GENETIC ALGORlIHMS WITH AN APPLICATION TO NONLINEAR TRANSPORTATION PROBLEMS
|
|
- Roberta Gray
- 5 years ago
- Views:
Transcription
1 101 GENETIC ALGORlIHMS WITH AN APPLICATION TO NONLINEAR TRANSPORTATION PROBLEMS MATTHEW HOBBS ISOR, VICTORIA UNIVERSITY WELLINGTON Abstract In this paper I will describe the theory and implementation of Genetic Algorithms. This is followed by a description of an attempt to solve the nonlinear transportation problem using a genetic algorithm approach, (Michalewicz, Vignaux, & Hobbs [1990]). This is a continuation of earlier research in which a cv«tem was built for the standard linear transportation problem (Vignaux & Michalewicz [1990]). Results of this technique are promising. 1 G enetic Program m ing Much of problem solving in O perations Research involves optim ization using one of a variety of techniques. The m ost extensive algorithm in practice is that of Linear Programming. Like LP, m ost algorithm s apply only to relatively sm all problem domains. Som e algorithm s guarantee an optim al solution while others are heuristics and aim to find an approxim ate or good solution. T his still leaves a wide range of problems for which there are no useful algorithm s. In general, algorithm s take advantage of particular structural properties of the problem in order to solve it. This approach fails for m ost com plex problem s. In may cases the information m ay be little more than the payoff of any particular solution (p.? cost) and the expression of the solution (e.g.? p"'h ct:cn schedule). In this case the procedure for payoff optim ization is unclear. If we have a given solution, w ith a determ ined payoff, how do we know if it is optim al? How do we determine what part of the solution expression should be changed if it is not? W ith only the one solution we can usually answer none of these questions. The key is to incorporate comparisons with other solutions into the algorithm - of tbe type described above are usually probabilistic search tech niques. In the sim plest case (with the least number of assum ptions m ade) com pletely random search operates by randomly selecting another solution (independent to the history of the search) and remembering only the best solution so far. The obvious
2 102 disadvantage is the tim e costs involved (when the feasible set is of any size). A more sensible approach is to som ehow bias the random search to home in on the best solutions, quickly. An area where random search can be improved is in usiii6 of the search to select new (trial) solutions. The questions becom es one of asking how do we m ost efficiently use m e history to produce the trial solutions? Sim ulated Annealing is an algorithm whereby the history at any stage in the algorithm is the current solution. A trial solution is chosen by m odifying the current solution in som e way (m utation; and the new current solution is chosen probabilistically between the trial and current solutions, according to their relative payoff. In Sim ulated A nnealing the search history used at any step is only the current solution, previous solutions are discarded. G enetic Algorithm s use the idea that previous solutions should not be discarded im m ediately and that useful in fo ;:.. can be obtained by m aintaining groups of solutions as the search history. The prim ary problem is how to com bine the pool of solutions to create new (better) solutions. T he concept of m aintaining a pool of solutions in order to generate new trial solutions is analogous to that of natural evolution with genetic code being the expression of an individual solution. New solutions are created by com bining code from existing solutions (crossover). The optim izational tendency occurs when current solutions are discarded from the pool (e.g. die) according to lack of payoff. A lgorithm s that include ideas such as these are called G enetic A lgorithm s (Holland [1975], D avis [1987], G oldberg [1989]). A typical G enetic A lgorithm starts w ith a population of random ly generated solutions (the initial population) and repeatedly applies genetic operators m odeled on natural genetic processes (e.g. crossover, m utation) to the population. Consider this extract from D avis [1987]:... the m etaphor underlying genetic algorithm s is that of natural evolution. In evolution, the problem each species faces is one of searching for beneficial adaptions to a com plicated and changing environm ent. T he knowledge that each species has gained is em bodied in the m akeup of the chrom osom es of its m em bers. The operations that alter this chrom osom al m akeup are applied when parents reproduce; am ong them are random m utation, inversion of chrom osom al m aterial, and crossover - exchange of chrom osom al m aterial oetween two parents chrom osom es." Theory Any run of a genetic algorithm is essentially a sim ulation of the evolution of a set of solutions exposed to constraints and performance (the environm ent). Those individuals (solutions) better suited to the environm ent, in general, will have a greater chance of survival and therefore have a greater chance of producing offspring. A lthough this m ay seem initially as a hill-clim bing process, the algorithm (at the sam e tim e) m aintains population diversity within the search space. The ability to m aintain diversity enables the algorithm to escape local optim a and ie a p tall buildings w ithout too much ado. Conversely, near a global optim um, the algorithm would have difficulty hom ing in to the precise solutions, due to the blindness of the random m utations.
3 103 The expression of a solution ( chromosome) for various problems will vary considerably but can be considered as a collection of elem ents (genes). Ideally we would like to be able to treat each gene separately, for optim ization purposes. T his is not usually feasible due to the often highly nonlinear or constrained nature of the problem. The only information we have m ay be the payoff of the chrom osom e jt = whole. A feature of the genetic algorithm is in that although it appears to involve only com petition between chrom osom es, due to the nature of the genetic operators (e.g. crossover) - that m anipulate genes directly - in fact com petition occurs at the gene level. This makes sense when we consider that new solutions are created with only a subset of genes differing from their parent(s). A useful term to use is that of a schema - see Holland [1975] or G oldberg [1989]. A schem a (in this context) can be considered as a sim ilarity tem plate for chrom o somes defining a set of fixed genes. For exam ple fixing gene number 3 at yes' and gene number 7 at no (and leaving all other genes free) defines a schem a for the chrom osom es. To be m ore accurate than the previous paragraph, com petition is on the schem a level. A lthough a genetic algorithm processes only n structures per generation, it also processes of th e order of n3 schem ata. Holland has nam ed tlys im portant result implicit parallelism. It is generally accepted that for any problem to be attem pted by a genetic algorithm there must be five com ponents, as follows. 1. A genetic representation of solutions to the problem (chrom osom e), 2. A way to crcatc random solutions (for the initialization). 3. An evaluation function (the environm ent ), rating solutions by fitness. 4. G enetic operators to alter child com position during reproduction, and 5. Values for the param eters that the genetic algorithm uses (population size, probabilities of applying genetic operators, etc.) For a variety of reasons m ost applications of the genetic algorithm have used binary (bit string) representations for the solutions (chromosomes). But the -im plicit parallelism result is not restricted to bit strings (see A ntonisse [1989]). The application in this paper uses a m atrix of real numbers. Although it is true that any structure that can be put on com puter can be represented as a binary vector (string), the difference is that the schem a for the richer structure are much m ore relevant, and there are much less of them. Im plem entation A genetic algorithm will typically be of the following form. 1. create an initial set of random solutions (the initial population); 2. evaluate the solutions ^see now good they are); 3. put the better solutions into a set:
4 try to combine these solutions to produce child solutions; 5. discard poorer solutions to m aintain population size; 6. repeat the above three steps until we get a good solution or resources run out. There are two main genetic operators, mutation and crossover. The m utation operator arbitrarily alters one or m ore com ponents of a selected structure this increases the variability of the population (introducing new - or lost - schem a). In general, each gene of each chrom osom e in the population undergoes a random change with a probability equal to the m utation rate. The crossover operator com bines the features of two parent structures to form two sim ilar offspring. It operates by sw apping corresponding segm ents of a chrom osom e representing the parent solut ions (w ith the ob jective of com bining good schem ata to produce better schem ata - with more fixed genes). W hen problem s are found to be too specialized or com plex for the standard algorithm s it is necessary to take a heuristic approach to the problem. G enetic algorithm s can be a powerful tool in such cases due to the fact that it can com bine the inform ation from various solutions (and at the sam e tim e, m aintain the diversity required to try to span the search space). Currently there is no program ming language specific to this problem dom ain so system s have had to be set up in other languages as available. It should be noted that when the param eters to the problem are changed (e.g. a different problem of the sam e type) the programs set up will require very little change. For exam ple if the objective function (goal) of the problem changes it is only necessary to change the evaluation procedure and no other. Such system s are m ore flexible to changes than m ost. For exam ple a problem beine solved using Linear Program m ing is fine until nonlinearity is introduced, in which case another algorithm is required. G enetic algorithm s can com pete very effectively against standard nonlinear algorithm s on nonlinear problems. T hey m ay be best used in com bination with nonlinear algorithm s for such problem s. For exam ple using a nonlinear algorithm to home-in on the local optim a. There are a number of applications for which genetic algorithm s have been applied including: physical design of circuit boards; travelling salesm an (nodecovering) problem; com binatorial problem s in general; gam e strategy; nonlinear problem s; optical design; keyboard configuration; m achine learning; sim ulation of evolution: and gas pipeline optim ization. In fact any problem that m eets the five properties listed previously can be attem pted by a genetic algorithm. 2 A G enetic Algorithm for the Nonlinear Transportation Problem In testin g the use of the genetic algorithm on the linear transportation problem (see V ignaux Iz M ichalewicz [1989],[1990]) it is possible to compare its solution with the known optim um found using the standard algorithm and therefore to determ ine how efficient or otherw ise the genetic algorithm is in absolute term s. Once we m ove to nonlinear objective functions, the optim um m ay not be known. Testing is reduced to com paring the results with those of other nonlinear solution m ethods that may them selves have converged to a local optim um.
5 105 The genetic algorithm will be com pared with the GAM S nonlinear system (w ith MINOS optim izer) as a typical exam ple of an industry-standard efficient m ethod of solution. T his system, being essentially a gradient-driven m ethod, found som e of the problem s set up difficult or im possible to solve. In these cases m odifications to the objective functions were m ade so that the m ethod could at least find an approximate solution. T he genetic system itself was w ritten by Z. M ichalewicz in the CT programming language. The param eters required include (as well as the problem description): number of iterations, population size, m utation and crossover rates, and random number starting seed. The behaviour of nonlinear optim ization algorithm s depend m arkedly on the form of the objective function. It is clear that different solution techniques m ay behave differently. In the experim ents the overall ob jective function is the sum of the arc objective m ncuons, thus there were no cross term s. Six different arc objective functions were used including: step function (w ith 5 equal steps): discretely changing slope (3 zones of a particular gradient); square; square-root: a function w ith a peak; and a linearly increasing sine function. T hey were used in conjunction w ith five problem structures in which the supply and dem and vectors and the corresponding parameter m atrix were randomly generated. T he solutions to these problem s are unknown. The objective function for the transportation problem was thus of the form / < * «) where f(x) is one of the six arc objective functions. Experiments and results The population size was fixed at 40. T he m utation rate was 20% w ith the proportion of mutation-1 being 50%, and the crossover rate was 5%. Problem s were run for 10,000 generations. The system s were tested on the six functions for five random ly generated cost matrices. See the second graph page for a graphical display of the results. The genetic system was run on SPARC sta tio n /S U N term inals while GAM S was run on an O livetti 386 with m ath co-processor. A lthough tim e com parisons between the two m achines are difficult to m ake it should be noted that in general GAM S finished each run well before the genetic system. An exception is case A (in which GAMS evaluates numerous arc-tangent functions) when the genetic algorithm took no more than 15 m inutes to com plete while GAM S averaged at about tw ice that. For cases A,B, and D. where the GAM S param eter m eant that m ultiple runs had to be performed to find the best G A M 6! solution, the genetic system overall was much faster. Conclusions The transportation problem was chosen as it provided a relatively sim ple convex feasible set. This means that it is easier to ensure feasibility in the solutions. The
6 106 procedure was then to look at the effects that the objective function alone lias on the solving of the problem. For the class of practical problem s the genetic system is, on average, better than G A M :.j_, ij% in case A and by 11.6% in case B. For the reasonable functions the results were different. In case C (the square function), the genetic system performed worse by 7.6% while in case D (the square-root function), the genetic system was worse by only... F r the other of functions the genetic system is dom inant. The genetic system resulted in im provem ents of 32.9% and 55.1% over GAM S, averaging over the five problems. T his dem onstrates the superiority of the genetic m ethod over other system s which are very often lim ited to certain classes of problem functions. G AM S did well on the sm ooth /m on otonic (reasonable) functions, it is these cases where the gradient m easurem ent techniques axe m ost apt. In case C GAMS bettered the genetic system with much less cost in solving tim e. For the practical problem s, the gradient techniques have difficulty seeing around the corner to new zones of better costs. The genetic algorithm, taking a more structural approach, is able to jum p betw een zones readily, resulting in much better solutions. For the other problems, although they are both =mooth, have significant structural features. Like the practical problem s, but even m ore so, the genetic system did much better the GAM S. REFER ENCES A ntonisse, J., A New Interpretation of Schem a Notation that Overturns the Binary Encoding Constraint, Proceedings of the Third International Conference on G enetic A lgorithm s (J. David Schaffer, editor), George Mason University, June pp D avis, L., (editor), G enetic A lgorithm s and Sim ulated A nnealing, Pitm an, L ondon Goldberg, D.E., G enetic Algorithm s in Search. O ptim ization and Machine Learning, Addison W esley, H olland, J., "A daptation in Natural <uiu A u ih cim S ystem s, Ann Arbor: University of M ichigan Press, M ichalew icz, Z., V ignaux, G.A., Hobbs, M. A G enetic A lgorithm for the Nonlinear Transportation Problem, subm itted to the O perations Research Society of A m erica (O R SA ) Journal on C om puting. Vignaux, G.A., M ichalewicz, Z., G enetic Algorithm s for the Transportation Problem, Proceedings of the 4th International Sym posium on M ethodologies for Intelligent System s, Charlotte, October 12-14, Vignaux, G.A., M ichalewicz, Z., A G enetic Algorithm for the Linear Transportation Problem, subm itted to IEEE Transactions on M an, System s, and Cybernetics.
Nov Julien Michel
Int roduct ion t o Free Ene rgy Calculat ions ov. 2005 Julien Michel Predict ing Binding Free Energies A A Recept or B B Recept or Can we predict if A or B will bind? Can we predict the stronger binder?
More informationLecture 9 Evolutionary Computation: Genetic algorithms
Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic
More informationCSC 4510 Machine Learning
10: Gene(c Algorithms CSC 4510 Machine Learning Dr. Mary Angela Papalaskari Department of CompuBng Sciences Villanova University Course website: www.csc.villanova.edu/~map/4510/ Slides of this presenta(on
More informationProcedures for Computing Classification Consistency and Accuracy Indices with Multiple Categories
esearcli R eport Semes 2 0 0 0 Procedures for Computing Classification Consistency and Accuracy Indices with Multiple Categories Won-Chan Lee Bradley A. Hanson Robert L. Brennan ACT O c t o b e r For additional
More informationPresented by Arkajit Dey, Matthew Low, Efrem Rensi, Eric Prawira Tan, Jason Thorsen, Michael Vartanian, Weitao Wu.
Presented by Arkajit Dey, Matthew Low, Efrem Rensi, Eric Prawira Tan, Jason Thorsen, Michael Vartanian, Weitao Wu. I ntroduction Transient Chaos Sim ulation and Anim ation Return Map I Return Map I I Modified
More informationLocal Search & Optimization
Local Search & Optimization CE417: Introduction to Artificial Intelligence Sharif University of Technology Spring 2017 Soleymani Artificial Intelligence: A Modern Approach, 3 rd Edition, Chapter 4 Outline
More informationApplying Metrics to Rule-Based Systems
Applying Metrics to Rule-Based Systems A Thesis by: Supervisor: Paul D oyle, B.Sc. M r. Renaat Verbruggen Subm itted to D ublin C ity U niversity C om puter A pplications for the degree o f M a s te r
More informationSearch. Search is a key component of intelligent problem solving. Get closer to the goal if time is not enough
Search Search is a key component of intelligent problem solving Search can be used to Find a desired goal if time allows Get closer to the goal if time is not enough section 11 page 1 The size of the search
More informationEvolutionary Computation
Evolutionary Computation - Computational procedures patterned after biological evolution. - Search procedure that probabilistically applies search operators to set of points in the search space. - Lamarck
More informationLocal Search & Optimization
Local Search & Optimization CE417: Introduction to Artificial Intelligence Sharif University of Technology Spring 2018 Soleymani Artificial Intelligence: A Modern Approach, 3 rd Edition, Chapter 4 Some
More informationGenetic Algorithm. Outline
Genetic Algorithm 056: 166 Production Systems Shital Shah SPRING 2004 Outline Genetic Algorithm (GA) Applications Search space Step-by-step GA Mechanism Examples GA performance Other GA examples 1 Genetic
More informationOPTIMISATION PROCESSES IN TIDAL ANALYSIS
OPTIMISATION PROCESSES IN TIDAL ANALYSIS by M. T. M u r r a y Liverpool Tid al In stitu te Introduction An analysis of tidal observations is norm ally carried out, not as an end in itself, but in order
More informationCS 331: Artificial Intelligence Local Search 1. Tough real-world problems
S 331: rtificial Intelligence Local Search 1 1 Tough real-world problems Suppose you had to solve VLSI layout problems (minimize distance between components, unused space, etc.) Or schedule airlines Or
More informationM A T R IX M U L T IP L IC A T IO N A S A T E C H N IQ U E O F P O P U L A T IO N AN A L Y S IS
M A T R IX M U L T IP L IC A T IO N A S A T E C H N IQ U E O F P O P U L A T IO N AN A L Y S IS N ATH A N KEYFITZ This paper takes advantage o f the fact that the changes in numbers o f a human (o r any
More informationIntrod uction to Num erical Analysis for Eng ineers
Introd uction to Num erical Analysis for Eng ineers System s of Linear Equations Cram er s Rule Gaussian Elim ination Num erical im plem entation Num erical stability: Partial Pivoting, Equilibration,
More informationLocal Beam Search. CS 331: Artificial Intelligence Local Search II. Local Beam Search Example. Local Beam Search Example. Local Beam Search Example
1 S 331: rtificial Intelligence Local Search II 1 Local eam Search Travelling Salesman Problem 2 Keeps track of k states rather than just 1. k=2 in this example. Start with k randomly generated states.
More informationDesigning the Human Machine Interface of Innovative Emergency Handling Systems in Cars
Designing the Human Machine Interface of Innovative Emergency Handling Systems in Cars M anfred D A N G E L M A IE R * and P etra M A N G O L ** *Fraunhofer IAO, N obelstr. 12, D -70569 Stuttgart, **IA
More informationAPPLICATION OF AUTOMATION IN THE STUDY AND PREDICTION OF TIDES AT THE FRENCH NAVAL HYDROGRAPHIC SERVICE
APPLICATION OF AUTOMATION IN THE STUDY AND PREDICTION OF TIDES AT THE FRENCH NAVAL HYDROGRAPHIC SERVICE by L. R o u m é g o u x Ingénieur H ydrographe en Chef, C hief of the T idal Section The considerable
More information(2009) Journal of Rem ote Sensing (, 2006) 2. 1 (, 1999), : ( : 2007CB714402) ;
100724619 (2009) 0220183207 Journal of Rem ote Sensing 1, 2, 3, 3, 3, 1 1., 100101; 2., 100049; 3., 100080 :,,, 3, ( ),1%,, :,,, : TP79 : A 1 20,,,,;, (, 1999),,,,, (, 2006),,,,, 2 2. 1 : 2007209217; :
More informationCrossover Techniques in GAs
Crossover Techniques in GAs Debasis Samanta Indian Institute of Technology Kharagpur dsamanta@iitkgp.ac.in 16.03.2018 Debasis Samanta (IIT Kharagpur) Soft Computing Applications 16.03.2018 1 / 1 Important
More informationChapter 8: Introduction to Evolutionary Computation
Computational Intelligence: Second Edition Contents Some Theories about Evolution Evolution is an optimization process: the aim is to improve the ability of an organism to survive in dynamically changing
More informationArtificial Intelligence (AI) Common AI Methods. Training. Signals to Perceptrons. Artificial Neural Networks (ANN) Artificial Intelligence
Artificial Intelligence (AI) Artificial Intelligence AI is an attempt to reproduce intelligent reasoning using machines * * H. M. Cartwright, Applications of Artificial Intelligence in Chemistry, 1993,
More informationOn the M in imum Spann ing Tree Determ ined by n Poin ts in the Un it Square
Vol. 14 No. CH INESE QUARTERLY JOURNAL OF M ATHEM ATICS June 1999 On the M in imum Spann ing Tree Determ ined by n Poin ts in the Un it Square Ye J ichang ( ) Xu Yinfeng ( ) Xu Chengxian ( ) (X iπan J
More informationIntelligens Számítási Módszerek Genetikus algoritmusok, gradiens mentes optimálási módszerek
Intelligens Számítási Módszerek Genetikus algoritmusok, gradiens mentes optimálási módszerek 2005/2006. tanév, II. félév Dr. Kovács Szilveszter E-mail: szkovacs@iit.uni-miskolc.hu Informatikai Intézet
More informationGENETIC ALGORITHM FOR CELL DESIGN UNDER SINGLE AND MULTIPLE PERIODS
GENETIC ALGORITHM FOR CELL DESIGN UNDER SINGLE AND MULTIPLE PERIODS A genetic algorithm is a random search technique for global optimisation in a complex search space. It was originally inspired by an
More informationLecture 15: Genetic Algorithms
Lecture 15: Genetic Algorithms Dr Roman V Belavkin BIS3226 Contents 1 Combinatorial Problems 1 2 Natural Selection 2 3 Genetic Algorithms 3 31 Individuals and Population 3 32 Fitness Functions 3 33 Encoding
More informationRepresentation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover
Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover Abstract The traditional crossover operator used in genetic search exhibits a position-dependent
More informationData Warehousing & Data Mining
13. Meta-Algorithms for Classification Data Warehousing & Data Mining Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 13.
More informationThe Story So Far... The central problem of this course: Smartness( X ) arg max X. Possibly with some constraints on X.
Heuristic Search The Story So Far... The central problem of this course: arg max X Smartness( X ) Possibly with some constraints on X. (Alternatively: arg min Stupidness(X ) ) X Properties of Smartness(X)
More informationChapter 5 Workshop on Fitting of Linear Data
Chapter 5 Workshop on Fitting of Linear Data (Contributed by E.L. Allen, SJSU) 5.0 Learning Objectives After successfully com pleting this laboratory workshop, including the assigned reading, the lab repot
More informationGenetic Algorithms: Basic Principles and Applications
Genetic Algorithms: Basic Principles and Applications C. A. MURTHY MACHINE INTELLIGENCE UNIT INDIAN STATISTICAL INSTITUTE 203, B.T.ROAD KOLKATA-700108 e-mail: murthy@isical.ac.in Genetic algorithms (GAs)
More informationTIDAL PREDICTION WITH A SMALL PERSONAL COMPUTER
International Hydrographic Review, Monaco, LXII (2), July 1985 TIDAL PREDICTION WITH A SMALL PERSONAL COMPUTER by A.S. F R A N C O 1*1 ABSTRACT This paper show s how a personal com puter with 16Kb only
More informationINVARIANT SUBSETS OF THE SEARCH SPACE AND THE UNIVERSALITY OF A GENERALIZED GENETIC ALGORITHM
INVARIANT SUBSETS OF THE SEARCH SPACE AND THE UNIVERSALITY OF A GENERALIZED GENETIC ALGORITHM BORIS MITAVSKIY Abstract In this paper we shall give a mathematical description of a general evolutionary heuristic
More informationV. Evolutionary Computing. Read Flake, ch. 20. Genetic Algorithms. Part 5A: Genetic Algorithms 4/10/17. A. Genetic Algorithms
V. Evolutionary Computing A. Genetic Algorithms 4/10/17 1 Read Flake, ch. 20 4/10/17 2 Genetic Algorithms Developed by John Holland in 60s Did not become popular until late 80s A simplified model of genetics
More informationLarge chunks. voids. Use of Shale in Highway Embankments
Use of Shale in Highway Embankments C. W. Lovell R esearch Engineer Joint Highway R esearch Project School of Civil E ngineering P urdue University IN T R O D U C T IO N Poor perform ance of m idw estern
More information2 Semester Final Exam Study Guide
Vista Murrieta High School Advanced Placem ent Biology G. Nicholas nd 2 Semester Final Exam Study Guide The following questions will potentially be on the final exam. Answer all questions in an outline
More informationImpact of Drink-drive Enforcement and Public Education Programs in Victoria, Australia
Impact of Drink-drive Enforcement and Public Education Programs in Victoria, Australia D avid H ealy, T ransport A ccident C om m ission B A C K G R O U N D In D ecem ber 1989, the T ransport A ccident
More informationSome Filtering Techniques for Digital Image Processing
Some Filtering Techniques for Digital Image Processing by Chi-M ing Leung l.l.sc. McMaster University, 1975 M. A. University of British Columbia, 1971 B.Sc. T h e Chinese U niversity of Hcng Kong, 1969
More informationChapter - 3. ANN Approach for Efficient Computation of Logarithm and Antilogarithm of Decimal Numbers
Chapter - 3 ANN Approach for Efficient Computation of Logarithm and Antilogarithm of Decimal Numbers Chapter - 3 ANN Approach for Efficient Computation of Logarithm and Antilogarithm of Decimal Numbers
More informationLOCAL SEARCH. Today. Reading AIMA Chapter , Goals Local search algorithms. Introduce adversarial search 1/31/14
LOCAL SEARCH Today Reading AIMA Chapter 4.1-4.2, 5.1-5.2 Goals Local search algorithms n hill-climbing search n simulated annealing n local beam search n genetic algorithms n gradient descent and Newton-Rhapson
More informationInteger weight training by differential evolution algorithms
Integer weight training by differential evolution algorithms V.P. Plagianakos, D.G. Sotiropoulos, and M.N. Vrahatis University of Patras, Department of Mathematics, GR-265 00, Patras, Greece. e-mail: vpp
More informationUsing Evolutionary Techniques to Hunt for Snakes and Coils
Using Evolutionary Techniques to Hunt for Snakes and Coils Abstract The snake-in-the-box problem is a difficult problem in mathematics and computer science that deals with finding the longest-possible
More informationA GA Mechanism for Optimizing the Design of attribute-double-sampling-plan
A GA Mechanism for Optimizing the Design of attribute-double-sampling-plan Tao-ming Cheng *, Yen-liang Chen Department of Construction Engineering, Chaoyang University of Technology, Taiwan, R.O.C. Abstract
More informationGenetic Algorithms and Genetic Programming Lecture 17
Genetic Algorithms and Genetic Programming Lecture 17 Gillian Hayes 28th November 2006 Selection Revisited 1 Selection and Selection Pressure The Killer Instinct Memetic Algorithms Selection and Schemas
More informationLight-absorbing capacity of phytoplankton in the Gulf of Gdańsk in May, 1987
Light-absorbing capacity of phytoplankton in the Gulf of Gdańsk in May, 1987 O C E A N O L O G IA, 28, 1990 P L ISSN 0078-3234 P h y to p lan k to n C hlorophyll L ight-absorbing capacity Pigm ent index
More informationScaling Up. So far, we have considered methods that systematically explore the full search space, possibly using principled pruning (A* etc.).
Local Search Scaling Up So far, we have considered methods that systematically explore the full search space, possibly using principled pruning (A* etc.). The current best such algorithms (RBFS / SMA*)
More informationThe effect of pollutants of crude oil origin on the diffusive reflectance of the ocean*
The effect of pollutants of crude oil origin on the diffusive reflectance of the ocean* O C E A N O L O G IA, 23, 1986 PL ISSN 0078-3234 R adiance Oil pollution Diffusive reflectance K r z y s z t o f
More informationV. Evolutionary Computing. Read Flake, ch. 20. Assumptions. Genetic Algorithms. Fitness-Biased Selection. Outline of Simplified GA
Part 5A: Genetic Algorithms V. Evolutionary Computing A. Genetic Algorithms Read Flake, ch. 20 1 2 Genetic Algorithms Developed by John Holland in 60s Did not become popular until late 80s A simplified
More informationThe M echanism of Factor VIII Inactivation by H um an Antibodies
ANNALS O F CLINICAL A N D LABORATORY SC IE N C E, Vol. 15, No. 1 Copyright 1985, Institute for Clinical Science, Inc. The M echanism of Factor VIII Inactivation by H um an Antibodies II. The Effect of
More informationEvolutionary computation
Evolutionary computation Andrea Roli andrea.roli@unibo.it DEIS Alma Mater Studiorum Università di Bologna Evolutionary computation p. 1 Evolutionary Computation Evolutionary computation p. 2 Evolutionary
More informationIV. Evolutionary Computing. Read Flake, ch. 20. Assumptions. Genetic Algorithms. Fitness-Biased Selection. Outline of Simplified GA
IV. Evolutionary Computing A. Genetic Algorithms Read Flake, ch. 20 2014/2/26 1 2014/2/26 2 Genetic Algorithms Developed by John Holland in 60s Did not become popular until late 80s A simplified model
More informationDesign Optimization of an Electronic Component with an Evolutionary Algorithm Using the COMSOL-MATLAB LiveLink
Design Optimization of an Electronic Component with an Evolutionary Algorithm Using the COMSOL-MATLAB LiveLink Eva Pelster 1,David Wenger,1 1 Wenger Engineering GmbH, Einsteinstr. 55, 8977 Ulm, mail@wenger-engineering.com
More informationFew thoughts on PFA, from the calorim etric point of view
1 Few thoughts on PFA, from the calorim etric point of view discussing pad siz e, M oliere radius, distance from the interaction point. H enri Videau 2 H ave you had anytim e a close view at show ers?
More informationO p tim ization o f P iezo electric A ctu a to r C onfigu ration on a F lexib le F in for V ib ration C ontrol U sin g G en etic A lgorith m s
O p tim ization o f P iezo electric A ctu a to r C onfigu ration on a F lexib le F in for V ib ration C ontrol U sin g G en etic A lgorith m s by A ndrew R ader, B.E ng. A Thesis subm itted to the Faculty
More informationOscar Cubo M edina fi.upm.es)
. euristics. reedy. LocalSearch. illimbing. Meta-heuristics. Tabu Search. Sim ulated nnealing Oscar ubo M edina (ocubo@ fi.upm.es) ased on Neighbourhood Search euristic derives from the verb heuriskein
More informationM a rtin H. B r e e n, M.S., Q u i T. D a n g, M.S., J o se p h T. J a in g, B.S., G reta N. B o y d,
The effect of a one for the road drink of hard liquor, beer or wine on peak breath alcohol concentration in a social drinking environment with food consumption M a rtin H. B r e e n, M.S., Q u i T. D a
More informationEffect of Methods of Platelet Resuspension on Stored Platelets
ANNALS O F CLINICAL AND LABORATORY S C IEN C E, Vol. 14, No. 5 Copyright 1984, Institute for Clinical Science, Inc. Effect of Methods of Platelet Resuspension on Stored Platelets THOMAS KIRALY, M.A., S.B.B.
More informationComputational Complexity and Genetic Algorithms
Computational Complexity and Genetic Algorithms BART RYLANDER JAMES FOSTER School of Engineering Department of Computer Science University of Portland University of Idaho Portland, Or 97203 Moscow, Idaho
More informationA GENETIC ALGORITHM FOR FINITE STATE AUTOMATA
A GENETIC ALGORITHM FOR FINITE STATE AUTOMATA Aviral Takkar Computer Engineering Department, Delhi Technological University( Formerly Delhi College of Engineering), Shahbad Daulatpur, Main Bawana Road,
More informationGenetic Algorithms. Donald Richards Penn State University
Genetic Algorithms Donald Richards Penn State University Easy problem: Find the point which maximizes f(x, y) = [16 x(1 x)y(1 y)] 2, x, y [0,1] z (16*x*y*(1-x)*(1-y))**2 0.829 0.663 0.497 0.331 0.166 1
More informationFundamentals of Genetic Algorithms
Fundamentals of Genetic Algorithms : AI Course Lecture 39 40, notes, slides www.myreaders.info/, RC Chakraborty, e-mail rcchak@gmail.com, June 01, 2010 www.myreaders.info/html/artificial_intelligence.html
More informationRotary D ie-cut System RD series. RD series. Rotary D ie-cut System
Rotary D ie-cut System RD series RD series Rotary D ie-cut System Si m p l e, h i g h q u a l i t y, c o m p a c t r o t a r y Di e -c u t s y s t e Improve your die cutting process! Easy change over the
More informationAn Effective Chromosome Representation for Evolving Flexible Job Shop Schedules
An Effective Chromosome Representation for Evolving Flexible Job Shop Schedules Joc Cing Tay and Djoko Wibowo Intelligent Systems Lab Nanyang Technological University asjctay@ntuedusg Abstract As the Flexible
More informationA WAY TO DEAL WITH THE PROJECT CRASHING PROBLEM
Hamdjatou Kane Gilbert Nkubili Département des sciences administratives Université du Quebec en Outaouais in Canada Barthelemy Ateme-Nguema Département des sciences de la gestion Université du Quebec en
More informationArtificial Intelligence Methods (G5BAIM) - Examination
Question 1 a) According to John Koza there are five stages when planning to solve a problem using a genetic program. What are they? Give a short description of each. (b) How could you cope with division
More informationThe Effectiveness of the «Checkpoint Tennessee» Program
The Effectiveness of the «Checkpoint Tennessee» Program John H. Lacey*, R alph K. Jones*and Jam es C. Fell** *M id-a m erica R esearch Institute, Shepherdstow n, W est V irginia U SA **N ational H ighw
More informationCOMBINATION OF TAGUCHI METHOD AND ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE OPTIMAL DESIGN OF FLAT-PLATE COLLECTORS
COMBINATION OF TAGUCHI METHOD AND ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE OPTIMAL DESIGN OF FLAT-PLATE COLLECTORS Soteris A. Kalogirou Cyprus University of Technology, P. O. Box 509, 60, Limassol, Cyprus
More informationEvolutionary Computation. DEIS-Cesena Alma Mater Studiorum Università di Bologna Cesena (Italia)
Evolutionary Computation DEIS-Cesena Alma Mater Studiorum Università di Bologna Cesena (Italia) andrea.roli@unibo.it Evolutionary Computation Inspiring principle: theory of natural selection Species face
More informationCrossover Gene Selection by Spatial Location
Crossover Gene Selection by Spatial Location ABSTRACT Dr. David M. Cherba Computer Science Department Michigan State University 3105 Engineering Building East Lansing, MI 48823 USA cherbada@cse.msu.edu
More informationThe Role of Crossover in Genetic Algorithms to Solve Optimization of a Function Problem Falih Hassan
The Role of Crossover in Genetic Algorithms to Solve Optimization of a Function Problem Falih Hassan ABSTRACT The genetic algorithm is an adaptive search method that has the ability for a smart search
More informationA Statistical Genetic Algorithm
A Statistical Genetic Algorithm Angel Kuri M. akm@pollux.cic.ipn.mx Centro de Investigación en Computación Instituto Politécnico Nacional Zacatenco México 07738, D.F. Abstract A Genetic Algorithm which
More informationDepartment of Mathematics, Graphic Era University, Dehradun, Uttarakhand, India
Genetic Algorithm for Minimization of Total Cost Including Customer s Waiting Cost and Machine Setup Cost for Sequence Dependent Jobs on a Single Processor Neelam Tyagi #1, Mehdi Abedi *2 Ram Gopal Varshney
More informationResearch Article A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems
Journal of Applied Mathematics Volume 2013, Article ID 757391, 18 pages http://dx.doi.org/10.1155/2013/757391 Research Article A Novel Differential Evolution Invasive Weed Optimization for Solving Nonlinear
More informationOptimization and Evaluation of Cardiac Enzym es and Isoenzym es M easured on a Random Access Analyzer
ANNALS OF CLINICAL AND LABORATORY SCIENCE, Vol. 15, No. 5 Copyright 1985, Institute for Clinical Science, Inc. Optimization and Evaluation of Cardiac Enzym es and Isoenzym es M easured on a Random Access
More informationC o r p o r a t e l i f e i n A n c i e n t I n d i a e x p r e s s e d i t s e l f
C H A P T E R I G E N E S I S A N D GROWTH OF G U IL D S C o r p o r a t e l i f e i n A n c i e n t I n d i a e x p r e s s e d i t s e l f i n a v a r i e t y o f f o r m s - s o c i a l, r e l i g i
More information22c:145 Artificial Intelligence
22c:145 Artificial Intelligence Fall 2005 Informed Search and Exploration III Cesare Tinelli The University of Iowa Copyright 2001-05 Cesare Tinelli and Hantao Zhang. a a These notes are copyrighted material
More informationBinary Particle Swarm Optimization with Crossover Operation for Discrete Optimization
Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization Deepak Singh Raipur Institute of Technology Raipur, India Vikas Singh ABV- Indian Institute of Information Technology
More informationС-4. Simulation of Smoke Particles Coagulation in the Exhaust System of Piston Engine
Kaliningrad 2012 Simulation of Smoke Particles Coagulation in the Exhaust System of Piston Engine С-4 Sergey M. Frolov1, Konstantin A. Avdeev1, Vladislav S. Ivanov1, Branislav Basara2, Peter Priesching2,
More informationNORTHW ESTERN UNIVERSITY. A Case S tudy of Three Universities A DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL
NORTHW ESTERN UNIVERSITY C onstructing T eaching Practices A round Novel Technologies: A Case S tudy of Three Universities A DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLM ENT O F THE
More informationLecture 22. Introduction to Genetic Algorithms
Lecture 22 Introduction to Genetic Algorithms Thursday 14 November 2002 William H. Hsu, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Readings: Sections 9.1-9.4, Mitchell Chapter 1, Sections
More informationChapter 4 Beyond Classical Search 4.1 Local search algorithms and optimization problems
Chapter 4 Beyond Classical Search 4.1 Local search algorithms and optimization problems CS4811 - Artificial Intelligence Nilufer Onder Department of Computer Science Michigan Technological University Outline
More informationTo link to this article:
This article was downloaded by: [Duke University Libraries] On: 21 May 2015, At: 09:26 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer
More informationProceedings of the Seventh Oklahoma Conference on Articial Intelligence, pp , November, Objective Function
Proceedings of the Seventh Oklahoma Conference on Articial Intelligence, pp. 200-206, November, 1993. The Performance of a Genetic Algorithm on a Chaotic Objective Function Arthur L. Corcoran corcoran@penguin.mcs.utulsa.edu
More informationZebo Peng Embedded Systems Laboratory IDA, Linköping University
TDTS 01 Lecture 8 Optimization Heuristics for Synthesis Zebo Peng Embedded Systems Laboratory IDA, Linköping University Lecture 8 Optimization problems Heuristic techniques Simulated annealing Genetic
More informationIntroduction to Evolutionary Computation
Introduction to Evolutionary Computation 1 Evolutionary Computation Different lines of investigation in simulated evolution GA (Genetic Algorithms) ES (Evolution Strategies) EP (Evolutionary Programming)
More informationPROBLEM SOLVING AND SEARCH IN ARTIFICIAL INTELLIGENCE
Artificial Intelligence, Computational Logic PROBLEM SOLVING AND SEARCH IN ARTIFICIAL INTELLIGENCE Lecture 4 Metaheuristic Algorithms Sarah Gaggl Dresden, 5th May 2017 Agenda 1 Introduction 2 Constraint
More informationImplicit Formae in Genetic Algorithms
Implicit Formae in Genetic Algorithms Márk Jelasity ½ and József Dombi ¾ ¾ ½ Student of József Attila University, Szeged, Hungary jelasity@inf.u-szeged.hu Department of Applied Informatics, József Attila
More informationHavrda and Charvat Entropy Based Genetic Algorithm for Traveling Salesman Problem
3 IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.5, May 008 Havrda and Charvat Entropy Based Genetic Algorithm for Traveling Salesman Problem Baljit Singh, Arjan Singh
More informationBoris Backovic, B.Eng.
SOURCE-CHANNEL CODEC FOR A WCDMA BASED MULTIMEDIA SYSTEM by Boris Backovic, B.Eng. Toronto, September 2005 A Project Report presented to Ryerson University in partial fulfillment of the requirements for
More informationUnit 1A: Computational Complexity
Unit 1A: Computational Complexity Course contents: Computational complexity NP-completeness Algorithmic Paradigms Readings Chapters 3, 4, and 5 Unit 1A 1 O: Upper Bounding Function Def: f(n)= O(g(n)) if
More informationCHAPTER 4 INTRODUCTION TO DISCRETE VARIABLE OPTIMIZATION
CHAPTER 4 INTRODUCTION TO DISCRETE VARIABLE OPTIMIZATION. Introduction.. Examples of Discrete Variables One often encounters problems in which design variables must be selected from among a set of discrete
More informationThermal Unit Commitment
Thermal Unit Commitment Dr. Deepak P. Kadam Department of Electrical Engineering, Sandip Foundation, Sandip Institute of Engg. & MGMT, Mahiravani, Trimbak Road, Nashik- 422213, Maharashtra, India Abstract:
More informationLSU Historical Dissertations and Theses
Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1976 Infestation of Root Nodules of Soybean by Larvae of the Bean Leaf Beetle, Cerotoma Trifurcata
More informationAuthor's personal copy
Physics Letters A 373 (2009) 2717 2721 Contents lists available at S c ienc ed irec t Physics Letters A w w w.elsevier.c om /loc ate/p la A search for the sim plest chaotic partial differential equation
More informationDrugs other than alcohol (medicines and illicit drugs) in people involved in fatal road accidents in Spain
Drugs other than alcohol (medicines and illicit drugs) in people involved in fatal road accidents in Spain F.J. A lv a r e z 1, M. S a n c h o 2, J. V ega3, M.C. D el R io 1, M.A. R a m s2, D. Q u e ip
More informationEvolving Presentations of Genetic Information: Motivation, Methods, and Analysis
Evolving Presentations of Genetic Information: Motivation, Methods, and Analysis Peter Lee Stanford University PO Box 14832 Stanford, CA 94309-4832 (650)497-6826 peterwlee@stanford.edu June 5, 2002 Abstract
More informationNAVIGATIONAL CHART OF THE NORTH ATLANTIC USING AN OBLIQUE CONFORMAL MAP PROJECTION
NAVIGATIONAL CHART OF THE NORTH ATLANTIC USING AN OBLIQUE CONFORMAL MAP PROJECTION fay M r. A ndre G o u g e n h e i m Ingenieur H ydrographe General de l re classe (Retd.) F o rm e r Director of the F
More informationGenetic Algorithms & Modeling
Genetic Algorithms & Modeling : Soft Computing Course Lecture 37 40, notes, slides www.myreaders.info/, RC Chakraborty, e-mail rcchak@gmail.com, Aug. 10, 2010 http://www.myreaders.info/html/soft_computing.html
More informationDetermination of Optimal Tightened Normal Tightened Plan Using a Genetic Algorithm
Journal of Modern Applied Statistical Methods Volume 15 Issue 1 Article 47 5-1-2016 Determination of Optimal Tightened Normal Tightened Plan Using a Genetic Algorithm Sampath Sundaram University of Madras,
More informationPDF hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The version of the following full text has not yet been defined or was untraceable and may differ from the publisher's version. For
More information