4) Metaheuristics Takashi Chikayama School of Engineering The University of Tokyo Formalization of Constraint Optimization Minimize (or maximize) the objective function f(x 0,, x n )
More informationArtificial Intelligence Application (Genetic Algorithm)
Babylon University College of Information Technology Software Department Artificial Intelligence Application (Genetic Algorithm) By Dr. Asaad Sabah Hadi 2014-2015 EVOLUTIONARY ALGORITHM The main idea about
More informationSuppose you have a problem You don t know how to solve it What can you do? Can you use a computer to somehow find a solution for you?
Gurjit Randhawa Suppose you have a problem You don t know how to solve it What can you do? Can you use a computer to somehow find a solution for you? This would be nice! Can it be done? A blind generate
More informationARTIFICIAL INTELLIGENCE (CSCU9YE ) LECTURE 5: EVOLUTIONARY ALGORITHMS
ARTIFICIAL INTELLIGENCE (CSCU9YE ) LECTURE 5: EVOLUTIONARY ALGORITHMS Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Optimisation problems Optimisation & search Two Examples The knapsack problem
More informationMETAHEURISTICS Genetic Algorithm
METAHEURISTICS Genetic Algorithm Jacques A. Ferland Department of Informatique and Recherche Opérationnelle Université de Montréal ferland@iro.umontreal.ca Genetic Algorithm (GA) Population based algorithm
More informationCHAPTER 5 ENERGY MANAGEMENT USING FUZZY GENETIC APPROACH IN WSN
97 CHAPTER 5 ENERGY MANAGEMENT USING FUZZY GENETIC APPROACH IN WSN 5.1 INTRODUCTION Fuzzy systems have been applied to the area of routing in ad hoc networks, aiming to obtain more adaptive and flexible
More informationMeta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization
2017 2 nd International Electrical Engineering Conference (IEEC 2017) May. 19 th -20 th, 2017 at IEP Centre, Karachi, Pakistan Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic
More informationHeuristic Optimisation
Heuristic Optimisation Revision Lecture Sándor Zoltán Németh http://web.mat.bham.ac.uk/s.z.nemeth s.nemeth@bham.ac.uk University of Birmingham S Z Németh (s.nemeth@bham.ac.uk) Heuristic Optimisation University
More informationLecture
Lecture.. 7 Constrained problems & optimization Brief introduction differential evolution Brief eample of hybridization of EAs Multiobjective problems & optimization Pareto optimization This slides mainly
More informationGenetic Algorithms Variations and Implementation Issues
Genetic Algorithms Variations and Implementation Issues CS 431 Advanced Topics in AI Classic Genetic Algorithms GAs as proposed by Holland had the following properties: Randomly generated population Binary
More informationGenetic Algorithms and Genetic Programming Lecture 7
Genetic Algorithms and Genetic Programming Lecture 7 Gillian Hayes 13th October 2006 Lecture 7: The Building Block Hypothesis The Building Block Hypothesis Experimental evidence for the BBH The Royal Road
More informationLecture 6: The Building Block Hypothesis. Genetic Algorithms and Genetic Programming Lecture 6. The Schema Theorem Reminder
Lecture 6: The Building Block Hypothesis 1 Genetic Algorithms and Genetic Programming Lecture 6 Gillian Hayes 9th October 2007 The Building Block Hypothesis Experimental evidence for the BBH The Royal
More informationPROBLEM SOLVING AND SEARCH IN ARTIFICIAL INTELLIGENCE
Artificial Intelligence, Computational Logic PROBLEM SOLVING AND SEARCH IN ARTIFICIAL INTELLIGENCE Lecture 10 Tree Decompositions Sarah Gaggl Dresden, 30th June 2015 Agenda 1 Introduction 2 Uninformed
More informationChapter 9: Genetic Algorithms
Computational Intelligence: Second Edition Contents Compact Overview First proposed by Fraser in 1957 Later by Bremermann in 1962 and Reed et al in 1967 Popularized by Holland in 1975 Genetic algorithms
More informationA Learning Automata-based Memetic Algorithm
A Learning Automata-based Memetic Algorithm M. Rezapoor Mirsaleh and M. R. Meybodi 2,3 Soft Computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology,
More informationPseudo-code for typical EA
Extra Slides for lectures 1-3: Introduction to Evolutionary algorithms etc. The things in slides were more or less presented during the lectures, combined by TM from: A.E. Eiben and J.E. Smith, Introduction
More informationevent between resources such as by employing a HC [2]. [3-5] employed different clustering methods to solve the UCTP.
Hybridizing Genetic Algorithms and Particle Swarm Optimization Transplanted into a Hyper-Heuristic System for Solving University Course Timetabling Problem Morteza Alinia Ahandani* Department of Electrical
More informationIntroduction to Genetic Algorithms. Based on Chapter 10 of Marsland Chapter 9 of Mitchell
Introduction to Genetic Algorithms Based on Chapter 10 of Marsland Chapter 9 of Mitchell Genetic Algorithms - History Pioneered by John Holland in the 1970s Became popular in the late 1980s Based on ideas
More informationHYBRID GENETIC ALGORITHM AND GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE FOR SOLVING A NURSE SCHEDULING PROBLEM
HYBRID GENETIC ALGORITHM AND GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE FOR SOLVING A NURSE SCHEDULING PROBLEM 1 CHEBIHI FAYCAL, 2 MOHAMMED ESSAID RIFFI, 3 BELAID AHIOD 1 Research Scholar, Department
More informationIntroduction to Optimization
Introduction to Optimization Randomized Search Heuristics + Introduction to Continuous Optimization I November 25, 2016 École Centrale Paris, Châtenay-Malabry, France Dimo Brockhoff INRIA Saclay Ile-de-France
More informationAn Introduction to Evolutionary Algorithms
An Introduction to Evolutionary Algorithms Karthik Sindhya, PhD Postdoctoral Researcher Industrial Optimization Group Department of Mathematical Information Technology Karthik.sindhya@jyu.fi http://users.jyu.fi/~kasindhy/
More informationIntroduction to Artificial Intelligence 2 nd semester 2016/2017. Chapter 4: Beyond Classical Search
Introduction to Artificial Intelligence 2 nd semester 2016/2017 Chapter 4: Beyond Classical Search Mohamed B. Abubaker Palestine Technical College Deir El-Balah 1 Outlines local search algorithms and optimization
More informationA Genetic Algorithm for the Multiple Knapsack Problem in Dynamic Environment
, 23-25 October, 2013, San Francisco, USA A Genetic Algorithm for the Multiple Knapsack Problem in Dynamic Environment Ali Nadi Ünal Abstract The 0/1 Multiple Knapsack Problem is an important class of
More informationLocal Search. CS 486/686: Introduction to Artificial Intelligence Winter 2016
Local Search CS 486/686: Introduction to Artificial Intelligence Winter 2016 1 Overview Uninformed Search Very general: assumes no knowledge about the problem BFS, DFS, IDS Informed Search Heuristics A*
More informationGenetic Algorithms: Setting Parmeters and Incorporating Constraints OUTLINE OF TOPICS: 1. Setting GA parameters. 2. Constraint Handling (two methods)
Genetic Algorithms: Setting Parmeters and Incorporating Constraints OUTLINE OF TOPICS: 1. Setting GA parameters general guidelines for binary coded GA (some can be extended to real valued GA) estimating
More informationLocal Search. CS 486/686: Introduction to Artificial Intelligence
Local Search CS 486/686: Introduction to Artificial Intelligence 1 Overview Uninformed Search Very general: assumes no knowledge about the problem BFS, DFS, IDS Informed Search Heuristics A* search and
More informationMutations for Permutations
Mutations for Permutations Insert mutation: Pick two allele values at random Move the second to follow the first, shifting the rest along to accommodate Note: this preserves most of the order and adjacency
More informationGRASP. Greedy Randomized Adaptive. Search Procedure
GRASP Greedy Randomized Adaptive Search Procedure Type of problems Combinatorial optimization problem: Finite ensemble E = {1,2,... n } Subset of feasible solutions F 2 Objective function f : 2 Minimisation
More informationComputational Intelligence
Computational Intelligence Module 6 Evolutionary Computation Ajith Abraham Ph.D. Q What is the most powerful problem solver in the Universe? ΑThe (human) brain that created the wheel, New York, wars and
More informationApplication of Emerging Metaheuristics in Power System Field
Application of Emerging Metaheuristics in Power System Field Dr.ir. J.L. Rueda Torres 26 th June 2015 1 Outline 1. Problem complexity 2. Why metaheuristics? 3. Emerging algorithms 4. Applications 2 1 Problem
More informationCMU-Q Lecture 8: Optimization I: Optimization for CSP Local Search. Teacher: Gianni A. Di Caro
CMU-Q 15-381 Lecture 8: Optimization I: Optimization for CSP Local Search Teacher: Gianni A. Di Caro LOCAL SEARCH FOR CSP Real-life CSPs can be very large and hard to solve Methods so far: construct a
More informationGenetic Algorithms. Chapter 3
Chapter 3 1 Contents of this Chapter 2 Introductory example. Representation of individuals: Binary, integer, real-valued, and permutation. Mutation operator. Mutation for binary, integer, real-valued,
More informationSearch Algorithms for Regression Test Suite Minimisation
School of Physical Sciences and Engineering King s College London MSc in Advanced Software Engineering Search Algorithms for Regression Test Suite Minimisation By Benjamin Cook Supervised by Prof. Mark
More informationPre-requisite Material for Course Heuristics and Approximation Algorithms
Pre-requisite Material for Course Heuristics and Approximation Algorithms This document contains an overview of the basic concepts that are needed in preparation to participate in the course. In addition,
More informationMINIMAL EDGE-ORDERED SPANNING TREES USING A SELF-ADAPTING GENETIC ALGORITHM WITH MULTIPLE GENOMIC REPRESENTATIONS
Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 5 th, 2006 MINIMAL EDGE-ORDERED SPANNING TREES USING A SELF-ADAPTING GENETIC ALGORITHM WITH MULTIPLE GENOMIC REPRESENTATIONS Richard
More informationEvolutionary Computation Part 2
Evolutionary Computation Part 2 CS454, Autumn 2017 Shin Yoo (with some slides borrowed from Seongmin Lee @ COINSE) Crossover Operators Offsprings inherit genes from their parents, but not in identical
More informationChapter 14 Global Search Algorithms
Chapter 14 Global Search Algorithms An Introduction to Optimization Spring, 2015 Wei-Ta Chu 1 Introduction We discuss various search methods that attempts to search throughout the entire feasible set.
More informationArtificial Intelligence
Artificial Intelligence Informed Search and Exploration Chapter 4 (4.3 4.6) Searching: So Far We ve discussed how to build goal-based and utility-based agents that search to solve problems We ve also presented
More informationJob Shop Scheduling Problem (JSSP) Genetic Algorithms Critical Block and DG distance Neighbourhood Search
A JOB-SHOP SCHEDULING PROBLEM (JSSP) USING GENETIC ALGORITHM (GA) Mahanim Omar, Adam Baharum, Yahya Abu Hasan School of Mathematical Sciences, Universiti Sains Malaysia 11800 Penang, Malaysia Tel: (+)
More informationHeuristic Optimisation
Heuristic Optimisation Part 10: Genetic Algorithm Basics Sándor Zoltán Németh http://web.mat.bham.ac.uk/s.z.nemeth s.nemeth@bham.ac.uk University of Birmingham S Z Németh (s.nemeth@bham.ac.uk) Heuristic
More informationMultidimensional Knapsack Problem: The Influence of Representation
Centre for Informatics and Systems of the University of Coimbra Technical Report 003 February 2007 Multidimensional Knapsack Problem: The Influence of Representation Jorge Tavares Centre for Informatics
More informationLecture 6: Genetic Algorithm. An Introduction to Meta-Heuristics, Produced by Qiangfu Zhao (Since 2012), All rights reserved
Lecture 6: Genetic Algorithm An Introduction to Meta-Heuristics, Produced by Qiangfu Zhao (Since 2012), All rights reserved Lec06/1 Search and optimization again Given a problem, the set of all possible
More informationEnhanced Genetic Algorithm for Solving the School Timetabling Problem
Enhanced Genetic Algorithm for Solving the School Timetabling Problem Tan Lay Leng and I.A. Karimi Department of Chemical and Environment Engineering National University of Singapore 10 Kent Ridge Crescent
More informationAn Empirical Investigation of Meta-heuristic and Heuristic Algorithms for a 2D Packing Problem
European Journal of Operational Research 128/1, 34-57, 2000. An Empirical Investigation of Meta-heuristic and Heuristic Algorithms for a 2D Packing Problem E. Hopper and B. C. H. Turton School of Engineering,
More informationEvolving SQL Queries for Data Mining
Evolving SQL Queries for Data Mining Majid Salim and Xin Yao School of Computer Science, The University of Birmingham Edgbaston, Birmingham B15 2TT, UK {msc30mms,x.yao}@cs.bham.ac.uk Abstract. This paper
More informationHeuristic Optimization Introduction and Simple Heuristics
Heuristic Optimization Introduction and Simple Heuristics José M PEÑA (jmpena@fi.upm.es) (Universidad Politécnica de Madrid) 1 Outline 1. What are optimization problems? 2. Exhaustive vs. Heuristic approaches
More informationReducing Graphic Conflict In Scale Reduced Maps Using A Genetic Algorithm
Reducing Graphic Conflict In Scale Reduced Maps Using A Genetic Algorithm Dr. Ian D. Wilson School of Technology, University of Glamorgan, Pontypridd CF37 1DL, UK Dr. J. Mark Ware School of Computing,
More informationReal-Coded Memetic Algorithms with Crossover Hill-Climbing
Real-Coded Memetic Algorithms with Crossover Hill-Climbing Manuel Lozano lozano@decsai.ugr.es Dept. of Computer Science and A.I., University of Granada, 18071 - Granada, Spain Francisco Herrera herrera@decsai.ugr.es
More informationA Method of View Materialization Using Genetic Algorithm
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 2, Ver. III (Mar-Apr. 2016), PP 125-133 www.iosrjournals.org A Method of View Materialization Using
More informationDERIVATIVE-FREE OPTIMIZATION
DERIVATIVE-FREE OPTIMIZATION Main bibliography J.-S. Jang, C.-T. Sun and E. Mizutani. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, New Jersey,
More informationDETERMINING MAXIMUM/MINIMUM VALUES FOR TWO- DIMENTIONAL MATHMATICLE FUNCTIONS USING RANDOM CREOSSOVER TECHNIQUES
DETERMINING MAXIMUM/MINIMUM VALUES FOR TWO- DIMENTIONAL MATHMATICLE FUNCTIONS USING RANDOM CREOSSOVER TECHNIQUES SHIHADEH ALQRAINY. Department of Software Engineering, Albalqa Applied University. E-mail:
More informationEVOLVING LEGO. Exploring the impact of alternative encodings on the performance of evolutionary algorithms. 1. Introduction
N. Gu, S. Watanabe, H. Erhan, M. Hank Haeusler, W. Huang, R. Sosa (eds.), Rethinking Comprehensive Design: Speculative Counterculture, Proceedings of the 19th International Conference on Computer- Aided
More informationEvolutionary multi-objective algorithm design issues
Evolutionary multi-objective algorithm design issues Karthik Sindhya, PhD Postdoctoral Researcher Industrial Optimization Group Department of Mathematical Information Technology Karthik.sindhya@jyu.fi
More informationCHAPTER 4 FEATURE SELECTION USING GENETIC ALGORITHM
CHAPTER 4 FEATURE SELECTION USING GENETIC ALGORITHM In this research work, Genetic Algorithm method is used for feature selection. The following section explains how Genetic Algorithm is used for feature
More informationEvolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem
Evolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem Felix Streichert, Holger Ulmer, and Andreas Zell Center for Bioinformatics Tübingen (ZBIT), University of Tübingen,
More informationSolving Constraint Satisfaction Problems with Heuristic-based Evolutionary Algorithms
; Solving Constraint Satisfaction Problems with Heuristic-based Evolutionary Algorithms B.G.W. Craenen Vrije Universiteit Faculty of Exact Sciences De Boelelaan 1081 1081 HV Amsterdam Vrije Universiteit
More informationThe Genetic Algorithm for finding the maxima of single-variable functions
Research Inventy: International Journal Of Engineering And Science Vol.4, Issue 3(March 2014), PP 46-54 Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.com The Genetic Algorithm for finding
More informationA Web-Based Evolutionary Algorithm Demonstration using the Traveling Salesman Problem
A Web-Based Evolutionary Algorithm Demonstration using the Traveling Salesman Problem Richard E. Mowe Department of Statistics St. Cloud State University mowe@stcloudstate.edu Bryant A. Julstrom Department
More informationFuzzy Inspired Hybrid Genetic Approach to Optimize Travelling Salesman Problem
Fuzzy Inspired Hybrid Genetic Approach to Optimize Travelling Salesman Problem Bindu Student, JMIT Radaur binduaahuja@gmail.com Mrs. Pinki Tanwar Asstt. Prof, CSE, JMIT Radaur pinki.tanwar@gmail.com Abstract
More informationOutline. CS 6776 Evolutionary Computation. Numerical Optimization. Fitness Function. ,x 2. ) = x 2 1. , x , 5.0 x 1.
Outline CS 6776 Evolutionary Computation January 21, 2014 Problem modeling includes representation design and Fitness Function definition. Fitness function: Unconstrained optimization/modeling Constrained
More informationGenetic Programming. and its use for learning Concepts in Description Logics
Concepts in Description Artificial Intelligence Institute Computer Science Department Dresden Technical University May 29, 2006 Outline Outline: brief introduction to explanation of the workings of a algorithm
More informationIMPROVING A GREEDY DNA MOTIF SEARCH USING A MULTIPLE GENOMIC SELF-ADAPTATING GENETIC ALGORITHM
Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 4th, 2007 IMPROVING A GREEDY DNA MOTIF SEARCH USING A MULTIPLE GENOMIC SELF-ADAPTATING GENETIC ALGORITHM Michael L. Gargano, mgargano@pace.edu
More informationEvolutionary Methods for State-based Testing
Evolutionary Methods for State-based Testing PhD Student Raluca Lefticaru Supervised by Florentin Ipate University of Piteşti, Romania Department of Computer Science Outline Motivation Search-based software
More informationTHE Multiconstrained 0 1 Knapsack Problem (MKP) is
An Improved Genetic Algorithm for the Multiconstrained 0 1 Knapsack Problem Günther R. Raidl Abstract This paper presents an improved hybrid Genetic Algorithm (GA) for solving the Multiconstrained 0 1
More informationMulti-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design
City University of New York (CUNY) CUNY Academic Works International Conference on Hydroinformatics 8-1-2014 Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design Matthew
More informationIncorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms
H. Ishibuchi, T. Doi, and Y. Nojima, Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms, Lecture Notes in Computer Science 4193: Parallel Problem Solving
More informationGENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM
Journal of Al-Nahrain University Vol.10(2), December, 2007, pp.172-177 Science GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM * Azhar W. Hammad, ** Dr. Ban N. Thannoon Al-Nahrain
More informationParameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks
Submitted Soft Computing Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks C. Bielza,*, J.A. Fernández del Pozo, P. Larrañaga Universidad Politécnica de Madrid, Departamento
More informationarxiv: v1 [cs.ai] 12 Feb 2017
GENETIC AND MEMETIC ALGORITHM WITH DIVERSITY EQUILIBRIUM BASED ON GREEDY DIVERSIFICATION ANDRÉS HERRERA-POYATOS 1 AND FRANCISCO HERRERA 1,2 arxiv:1702.03594v1 [cs.ai] 12 Feb 2017 1 Research group Soft
More informationSimulated Annealing. G5BAIM: Artificial Intelligence Methods. Graham Kendall. 15 Feb 09 1
G5BAIM: Artificial Intelligence Methods Graham Kendall 15 Feb 09 1 G5BAIM Artificial Intelligence Methods Graham Kendall Simulated Annealing Simulated Annealing Motivated by the physical annealing process
More informationGreat Deluge with Non-linear Decay Rate for Solving Course Timetabling Problems
2008 4th International IEEE Conference "Intelligent Systems" Great Deluge with Non-linear Decay Rate for Solving Course Timetabling Problems Dario Landa-Silva and Joe Henry Obit Abstract Course timetabling
More informationAI Programming CS S-08 Local Search / Genetic Algorithms
AI Programming CS662-2013S-08 Local Search / Genetic Algorithms David Galles Department of Computer Science University of San Francisco 08-0: Overview Local Search Hill-Climbing Search Simulated Annealing
More informationLocal Search (Greedy Descent): Maintain an assignment of a value to each variable. Repeat:
Local Search Local Search (Greedy Descent): Maintain an assignment of a value to each variable. Repeat: Select a variable to change Select a new value for that variable Until a satisfying assignment is
More informationPath Planning Optimization Using Genetic Algorithm A Literature Review
International Journal of Computational Engineering Research Vol, 03 Issue, 4 Path Planning Optimization Using Genetic Algorithm A Literature Review 1, Er. Waghoo Parvez, 2, Er. Sonal Dhar 1, (Department
More informationA Memetic Genetic Program for Knowledge Discovery
A Memetic Genetic Program for Knowledge Discovery by Gert Nel Submitted in partial fulfilment of the requirements for the degree Master of Science in the Faculty of Engineering, Built Environment and Information
More informationCS:4420 Artificial Intelligence
CS:4420 Artificial Intelligence Spring 2018 Beyond Classical Search Cesare Tinelli The University of Iowa Copyright 2004 18, Cesare Tinelli and Stuart Russell a a These notes were originally developed
More informationEvolutionary Computation Algorithms for Cryptanalysis: A Study
Evolutionary Computation Algorithms for Cryptanalysis: A Study Poonam Garg Information Technology and Management Dept. Institute of Management Technology Ghaziabad, India pgarg@imt.edu Abstract The cryptanalysis
More informationGenetic Algorithm for Dynamic Capacitated Minimum Spanning Tree
28 Genetic Algorithm for Dynamic Capacitated Minimum Spanning Tree 1 Tanu Gupta, 2 Anil Kumar 1 Research Scholar, IFTM, University, Moradabad, India. 2 Sr. Lecturer, KIMT, Moradabad, India. Abstract Many
More informationAlgorithms & Complexity
Algorithms & Complexity Nicolas Stroppa - nstroppa@computing.dcu.ie CA313@Dublin City University. 2006-2007. November 21, 2006 Classification of Algorithms O(1): Run time is independent of the size of
More informationExploration vs. Exploitation in Differential Evolution
Exploration vs. Exploitation in Differential Evolution Ângela A. R. Sá 1, Adriano O. Andrade 1, Alcimar B. Soares 1 and Slawomir J. Nasuto 2 Abstract. Differential Evolution (DE) is a tool for efficient
More informationMETAHEURISTICS. Introduction. Introduction. Nature of metaheuristics. Local improvement procedure. Example: objective function
Introduction METAHEURISTICS Some problems are so complicated that are not possible to solve for an optimal solution. In these problems, it is still important to find a good feasible solution close to the
More informationNeural Network Weight Selection Using Genetic Algorithms
Neural Network Weight Selection Using Genetic Algorithms David Montana presented by: Carl Fink, Hongyi Chen, Jack Cheng, Xinglong Li, Bruce Lin, Chongjie Zhang April 12, 2005 1 Neural Networks Neural networks
More informationBI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR FLEXIBLE JOB-SHOP SCHEDULING PROBLEM. Minimizing Make Span and the Total Workload of Machines
International Journal of Mathematics and Computer Applications Research (IJMCAR) ISSN 2249-6955 Vol. 2 Issue 4 Dec - 2012 25-32 TJPRC Pvt. Ltd., BI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR FLEXIBLE JOB-SHOP
More information3.6.2 Generating admissible heuristics from relaxed problems
3.6.2 Generating admissible heuristics from relaxed problems To come up with heuristic functions one can study relaxed problems from which some restrictions of the original problem have been removed The
More information