NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Kumara Sastry; David Goldberg; Graham Kendall
Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957;Bremermann, 1958;Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology.
Enrique Alba; Francisco Chicano
In this chapter we describe the basics of Genetic Algorithms and how they can be used to train Artificial Neural Networks.\\u000a Supervised training of Multilayer Perceptrons for classification problems is considered. We also explain how the Genetic Algorithm\\u000a can be hybridized with other algorithms and present two hybrids between it and two classical algorithms for the neural network\\u000a training: Backpropagation
DARRELL WHITLEY
1993-01-01
This tutorial covers the canonical genetic algorithm as well as more experimentalforms of genetic algorithms, including parallel island models and parallel cellular geneticalgorithms. The tutorial also illustrates genetic search by hyperplane sampling. Thetheoretical foundations of genetic algorithms are reviewed, include the schema theoremas well as recently developed exact models of the canonical genetic algorithm.Keywords: Genetic Algorithms, Search, Parallel Algorithms1 Introduction...
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms
Kjellström, Hedvig
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms
Kjellström, Hedvig
Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms #12;Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example #12;Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic
Genetic Algorithms Stephanie Forrest
Forrest, Stephanie
Genetic Algorithms Stephanie Forrest Dept. of Computer Science University of New Mexico Albuquerque algorithm is a computational model of biological evolution. Genetic algorithms are useful, both as search methods for solving problems and for modeling evolutionary sys tems. In genetic algorithms, binary
Genetic Algorithms Artificial Life
Forrest, Stephanie
Genetic Algorithms and Artificial Life Melanie Mitchell Santa Fe Institute 1660 Old Pecos Tr11072 Revised December 15, 1993 To appear in Artificial Life Abstract Genetic algorithms are computational and current scope of research on genetic algorithms in artificial life, using illustrative examples in which
Genetic Algorithms Artificial Life
Mitchell, Melanie
Genetic Algorithms and Artificial Life Melanie Mitchell Santa Fe Institute 1660 Old Pecos Tr artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, using illustrative examples in which the genetic algorithm is used to study how learning
M. Srinivas; Lalit M. Patnaik
1994-01-01
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we
Where Genetic Algorithms Excel
Eric B. Baum; Dan Boneh; Charles Garrett
2001-01-01
We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a
Genetic algorithm eclipse mapping
A. V. Halevin
2008-01-21
In this paper we analyse capabilities of eclipse mapping technique, based on genetic algorithm optimization. To model of accretion disk we used the "fire-flies" conception. This model allows us to reconstruct the distribution of radiating medium in the disk using less number of free parameters than in other methods. Test models show that we can achieve good approximation without optimizing techniques.
Fingerprint registration using genetic algorithms
Hany H. Ammar; Yongyi Tao
2000-01-01
In automated fingerprint identification systems, an efficient and accurate alignment algorithm in the preprocessing stage plays a crucial role in the performance of the whole system. We explore the use of genetic algorithms for optimizing the alignment of a pair of fingerprint images. To test its performance, we compare the implemented genetic algorithm with two other algorithms, namely, 2D and
Genetic Algorithms Genetic Programming
] F. H. Bennett III, J. R. Koza, D. Andre, and M. A. Keane, Evolution of a 60 Decibel OP Amp using) Configuration Bit Stream Bennett III #12;rossover mutation rossover Mutation crossover . crossover point transactions on evolutionary computation, vol3, no 3, pp. 220-235, september 1999. [2] Koza, J. R., Genetic
Genetic Algorithms and Local Search
NASA Technical Reports Server (NTRS)
Whitley, Darrell
1996-01-01
The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.
A Genetic Algorithm Tutorial Darrell Whitley
Whitley, Darrell
A Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling
A Genetic Algorithm Tutorial Darrell Whitley
Evett, Matthew
A Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University algorithm as well as more experimental formsof genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling
Genetic-Algorithm Programming Environments
José L. Ribeiro Filho; Philip C. Treleaven; Cesare Alippi
1994-01-01
Interest in Genetic algorithms is expanding rapidly. This paper reviews software environments for programming Genetic Algorithms (GAs). As background, we initially preview genetic algorithms' models and their programming. Next we classify GA software environments into three main categories: Application-oriented, Algorithm-oriented and Tool-Kits. For each category of GA programming environment we review their common features and present a case study of
A Process Algebra Genetic Algorithm
Karaman, Sertac
A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. ...
An introduction to genetic algorithms
Scott M. Thede
2004-01-01
A genetic algorithm is one of a class of algorithms that searches a solution space for the optimal solution to a problem. This search is done in a fashion that mimics the operation of evolution - a \\
Thinned arrays using genetic algorithms
Randy L. Haupt
1994-01-01
Large arrays are difficult to thin in order to obtain low sidelobes. Traditional statistical methods of aperiodic array synthesis fall far short of optimum configurations. Traditional optimization methods are not well suited for optimizing a large number of parameters or discrete parameters. This paper presents how to optimally thin an array using genetic algorithms. The genetic algorithm determines which elements
Genetic algorithm for information retrieval
Philomina Simon; S. Siva Sathya
2009-01-01
Retrieval of relevant documents from a collection is a tedious task. As genetic algorithms (GA) are robust and efficient search and optimization techniques, they can be used to search the huge document search space. In this paper, a general frame work of information retrieval system is discussed. The applicability of genetic algorithms in the field of information retrieval is also
9. Genetic Algorithms 9.1 Introduction
Cambridge, University of
66 9. Genetic Algorithms 9.1 Introduction The concept of evolution is prevalent in most biological to computational optimisation methods using ``genetic algorithms'' [50]. 9.2 Neural Networks and Genetic Algorithms
Elegance: Genetic Algorithms in Neural Reinforcement Control
Spronck, Pieter
Elegance: Genetic Algorithms in Neural Reinforcement Control Pieter Spronck Graduation committee: Genetic Algorithms in Neural Reinforcement Control. Graduation thesis (Master's degree). Delft University intelligence, genetic algorithms, neural control, neural networks, non-linear systems, reinforcement control
Scheduling Using Genetic Algorithms Ursula Fissgus
Scheduling Using Genetic Algorithms Ursula Fissgus Computer Science Department University Halle memory machine. We present a scheduling derivation step based on the genetic algorithm paradigm, data parallelism, genetic algorithms. 1 Introduction Several applications from scientific computing, e
Genetic algorithms as discovery programs
Hilliard, M.R.; Liepins, G.
1986-01-01
Genetic algorithms are mathematical counterparts to natural selection and gene recombination. As such, they have provided one of the few significant breakthroughs in machine learning. Used with appropriate reward functions and apportionment of credit, they have been successfully applied to gas pipeline operation, x-ray registration and mathematical optimization problems. This paper discusses the basics of genetic algorithms, describes a few successes, and reports on current progress at Oak Ridge National Laboratory in applications to set covering and simulated robots.
Scheduling with genetic algorithms
NASA Technical Reports Server (NTRS)
Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.
1994-01-01
In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.
On the Scalability of Simple Genetic Algorithms
Utrecht, Universiteit
On the Scalability of Simple Genetic Algorithms Dirk Thierens Department of Computer Science of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing and show
9. Genetic Algorithms 9.1 Introduction
Cambridge, University of
66 9. Genetic Algorithms 9.1 Introduction The concept of evolution is prevalent in most biological to computational optimisation methods using "genetic algorithms" [50]. 9.2 Neural Networks and Genetic Algorithms.1) with the function f being non-linear. Genetic algorithms (GAs) is one possible method of solving such a problem
Algorithms for Human Genetics Bonnie Beth Kirkpatrick
Kirkpatrick, Bonnie
Algorithms for Human Genetics by Bonnie Beth Kirkpatrick A dissertation submitted in partial Algorithms for Human Genetics, is ap- proved: Chair Date Date Date Date University of California, Berkeley #12;Algorithms for Human Genetics Copyright 2011 by Bonnie Beth Kirkpatrick #12;1 Abstract Algorithms
Genetic algorithm based tomographic flow visualization
Lyons, Donald Paul
1997-01-01
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 III EVOLUTIONARY ALGORITHMS FOR TOMOGRAPHIC FLOW VISUALIZATION . . . 18 I. II. III. IV. Genetic Algorithms . General Hybridization Schemes Concurrent Downhill Simplex Method. Hybrid Simplex Genetic Algorithms . . . . . . 1 8.... In section IV an alternative optimization technique is introduced for the purposes of developing a hybrid algorithm. Finally, section V discusses the details of the hybrid between the concurrent downhill simplex method and the Genetic Algorithm. I. Genetic...
Modeling Hybrid Genetic Algorithms Darrell Whitley
Whitley, Darrell
Modeling Hybrid Genetic Algorithms Darrell Whitley Computer Science Department, Colorado State University, Fort Collins, CO 80523 whitley@cs.colostate.edu 1 INTRODUCTION A ``hybrid genetic algorithm'' combines local search with a more traditional genetic algorithm. The most common form of hybrid genetic
K. Krishna; M. Narasimha Murty
1999-01-01
In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize
Genetic algorithm optimization of entanglement
Navarro-Mun'oz, Jorge C.; Rosu, H. C.; Lopez-Sandoval, R. [Potosinian Institute of Science and Technology, Apartado Postal 3-74 Tangamanga, 78231 San Luis Potosi (Mexico)
2006-11-15
We present an application of the genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation.
Genetic algorithm optimization of entanglement
Jorge C. Navarro-Munoz; H. C. Rosu; R. Lopez-Sandoval
2006-11-13
We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation
Niching Methods for Genetic Algorithms
Samir Mahfoud
1995-01-01
Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods
Irrigation Planning using Genetic Algorithms
K. Srinivasa Raju; D. Nagesh Kumar
2004-01-01
The present study deals with the application of Genetic Algorithms(GA) for irrigation planning. The GA technique is used to evolve efficient cropping pattern for maximizing benefits for an irrigation project in India. Constraints include continuity equation, land and water requirements, crop diversification and restrictions on storage. Penalty function approach is used to convert constrained problem into an unconstrained one. For
FINANCIAL FORECASTING USING GENETIC ALGORITHMS
Boetticher, Gary D.
reviews inductive machine learning and proceeds to cast the problem of financial time-series forecasting entitled Genetic Algorithms for Inductive Learning). Time-series forecasting is a special type of classification on which this study concentrates. Specifically, for any financial time series related
GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS
Havlicek, Joebob
. Particularly in applications involving financial time series, this can be a significant concern since managers recently to time series forecasting includes genetic algorithms (GA's) and evolutionary programming1 GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS STEPHEN D. SLOAN, RAYMOND W. SAW
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
Genetic Algorithm as an Attributes Selection Tool for Learning Algorithms
Halina Kwasnicka; Piotr Orski
2004-01-01
Learning algorithms, as NN or C4.5 require adequate sets of examples. In the paper we present the usability of genetic algorithms for selection significant features. Fitness of individuals is calculated on the basis of classification quality using NN or C4.5 algorithm. Results confirm that selected by GA significant features for C4.5 are also useful for NNs algorithm, but - what
An introduction to genetic algorithms for electromagnetics
Randy L. Haupt
1995-01-01
This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are “global” numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive
Genetic Algorithms Connecting evolution and learning
Indiana University
Genetic Algorithms · Connecting evolution and learning Apply evolutionary adaptation of Genetic Algorithms · Symbolic codes: each individual represented by a string · Search via biased sampling and a difficult search) · Crossover · Mutation #12;The Essential Genetic Algorithm #12;A Simple GA example #12
A Versatile Genetic Algorithm for Network Planning
Riedl, Anton
A Versatile Genetic Algorithm for Network Planning Anton Riedl Institute of Communication Networks, a new genetic algorithm is introduced which is used as a versatile tool for solving different types of optimization problems arising in the field of network planning. The genetic algorithm is applied to the minimum
Genetic Algorithms and Evolutionary Darrell Whitley
Whitley, Darrell
Genetic Algorithms and Evolutionary Computing Darrell Whitley Computer Science Department, Colorado State University Fort Collins, CO 80523 whitley@cs.colostate.edu 1 Introduction Genetic Algorithms are a family of computational models inspired by evolution. Other genetic and evolutionary algorithms include
An introduction to genetic algorithms
Kalyanmoy Deb
1999-01-01
Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization\\u000a methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to\\u000a various search and optimization problems in the recent past. In this paper, a brief description of a simple GA is presented.\\u000a Thereafter, GAs to handle
SURVEY OF GENETIC ALGORITHMS AND GENETIC PROGRAMMING John R. Koza
Fernandez, Thomas
SURVEY OF GENETIC ALGORITHMS AND GENETIC PROGRAMMING John R. Koza Computer Science Department://www-cs-faculty.stanford.edu/~koza/ ABSTRACT This paper provides an introduction to genetic algorithms and genetic programming and lists that is available over the Internet. 1. GENETIC ALGORITHMS John Holland's pioneering book Adaptation in Natural
New Results in Astrodynamics Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.
1998-01-01
Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.
An improved quantum clone genetic algorithm
Lihua Zhang; Liping Zhang; Haiyan Peng
2011-01-01
based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper .The algorithm have many merits:
Genetic Algorithms for Protein Folding Simulations
Ron Unger; John Moult
1993-01-01
Genetic algorithms methods utilize the same optimization procedures as natural genetic evolution, in which a population is gradually improved by selection. We have developed a genetic algorithm search procedure suitable for use in protein folding simulations. A population of conformations of the polypeptide chain is maintained, and conformations are changed bx mutation, in the form of conventional Monte Carlo steps,
Excursion-Set-Mediated Genetic Algorithm
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.
Genetic algorithms for DNA sequence assembly
Parsons, R.; Burks, C. (Los Alamos National Lab., NM (United States)); Forrest, S. (New Mexico Univ., Albuquerque, NM (United States). Dept. of Computer Science)
1993-04-13
This paper describes a genetic algorithm application to the DNA fragment assembly problems. The genetic algorithm uses a random key representation for representing the orderings of fragments. Two different fitness functions, both based on pairwise overlap strengths between fragments, were tested. The paper concludes that the genetic algorithm is a promising method for fragment assembly problems, achieving usable solutions quickly, but that the current fitness functions are flawed and that other representations might be more appropriate.
Cognitive Radio — Genetic Algorithm Approach
NASA Astrophysics Data System (ADS)
Reddy, Y. B.
2005-03-01
Cognitive Radio (CR) is relatively a new technology, which intelligently detects a particular segment of the radio spectrum currently in use and selects unused spectrum quickly without interfering the transmission of authorized users. Cognitive Radios can learn about current use of spectrum in their operating area, make intelligent decisions, and react to immediate changes in the use of spectrum by other authorized users. The goal of CR technology is to relieve radio spectrum overcrowding, which actually translates to a lack of access to full radio spectrum utilization. Due to this adaptive behavior, the CR can easily avoid the interference of signals in a crowded radio frequency spectrum. In this research, we discuss the possible application of genetic algorithms (GA) to create a CR that can respond intelligently in changing and unanticipated circumstances and in the presence of hostile jammers and interferers. Genetic algorithms are problem solving techniques based on evolution and natural selection. GA models adapt Charles Darwin's evolutionary theory for analysis of data and interchanging design elements in hundreds of thousands of different combinations. Only the best-performing combinations are permitted to survive, and those combinations "reproduce" further, progressively yielding better and better results.
Floating Entanglement Witness Measure and Genetic Algorithm
A. Baghbanpourasl; G. Najarbashi; M. Seyedkazemi
2007-08-27
In this paper based on the notion of entanglement witness, a new measure of entanglement called floating entanglement witness measure is introduced which satisfies some of the usual properties of a good entanglement measure. By exploiting genetic algorithm, we introduce a classical algorithm that computes floating entanglement witness measure. This algorithm also provides a method for finding entanglement witness for a given entangled state.
Training neural networks: backpropagation vs. genetic algorithms
M. N. H. Siddique; M. O. Tokhi
2001-01-01
There are a number of problems associated with training neural networks with backpropagation algorithm. The algorithm scales exponentially with increased complexity of the problem. It is very often trapped in local minima, and is not robust to changes of network parameters such as number of hidden layer neurons and learning rate. The use of genetic algorithms is a recent trend,
Thermal-Aware Floorplanning Using Genetic Algorithms
Wei-lun Hung; Yuan Xie; Narayanan Vijaykrishnan; Charles Addo-quaye; Theo Theocharides; Mary Jane Irwin
2005-01-01
In this work, we present a genetic algorithm based thermal-aware floorplanning framework that aims at reducing hot spots and distributing temperature evenly across a chip while optimizing the traditional design metric, chip area. The floorplanning problem is formulated as a genetic algorithm problem, and a tool called HotSpot is used to calculate floorplanning temperature based on the power dissipation, the
THE FUTURE AND APPLICATIONS OF GENETIC ALGORITHMS
Marcus Randall
This paper presents a method of producing solutions to difficult problems based on the laws of natural selection. The method, known as the genetic algorithm, is described in detail and applied to the c art pole c ontrol problem. The future of genetic algorithms is discussed in terms of potential commercial application.
New crossover operators in genetic algorithms
Yi Shang; Guo-Jie Li
1991-01-01
Two new crossover operators in genetic algorithms for solving some combinatorial problems with ordering are presented. One is enhanced order crossover (EOX). The other, GREE, is a heuristic crossover for a class of combinatorial optimization problems, such as traveling salesman problems (TSPs). Genetic algorithms using GREE as unique crossover run very fast and get good solutions. Combining GREE with EOX,
Improved genetic algorithms based optimum path planning for mobile robot
Soh Chin Yun; Veleppa Ganapathy; Lim Ooi Chong
2010-01-01
Improved genetic algorithms incorporate other techniques, methods or algorithms to optimize the performance of genetic algorithm. In this paper, improved genetic algorithms of optimum path planning for mobile robot navigation are proposed. An Obstacle Avoidance Algorithm (OAA) and a Distinguish Algorithm (DA) are introduced to generate the initial population in order to improve the path planning efficiency to select only
Algorithmics group, MPI for molecular genetics Delineation of protein
Spang, Rainer
Algorithmics group, MPI for molecular genetics Delineation of protein complexes Wasinee Rungsarityotin September 15, 2006 IMPRS Colloquium #12;Algorithmics group, MPI for molecular genetics Overview · Result · Conclusion and outlook #12;Algorithmics group, MPI for molecular genetics Protein complex
An Introduction to Genetic Algorithms and Evolution Strategies
Mehrdad Dianati; Insop Song; Mark Treiber
Genetic Algorithms and Evolution Strategies represent two of the three major Evolutionary Algorithms. This paper examines the history, theory and mathematical background, applications, and the current direction of bo th Genetic Algorithms and Evolution Strategies. Evolutionary Algorithms can be divided into three main areas of research: Genetic Algorithms (GA) (from which both Genetic Programming (which some researchers argue is a
Solving Maximal Clique Problem through Genetic Algorithm
NASA Astrophysics Data System (ADS)
Rajawat, Shalini; Hemrajani, Naveen; Menghani, Ekta
2010-11-01
Genetic algorithm is one of the most interesting heuristic search techniques. It depends basically on three operations; selection, crossover and mutation. The outcome of the three operations is a new population for the next generation. Repeating these operations until the termination condition is reached. All the operations in the algorithm are accessible with today's molecular biotechnology. The simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability.
Boyer, Edmond
A genetic algorithm for use in creative design processes 1 A Genetic Algorithm for use in Creative with natural growth mechanisms applied to architectural design processes. We implement a genetic algorithm.1 Integral evolutionary design There are numerous examples of evolutionary algorithms: genetic algorithms
An introduction to genetic algorithms for neural networks
Cambridge, University of
An introduction to genetic algorithms for neural networks Richard Kemp 1 Introduction Once a neural can use a genetic algorithm to try and solve the problem. What are genetic algorithms? Genetic algorithms (GAs) are search algo- rithms based on the mechanics of natural selection and genetics as observed
Refined genetic algorithm-economic dispatch example
Gerald B. Sheble; Kristin Brittig
1995-01-01
A genetic-based algorithm is used to solve a power system economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction,
Mobile Robot Path Planning Using Genetic Algorithms
Carlos E. Thomaz; Marco Aurélio Cavalcanti Pacheco; Marley B. R. Vellasco
1999-01-01
Genetic Algorithms (GAs) have demonstrated to be effective procedures for solving multi- criterion optimization problems. These algorithms mimic models of natural evolution and have the ability to adaptively search large spaces in near -optimal ways. One direct application of this intelligent technique is in the area of evolutionary robotics, where GAs are typically used for designing behavioral controllers for robots
Genetic Algorithms and the Immune System
Stephanie Forrest; Alan S. Perelson
1990-01-01
Using genetic algorithm techniques we introduce a model to examine the hypothesis that antibody and T cell receptor genes evolved so as to encode the information needed to recognize schemas that characterize common pathogens. We have implemented the algorithm on the Connection Machine for 16,384 64-bit antigens and 512 64-bit antibodies.
Genetic Algorithms and Supernovae Type Ia Analysis
C. Bogdanos; Savvas Nesseris
2009-06-29
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SnIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Genetic algorithms and supernovae type Ia analysis
Bogdanos, Charalampos; Nesseris, Savvas, E-mail: Charalampos.Bogdanos@th.u-psud.fr, E-mail: nesseris@nbi.dk [LPT, Universite de Paris-Sud-11, Bat. 210, 91405 Orsay CEDEX (France)] [LPT, Universite de Paris-Sud-11, Bat. 210, 91405 Orsay CEDEX (France)
2009-05-15
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) {identical_to} P{sub DE}/{rho}{sub DE}. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Exchange Rate Forecasting with Hybrid Genetic Algorithms
Jui-Fang Chang
\\u000a In recent years, Artificial Intelligence (AI) methods have proven to be successful tools for forecasting in the sectors of\\u000a business, finance, medical science and engineering. In this study, we employ a Genetic Algorithm (GA) to select the optimal\\u000a variable weights in order to predict exchange rates; subsequently, Genetic Algorithms, Particle Swam Optimization (PSO) and\\u000a Back Propagation Network (BPN) are utilized
Adaptive sensor fusion using genetic algorithms
Fitzgerald, D.S.; Adams, D.G.
1994-08-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.
Operator and parameter adaptation in genetic algorithms
Jim Smith; Terence C. Fogarty
1997-01-01
Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor\\u000a of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the\\u000a “population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which\\u000a has a scalar value attached
Genetic Algorithms To provide a background and understanding of basic genetic
Qu, Rong
Genetic Algorithms Objectives To provide a background and understanding of basic genetic algorithms and some of their applications. ·a basic genetic algorithm ·the basic discussion ·the applications of the algorithm #12;Genetic Algorithms 1859 Origin of the Species Survival of the Fittest #12;Genetic Algorithms
Refined genetic algorithm -- Economic dispatch example
Sheble, G.B.; Brittig, K. [Iowa State Univ., Ames, IA (United States)] [Iowa State Univ., Ames, IA (United States)
1995-02-01
A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.
A Genetic Algorithm System for Predicting Deniz Yuret
A Genetic Algorithm System for Predicting the OEX Deniz Yuret Michael de la Maza 1 Introduction. To that end, this article describes an application of genetic algorithms to predicting the OEX. 2 What is a genetic algorithm? Genetic algorithms were invented over twenty years ago by John Holland who drew upon
Artificial Neural Networks Lab 6A Genetic Algorithms
Duckett, Tom
Artificial Neural Networks Lab 6A Genetic Algorithms Purpose To study how a genetic algorithm can carefully before starting to solve it. Preparation Read the hand-out on genetic algorithms. Task 1, Implementation of a genetic algorithm In this task you have to solve a hard global optimization problem by using
A lowerbound result on the power of a genetic algorithm
A lowerbound result on the power of a genetic algorithm Kihong Park \\Lambda park@cs.bu.edu BU This paper presents a lowerbound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove
Overview of Information Security Using Genetic Algorithm and Chaos
Anil Kumar; M. K. Ghose
2009-01-01
Security, integrity, nonrepudiation, confidentiality, and authentication services are the most important factors in information security. Genetic algorithms (GAs) are a class of optimization algorithms. Many problems can be solved using genetic algorithms through modeling a simplified version of genetic processes. The application of a genetic algorithm to the field of cryptology is unique. Few works exist on this topic. In
Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Zheng, Chunmiao
A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost 2005 Abstract This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA
A hybrid of the genetic algorithm and concurrent simplex
Randolph, David Ethan
1995-01-01
1995 Major Subject: Computer Science A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis DAVID ETHAN RANDOLPH Submitted to Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... THE GENETIC ALGORITHM A. The Innards of the Genetic Algorithm. . . 1. A Toy Problem 2. The Works 3. A Second Toy Problem B. The Effectiveness of the Genetic Algorithm . C. Previous Genetic Algorithm Hybrids 1. Pipelining Hybrids . 2. Abstraction...
Genetic algorithms applied to optics and engineering
NASA Astrophysics Data System (ADS)
Cuevas, Francisco; Gonzalez, Otoniel; Susuki, Yamily; Hernandez, Daniel; Rocha, Martha; Alcala, Noé
2006-02-01
In the last years, Soft computing techniques, such as Genetic Algorithms, Neural Networks and Fuzzy systems, have been applied in different science areas. In this work, two applications of Genetic Algorithms in engineering and optics are presented. The Genetic Algorithms are optimization, search and learning machine techniques, which work in a random way. To achieve the problem solution by using of Genetic Algorithms, an iterative process should be developed. First, the problem to solve is modelled in a mathematical way by establishing of a fitness or objective function. After, a random initial population of strings (chromosomes) codifying problem solutions is generated, which samples the search solution space of the fitness function. Then, offspring populations are generated from previous one by using genetic operators: selection, crossover and mutation. In the selection process, possible solutions are chosen depending on their fitness function value. Then, in the crossover procedure, string segments of pairs of solutions are exchanged to generate the next population. Finally, some parameters in the offspring population are changed by mutation with a low probability. Results of the application of Genetic Algorithms to solve fringe analysis and nesting in finite materials problems are presented.
Using Genetic Algorithms to Optimize ACS-TSP
Marcin L. Pilat; Tony White
2002-01-01
We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve performance. Two modifications are proposed and tested. The first algorithm is a hybrid between ACS-TSP and a Genetic Algorithm that encodes experimental variables in ants. The algorithm does not yield improved results but oers concepts that can be used to improve the ACO algorithm. The
The Use of Genetic Algorithms in Multilayer Mirror Optimization
Hart, Gus
The Use of Genetic Algorithms in Multilayer Mirror Optimization by Shannon Lunt March 1999 of the Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 6 Flow chart of the Genetic Algorithm.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Genetic
A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann
Herrmann, Jeffrey W.
A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann Department of Mechanical-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine
VRP Based on Improved Niche Isolation Genetic Algorithm
Zixia Chen; Youshi Xuan
2007-01-01
With the problems that traditional genetic algorithm is easy to converge untimely, and its searching efficiency will be lower in later stage of evolution, the paper designs an improved niche isolation genetic algorithm. This algorithm is based on niche isolation genetic algorithm and adopts migrating operator and simulated annealing theory. It not only keeps the diversity of the group, but
A Combined Nelder-Mead Simplex and Genetic Algorithm
Nicolas Durand
It is usually said that genetic algorithm should be used when nothing else works. In practice, genetic algorithm are very often used for large sized global optimization problems, but are not very efficient for local optimization problems. The Nelder-Mead simplex algorithm has some common characteristics with genetic algorithm, but it can only find a local optimum close to the starting
A Distributed Pool Architecture for Genetic Algorithms
Roy, Gautam
2011-02-22
of multiple objectives. The development of this problem is still a work in progress and we anticipate in the future that the problem will become essentially so large and complex that using a distributed genetic algorithm will pay dividends. E. Fault... over multiple ?genera- tions?. The algorithm begins with a population of (typically random) individuals. At each iteration, the individuals are evaluated using a fitness function to select a subset. The cho- sen individuals are given the opportunity...
Facial Composite System Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zahradníková, Barbora; Duchovi?ová, So?a; Schreiber, Peter
2014-12-01
The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.
Genetic Algorithm Approaches for Actuator Placement
NASA Technical Reports Server (NTRS)
Crossley, William A.
2000-01-01
This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.
Gohar Vahdati; Sima Yaghoubian Ghouchani; Mahdi Yaghoobi
2010-01-01
In this paper, a hybrid search algorithm with Hopfield neural network (HNN) and Genetic algorithm (GA) is proposed. The HNN method is first used to generate valid solutions which are considered as solutions for initial population of genetic algorithm. Then, GA is used to perform exploitation around the best solution at each evaluation. The proposed algorithm has both the advantages
Shu-Xia Yang
2008-01-01
This paper proposes a organic hybrid model of the genetic algorithm and the particle swarm algorithm firstly, then establishes the multi-factor time series forecasting model, designs the BP neural networks, adopts the organic hybrid model of genetic algorithm and particle swarm algorithm to optimize the weight from the input layer to the hidden layer, the weight from the hidden layer
Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for
Kent, University of
Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm.ac.uk Abstract- Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part
Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach
Damla Turgut; Sajal K. Das; Ramez Elmasri; Begumhan Turgut
2002-01-01
We show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. In particular, we optimize our recently proposed weighted clustering algorithm (WCA). The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the
An investigation of messy genetic algorithms
NASA Technical Reports Server (NTRS)
Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley
1990-01-01
Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.
Applying a Genetic Algorithm to Reconfigurable Hardware
NASA Technical Reports Server (NTRS)
Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim
2004-01-01
This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.
Optimizing welding sequence with genetic algorithm
NASA Astrophysics Data System (ADS)
Kadivar, M. H.; Jafarpur, K.; Baradaran, G. H.
The genetic algorithm method has been utilized with a thermomechanical model to determine an optimum welding sequence. The thermomechanical model developed for this purpose predicts residual stress and distortion in thin plates. The thermal history of the plate is computed using a transient two-dimensional finite element model which serves as an input to the mechanical analysis. The mechanical response of the plate is estimated through a thermoelastic-viscoplastic finite element model. The proposed model is verified by comparison with the experimental data where available. By choosing the appropriate objective function for the considered case, an optimum welding sequence is determined by a genetic algorithm.
Hyperplane Ranking, Nonlinearity and the Simple Genetic Algorithm
Whitley, Darrell
Hyperplane Ranking, Nonlinearity and the Simple Genetic Algorithm Darrell Whitley Robert B ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking of their models of genetic algorithms (c.f. [9, 11, 12]). A critical question, then, is to what degree are genetic
Genetic algorithms in truss topological optimization
P. Hajela; E. Lee
1995-01-01
The present paper describes the use of a stochastic search procedure that is the basis of genetic algorithms, in developing near-optimal topologies of load-bearing truss structures. The problem addressed is one wherein the structural geometry is created from a specification of load conditions and available support points in the design space. The development of this geometry must satisfy kinematic stability
Reactive power optimization by genetic algorithm
K. Iba
1994-01-01
This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers\\/banks still remain as a difficult
Evolving Networks: Using the Genetic Algorithm
Belew, Richard K.
hybrids of neuralnetwork learning algo rithms with evolutionary search procedures, simply because NatureEvolving Networks: Using the Genetic Algorithm with Connectionist Learning Richard K. Belew John Mc, 1990 #12; Belew, McInerney & Schraudolf: Evolving Networks i Abstract It is appealing to consider
Convergence properties of simple genetic algorithms
NASA Technical Reports Server (NTRS)
Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.
1974-01-01
The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.
LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS
ALAN C. SCHULTZ
1994-01-01
Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. The approach to learning behaviors for robots described here reflects a particular methodology for learning via a simulation model. The motivation is that making mistakes on real systems may
Implementing continuous improvement using genetic algorithms
Petter Øgland
Purpose - On the metaphoric level, much as been written about complex adaptive systems (CAS) for implementing total quality management (TQM) and organizational learning (OL) in turbulent or unpredictable environments. The aim of this paper is to add practical insights on how a specific CAS-technique called genetic algorithms (GA) can be used for designing quality management systems for keeping the
Design space exploration using the genetic algorithm
Henrdk Esbensen; Ernest S. Kuh
1996-01-01
A typical VLSI layout problem involves the simultaneous optimization of a number of competing criteria. Rather than generating a single compromise solution, some recent approaches explicitly explores the design space and outputs a set of alternative solutions, thereby providing explicit information on the possible tradeoffs. This paper discuss the use of genetic algorithms (GAs) for design space exploration and propose
Lightweight telescope structure optimized by genetic algorithm
Mikio Kurita; Hiroshi Ohmori; Masashi Kunda; Hiroaki Kawamura; Noriaki Noda; Takayuki Seki; Yuji Nishimura; Michitoshi Yoshida; Shuji Sato; Tetsuya Nagata
2010-01-01
We designed the optics supporting structure (OSS) of a 3.8 m segmented mirror telescope by applying genetic algorithm optimization. The telescope is the first segmented mirror telescope in Japan whose primary mirror consists of 18 petal shaped segment mirrors. The whole mirror is supported by 54 actuators (3 actuators per each segment). In order to realize light-weight and stiff telescope
Improving tactical plans with genetic algorithms
Alan C. Schultz; John J. Grefenstette
1990-01-01
The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and uses genetic algorithms to search the space of decision policies. In the research presented here, the use of available
A Hybrid Genetic Algorithm for School Timetabling
Peter Wilke; Matthias Gröbner; Norbert Oster
2002-01-01
Hybrid Genetic Algorithms apply so called hybrid or repair operators or include problem specific knowledge about the problem domain in their mutation and crossover operators. These operators use local search to repair or avoid illegal or unsuitable assignments or just to improve the quality of the solutions already found.
System identification and control using genetic algorithms
Kristinn Kristinsson; Guy A. Dumont
1992-01-01
It is shown how genetic algorithms can be applied for system identification of both continuous and discrete time systems. It is shown that they are effective in both domains and are able to directly identify physical parameters or poles and zeros. This can be useful because changing one physical parameter might affect every parameter of a system transfer function. The
MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
Feature Subset Selection Using a Genetic Algorithm
Jihoon Yang; Vasant Honavar
1998-01-01
Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subset selection in the
Training Feedforward Neural Networks Using Genetic Algorithms
David J. Montana; Lawrence Davis
1989-01-01
Multilayered feedforward neural networks possess a number of properties which make them particu larly suited to complex pattern classification prob lems. However, their application to some real- world problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of
Convergence analysis of canonical genetic algorithms
Gunter Rudolph
1994-01-01
This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysis that a CGA will never converge to the global optimum regardless of the initialization, crossover, operator and objective function. But variants of CGA's that always maintain
PROGRAMING CNC MEASURING MACHINES BY GENETIC ALGORITHMS
Miran Brezocnik; Miha Kovacic; Joze Balic; Bogdan Sovilj
2004-01-01
The need for efficient and reliable tools for programming of CNC coordinate measuring machine is rapidly increasing in modern production. The proposed concept based on genetic algorithms assures generation and optimization of NC programs for measuring machine. Therefore the structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the tactile probe which performs simple elementary
A genetic algorithm for assembly line balancing
G. SURESH; V. V. VINOD; S. SAHU
1996-01-01
Assembly line balancing is a very important aspect in any mass production setup. However, finding the optimal balance is a very difficult proposition because of the computational complexity involved. Hence sub-optimal solutions are preferred over optimal solutions. In this work, a genetic algorithm (GA) is presented for obtaining good quality solutions for assembly line balancing problems. A major feature of
Genetic algorithms for modelling and optimisation
John McCall
2005-01-01
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory
Case-Based Initialization of Genetic Algorithms
Connie Loggia Ramsey; John J. Grefenstette
1993-01-01
In this paper, we introduce a case-based method of initializing genetic algorithms that are used to guide search in changing environments. This is incorporated in an anytime learning system. Any- time learning is a general approach to continuous learning in a changing environment. The agent's learning module continuously tests new strategies against a simulation model of the task environ- ment,
Adaptive system for generating neural networks using genetic algorithms
Armin Schneider
1995-01-01
An adaptive system is described which generates and trains neural networks using genetic algorithms. A genetic algorithm optimizes the network architecture trying to use as few connections as possible. The neurons of the networks generated by this algorithm are not necessarily organized in layers (except input and output). Because of this, classical algorithms for training neural networks can not be
An Evaluation of Local Improvement Operators Genetic Algorithms
Miller, John A.
exact algorithms. 1. Introduction Many traditional optimization algorithms suffer from myopia for highly. This is straightforward to do with genetic algorithms via the introduction of local improvement operators [Gold89An Evaluation of Local Improvement Operators for Genetic Algorithms John A. Miller+#, Walter D
Genetic Algorithms for Multiple-Choice Problems
NASA Astrophysics Data System (ADS)
Aickelin, Uwe
2010-04-01
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.
HARDWARE ACCELERATION OF MULTI-DEME GENETIC ALGORITHM
Qiu, Qinru
HARDWARE ACCELERATION OF MULTI-DEME GENETIC ALGORITHM FOR DNA CODEWORD SEARCHING Qinru Qiu1 and a hardware accelerator for accelerating the multi-deme genetic algorithm (GA) for the application of DNA
A Genetic CascadeCorrelation Learning Algorithm \\Lambda
George Mason University
A Genetic CascadeCorrelation Learning Algorithm \\Lambda Mitchell A. Potter Computer Science; however, in some applications gradient in formation may not be available. Biologically inspired genetic algorithms provide an alternative. Unfortunately, early attempts to use genetic algorithms to train connec
Genetic Algorithms Compared to Other Techniques for Pipe Optimization
Angus R. Simpson; Graeme C. Dandy; Laurence J. Murphy
1994-01-01
The genetic algorithm technique is a relatively new optimization tech- nique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization
A Simple Genetic Algorithm for Biomarker Mining Dusan Popovic1
A Simple Genetic Algorithm for Biomarker Mining Dusan Popovic1 , Alejandro Sifrim1 , Georgios A on a genetic algorithm with a novel fitness function and a bagging-like model aver- aging scheme. We signatures developed specially for the colon cancer case. Keywords. genetic algorithm, feature selection
Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm
1 Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm Michael L. Raymer Structural Analysis and Design Laboratory, Department of Biochemistry, 2 Genetic Algorithms Research among different pattern classes [4,5]. Genetic algorithms (GA's) have been applied to the problem
FINE-GRAINED PARALLEL GENETIC ALGORITHM: A STOCHASTIC OPTIMISATION METHOD
Bargiela, Andrzej
FINE-GRAINED PARALLEL GENETIC ALGORITHM: A STOCHASTIC OPTIMISATION METHOD A. Muhammad1 , A.King@solent.ac.uk Abstract This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control in the simulated annealing [Lundy'86, Otten'89, and Romeo'85]. The parallel genetic algorithm presented here
A Genetic Algorithm for Grammars James Anderson and Joe Staines
Goldschmidt, Christina
A Genetic Algorithm for Grammars James Anderson and Joe Staines July 1, 2010 Background training data. 1 #12;A Genetic Algorithm for Grammars Of course, there are many more grammars than be able to search heuristically. Project Proposal We propose a project which uses a genetic algorithm
A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett
Duckett, Tom
A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied. The fitness values in the genetic algorithm are obtained with a heuristic function that measures of the maps produced, and the search proceeds using a genetic algorithm (GA). GAs are a well-known search
GENETIC ALGORITHMS FOR PARTITIONING SETS WILLIAM A. GREENE
Greene, William A.
GENETIC ALGORITHMS FOR PARTITIONING SETS WILLIAM A. GREENE Computer Science Department University into subsets such that their sums are as nearly equal as possible. We devise a new genetic algorithm, Eager. Keywords: genetic algorithm, set partitioning, Equal Piles Problem, map coloring. 1. Introduction
Genetic algorithm dynamics on a rugged landscape Stefan Bornholdt*
Bornholdt, Stefan
Genetic algorithm dynamics on a rugged landscape Stefan Bornholdt* Institut fuÂ¨r Theoretische manuscript received 5 December 1997 The genetic algorithm is an optimization procedure motivated on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm
An Analysis of a Simple Genetic Algorithm Yuri Rabinovich
Wigderson, Avi
An Analysis of a Simple Genetic Algorithm Yuri Rabinovich Dept. of Computer Science, Hebrew for a simple, and yet nontrivial, family of genetic algorithms. 1 INTRODUCTION This paper originates optimiza tion. In Holland's [1] pioneering work it is sug gested that genetic algorithms are likely
lication of Genetic Algorithms for General Lotsizing Problems
Xie, Jinxing
82 lication of Genetic Algorithms for General Lotsizing Problems Xie Jinxing Tsinghua UEiversity,China Abstract: This paper presents an application of genetic algorithms for dynamic lotsizing problems, andor all the cost parameters can be time-varying. A genetic algorithm for the problems is introduced
Automatic Tuning of Agent-Based Models Using Genetic Algorithms
Paris-Sud XI, UniversitÃ© de
Automatic Tuning of Agent-Based Models Using Genetic Algorithms Beno^it Calvez and Guillaume on suggesting the use of genetic algorithms. The idea is to capture in the fitness func- tion the goal.) and to make the model automatically evolve in that direction. However the use of genetic algorithms (GA
A hybrid genetic algorithm for resolving closely spaced objects
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Lillo, W. E.; Schulenburg, N.
1995-01-01
A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.
Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem
Ferris, Michael C.
Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem Edward J optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their specialized use on our
GAPRUS -GENETIC ALGORITHMS BASED PIPE ROUTING USING TESSELLATED OBJECTS
Chen, Wei
GAPRUS - GENETIC ALGORITHMS BASED PIPE ROUTING USING TESSELLATED OBJECTS Sunand Sandurkar Software problems involving 3D freeform obstacles is demonstrated. Key words: Pipe Routing, Genetic Algorithms of CAD model as a connected array of triangles (tessellated format) GAPRUS Genetic Algorithm based Pipe
Distributed Genetic Algorithms with New Sharing Approach Multiobjective Optimization Problems
Coello, Carlos A. Coello
Distributed Genetic Algorithms with New Sharing Approach Multiobjective Optimization Problems@mail.doshisha.ac.jp sin@mikilab.doshisha.ac.jp 1 Abstract this paper, a new distributed genetic algorithm multiobjective and those in the relationship of tradeoff. genetic algorithm powerful timization methods based mechanics
Technical Report No. 494 Using Cyclic Genetic Algorithms
Portland State University
Technical Report No. 494 Using Cyclic Genetic Algorithms to Recon gure Hardware Controllers Indiana University Bloomington, Indiana 47405-4101 #12;Using Cyclic Genetic Algorithms to Recon gure for a small hexapod robot are generated by a cyclic genetic algorithm. From these automata a Xilinx net list
Determining Relative Importance and Best Settings for Genetic Algorithm Control
Determining Relative Importance and Best Settings for Genetic Algorithm Control Parameters A. L parameters of a classic genetic algorithm (GA). We plan to use these control settings to parameterize a GA findings are robust over 60 numeric optimization problems. Keywords Genetic algorithms, optimization
The Use of Genetic Algorithms in Multilayer Mirror Optimization
Hart, Gus
The Use of Genetic Algorithms in Multilayer Mirror Optimization Shannon Lunt R. S. Turley 2 Abstract We have applied the genetic algorithm to extreme ultraviolet (XUV) multilayer mirror optimization. We have adapted the genetic algorithm to design optimal bifunctional mirrors for the IMAGE
A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett
Duckett, Tom
A Genetic Algorithm for Simultaneous Localization and Mapping Tom Duckett Centre for Applied. The fitness values in the genetic algorithm are obtained with a heuristic function that measures of the maps produced, and the search proceeds using a genetic algorithm (GA). GAs are a wellknown search
An Analysis of a Simple Genetic Algorithm Yuri Rabinovich
Wigderson, Avi
An Analysis of a Simple Genetic Algorithm Yuri Rabinovich Dept. of Computer Science, Hebrew for a simple, and yet nontrivial, family of genetic algorithms. 1 INTRODUCTION This paper originates optimiza- tion. In Holland's [1] pioneering work it is sug- gested that genetic algorithms are likely
Towards a Genetic Algorithm for Function Optimization Sonja Novkovic
Towards a Genetic Algorithm for Function Optimization Sonja Novkovic and Davor Sverko Abstract: This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, such as non-coding segments, elitist selection and multiple crossover. Key words: Genetic algorithm, Royal
The Evolution of Understanding: A Genetic Algorithm Model of the
Levin, Michael
The Evolution of Understanding: A Genetic Algorithm Model of the Evolution of Communication Michael.edu Keywords: evolution of communication, genetic algorithm, self-organization #12;Abstract Much animal such understanding. Using a genetic algorithm implemented on a computer, I demonstrate that a significant though
Modeling the model Characteristics and behavior of genetic algorithms
Modeling the model Characteristics and behavior of genetic algorithms Author. Janeen Neri Progress in data files containing the fitness distribution and genetic algorithm specifications to be tested, and normalizes the fitness data for easier manipulation. A de- tailed pseudocode outline of the genetic algorithm
Ordering Autonomous Underwater Vehicle Inspection Locations with a Genetic Algorithm
Idaho, University of
Ordering Autonomous Underwater Vehicle Inspection Locations with a Genetic Algorithm Brandon Morton@acm.org Abstract--This paper describes a genetic algorithm for solving the traveling salesman problem (TSP (MOOS). The results show that the genetic algorithm performs significantly better than the approach
Applying Genetic Algorithm to Modeling Nonlinear Transfer Functions
Loyka, Sergey
Applying Genetic Algorithm to Modeling Nonlinear Transfer Functions Sergey L. Loyka Abstract- A genetic algorithm technique for the approximation of nonlinear transfer functions is proposed of this technique to behavioral-level simulation is also discussed. Keywords genetic algorithm, nonlinear transfer
Topology design of feedforward neural networks by genetic algorithms
Topology design of feedforward neural networks by genetic algorithms Slawomir W. Stepniewski 1 to achieve near optimal performance still remains a very challenging task. Genetic algorithms may be used, a genetic algorithm need not be limited to simply adjusting patterns of connections, but, for example, can
Combining genetic optimisation with hybrid learning algorithm for radial basis
Hefei Institute of Intelligent Machines
hidden neuron. GA and HLA: A general framework for the genetic algorithm (GA) has been described in [6Combining genetic optimisation with hybrid learning algorithm for radial basis function neural neural networks (RBFNN) is proposed. A genetic algorithm initially optimises the parameters of the RBFNN
Minimization of Multivalued Multithreshold Perceptrons Using Genetic Algorithms
Obradovic, Zoran
Minimization of Multivalued Multithreshold Perceptrons Using Genetic Algorithms Alioune Ngom, Ivan;s-perceptron, for some number of thresholds s. We propose a genetic algorithm to search for an op- timal k of thresholds. Experimental results show that the genetic algorithm nd optimal solutions in most cases. 1
Classification with Scaled Genetic Algorithms in a Coevolutionary Setting
Lothar M. Schmitt
2004-01-01
This work discusses asymptotic convergence of scaled genetic algorithms in a coevolutionary setting where the underlying population contains fixed numbers of creatures of various types. These types of crea- tures can act on each other in cooperative or competitive manner. The genetic algorithm uses common mutation and crossover operators as well as proportional fitness selection. By a scaled genetic algorithm,
Designing a fuzzy model by adaptive macroevolution genetic algorithms
Yo-Ping Huang; Sheng-Fang Wang
2000-01-01
In this paper the adaptive macroevolution genetic algorithms are proposed to identify three different types of fuzzy models. Several newly established techniques, such as adaptive choice function and macroevolution, are adopted into the simple genetic algorithms to improve the optimization capability. The genetic algorithms used here are controlled to retain the best solution in the population until a better one
Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?
NASA Astrophysics Data System (ADS)
Jones, Erika
2015-04-01
Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.
Cambridge, University of
Appendix A This is the documentation for the genetic algorithm program, as described in chapter 9. Program MAP_GENETIC_ALGORITHM 1. Provenance of code. 2. Purpose of code. 3. Specification. 4. Description, Cambridge, U.K. Application added: September 2002 Purpose An application of the genetic algorithm (GA
Genetic algorithms for minimal source reconstructions
Lewis, P.S.; Mosher, J.C.
1993-12-01
Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.
PSS Parameters Tuning Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Abdulrahim, M.; Almoula, Zakaria Fadl; Al-Hafid, Hafid
2008-10-01
Optimal tuning of power system stabilizer (PSS) parameters using genetic algorithm with single objective function is presented in this paper. A Single Machine Infinite Bus (SMIB) system is considered. The main objective of this research paper is to investigate the suitability of genetic algorithm for effective tuning of parameters of the power system stabilizer in a single machine infinite bus system. A conventional speed based lead-lag PSS is used. A simple and effective method of tuning the parameters of PSS is proposed which is posed as an optimization formulation by maximizing the damping of modes of oscillations of the SMIB system over a wide range of loading conditions and different system configurations. It is found that GA based PSS with single objective design shows improved dynamic performance over Conventional PSS over a wide range of operating conditions and different system parameters.
Application of Genetic Algorithms in Seismic Tomography
NASA Astrophysics Data System (ADS)
Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos
2010-05-01
In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the application of hybrid genetic algorithms in seismic tomography is examined and the efficiency of least squares and genetic methods as representative of the local and global optimization, respectively, is presented and evaluated. The robustness of both optimization methods has been tested and compared for the same source-receiver geometry and characteristics of the model structure (anomalies, etc.). A set of seismic refraction synthetic (noise free) data was used for modeling. Specifically, cross-well, down-hole and typical refraction studies using 24 geophones and 5 shoots were used to confirm the applicability of the genetic algorithms in seismic tomography. To solve the forward modeling and estimate the traveltimes, the revisited ray bending method was used supplemented by an approximate computation of the first Fresnel volume. The root mean square (rms) error as the misfit function was used and calculated for the entire random velocity model for each generation. After the end of each generation and based on the misfit of the individuals (velocity models), the selection, crossover and mutation (typical process steps of genetic algorithms) were selected continuing the evolution theory and coding the new generation. To optimize the computation time, since the whole procedure is quite time consuming, the Matlab Distributed Computing Environment (MDCE) was used in a multicore engine. During the tests, we noticed that the fast convergence that the algorithm initially exhibits (first 5 generations) is followed by progressively slower improvements of the reconstructed velocity models. Thus, to improve the final tomographic models, a hybrid genetic algorithm (GA) approach was adopted by combining the GAs with a local optimization method after several generations, on the basis of the convergence of the resulting models. This approach is shown to be efficient, as it directs the solution search towards a model region close to the global minimum solution.
Predicting mining activity with parallel genetic algorithms
Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.
2005-01-01
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Dynamic Parameter Encoding for Genetic Algorithms
Nicol N. Schraudolph; Richard K. Belew
1992-01-01
Thecommonuseofstaticbinaryplace-value codesforreal-valuedparametersofthephen- otype in Holland's genetic algorithm (GA) forceseitherthesacriflceofrepresentational precision for e-ciency of search or vice versa. Dynamic Parameter Encoding(DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from flxed-length binary genes to real values. DPE is shown to be empirically efiective and amenable to analysis; we
Parallel Genetic Algorithm for Alpha Spectra Fitting
Carlos J. García-Orellana; Pilar Rubio-Montero; Horacio González-Velasco
2005-01-01
We present a performance study of alpha-particle spectra fitting using parallel Genetic Algorithm (GA). The method uses a two-step approach. In the first step we run parallel GA to find an initial solution for the second step, in which we use Levenberg-Marquardt (LM) method for a precise final fit. GA is a high resources-demanding method, so we use a Beowulf
Selective Breeding in a Multiobjective Genetic Algorithm
Geoffrey T. Parks; I. Miller
1998-01-01
This paper describes an investigation of the efficacy of various elitist selection strategies in a multiobjective Genetic\\u000a Algorithm implementation, with parents being selected both from the current population and from the archive record of nondominated\\u000a solutions encountered during search. It is concluded that, because the multiobjective optimization process naturally maintains\\u000a diversity in the population, it is possible to improve the
Genetic algorithms for multiagent fusion system learning
NASA Astrophysics Data System (ADS)
Pigeon, Luc; Inglada, Jordi; Solaiman, Basel
2001-03-01
The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be presented as the system openness, the system evolution capabilities and the system performance. Multi- agent architecture can be designed in order to respect the first two efficiency constraints. As for the third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task. The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first. The need for such system and its parameters is explained. A brief overview of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.
A COMPARISON OF GENETIC ALGORITHMS AND OTHER MACHINE LEARNING SYSTEMS
Congdon, Clare Bates
disease research task, an inquiry into genetic and biochemical factors and their association with a family history machine learning approach investigated in this research is genetic algorithms (GA's); decision trees applica tions of genetic algorithms to various research questions in human genetics. This led to her
Using Genetic Algorithms in Secured Business Intelligence Mobile Applications
Silvia TRIF
2011-01-01
The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of
A genetic algorithm for the job shop problem
Federico Della Croce; Roberto Tadei; Giuseppe Volta
1995-01-01
In this paper we introduce a genetic algorithm whose peculiarities are the introduction of an encoding based on preference rules and an updating step which speeds up the evolutionary process. This method improves on the results gained previously with Genetic Algorithms and has shown itself to be competitive with other heuristics. The same algorithm has been applied to flow shop
Proposal of a Multilayer Shield Design Using Genetic Algorithm
Ricardo C. Souza; Glauco Fontgalland; Marcos A. Barbosa de Melo; Raimundo C. S. Freire; R. B. Vasconcelos
2005-01-01
In this paper, genetic algorithms were used in order to synthesize dielectric multilayer shields with symmetric structures and plane interfaces considering TEM waves with normal and oblique incidences. The genetic algorithm optimization objective is to obtain each layer thickness to get a suitable filter response related to scattering parameters. The implemented algorithm codes were created in Matlab software in a
Synthesize of multilayer passive structures using genetic algorithms
Ricardo C. Souza; Glauco Fontgalland; M. A. B. de Melo; R. C. S. Freire; R. B. Vasconcelos
2005-01-01
In this paper genetic algorithms were used in order to synthesize dielectric multilayer and symmetric passive structures with plane interfaces, considering TEM waves with normal and oblique incidences. The genetic algorithm optimization objective is to obtain each layer thickness to get a suitable filter response related to scattering parameters. The implemented algorithm codes were created in Matlab software in a
A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIMETABLING PROBLEM
D. ABRAMSON; J. ABELA
1992-01-01
Genetic algorithms (GA) have been applied to a number of optimisation problems with some success The algorithms mimic the process of natural selection, with the effect of creating a number of potentially optimal solutions to some complex search problem. One of the major disadvantages of genetic algorithms is that they are very slow. In this paper we discuss the application
A Parallel Genetic Algorithm for Solving the School Timetabling Problem
D. Abramson Ê
Genetic algorithms (GA) have been applied to a number of optimisation problems with some success (1). The algorithms mimic the process of natural selection, with the effect of creating a number of potentially optimal solutions to some complex search problem. One of the major disadvantages of genetic algorithms is that they are very slow. In this paper we discuss the
Genetic algorithm solution of economic dispatch with valve point loading
D. C. Walters; G. B. Sheble
1993-01-01
A genetics-based algorithm is proposed to solve an economic dispatch problem for valve point discontinuities. The algorithm utilizes payoff information of candidate solutions to evaluate their optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are circumvented. The formulations of an economic dispatch computer program using genetic algorithms are presented and the program's performances using two different encoding
An Indirect Genetic Algorithm for Set Covering Problems
Uwe Aickelin
2008-01-01
This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that
Optical Constants Determined by Genetic Algorithms
NASA Astrophysics Data System (ADS)
Smith, David Y.; Karstens, William; Malghani, Shaheen M.
2005-03-01
A recent determination^a of the complex refractive index, n(?) + i ?(?), of porous silicon employed a genetic^b algorithm to fit the Fresnel equations to reflectance spectra. The procedure appeared to involve more unknowns than explicit equations available for fitting, an indeterminate problem. However, the index values obtained were reasonable, and predicted the properties of porous-silicon multilayes. We have traced this success to the interpolation formulas used for n and ? in the fitting algorithm. They amount to an implicit optical-constant model with the de facto assumption of an analytic complex index that can be approximated by a cubic polynomial. Our analysis suggests the procedure can be improved by explicitly using a more appropriate model, e.g., one that uses wave number as the expansion variable and requires that n and ? be even and odd functions of ?, respectively. ^a V. Torres-Costa, R. J. Mart'in-Palma, and J. M. Mart'inez-Duart, J. Appl. Phys. 96, 4197 (2004). ^b D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, 1989).
Comparison of genetic algorithms with conjugate gradient methods
NASA Technical Reports Server (NTRS)
Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.
1972-01-01
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
Polychromator filter design with genetic algorithm
NASA Astrophysics Data System (ADS)
Oh, Seungtae; Park, Jiyoung
2015-02-01
In Thomson scattering (TS) diagnostics, polychromators are equipped with several optical band-pass filters which cover the spectral region where the radiation from the incident laser beam is expected to be Doppler shifted. The spectral location of the transmission band of individual filters has a strong influence on the measured electron temperature (Te) since the latter is derived from a previously computed lookup table including the spectral specifications of the filters. Here, we present the design of the set of polychromator filters through genetic algorithms (GAs). We examine the developed algorithm under two specific target conditions, and optimized filter sets covering the wavelength region longer than the wavelength of the incident laser seem to be more effective in improving the accuracy of the Te calculations provided by the diagnostic.
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
A genetic algorithm to minimize chromatic entropy
Durrett, Greg
We present an algorithmic approach to solving the problem of chromatic entropy, a combinatorial optimization problem related to graph coloring. This problem is a component in algorithms for optimizing data compression when ...
Removing the Genetics from the Standard Genetic Algorithm
Shumeet Baluja; Rich Caruana
1995-01-01
We present an abstraction of the genetic algorithm (GA), termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA's population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being simpler, both computationally and theoreti- cally, than the GA. Empirical results reported elsewhere show that PBIL is
Genetic algorithms for modelling and optimisation
NASA Astrophysics Data System (ADS)
McCall, John
2005-12-01
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.
Integrating Genetic Algorithm, Tabu Search Approach for Job Shop Scheduling
Thamilselvan, R
2009-01-01
This paper presents a new algorithm based on integrating Genetic Algorithms and Tabu Search methods to solve the Job Shop Scheduling problem. The idea of the proposed algorithm is derived from Genetic Algorithms. Most of the scheduling problems require either exponential time or space to generate an optimal answer. Job Shop scheduling (JSS) is the general scheduling problem and it is a NP-complete problem, but it is difficult to find the optimal solution. This paper applies Genetic Algorithms and Tabu Search for Job Shop Scheduling problem and compares the results obtained by each. With the implementation of our approach the JSS problems reaches optimal solution and minimize the makespan.
Genetic algorithm optimization applied to electromagnetics: a review
Daniel S. Weile; Eric Michielssen
1997-01-01
Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are
Serial and Parallel Genetic Algorithms as Function Optimizers
V. Scott Gordon; L. Darrell Whitley
1993-01-01
Parallel genetic algorithms are often very differentfrom the "traditional" genetic algorithmproposed by Holland, especially withregards to population structure and selectionmechanisms. In this paper we compare severalparallel genetic algorithms across a widerange of optimization functions in an attemptto determine whether these changes have positiveor negative impact on their problemsolvingcapabilities. The findings indicatethat the parallel structures perform as well asor ...
Training product unit neural networks with genetic algorithms
NASA Technical Reports Server (NTRS)
Janson, D. J.; Frenzel, J. F.; Thelen, D. C.
1991-01-01
The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.
Dominant takeover regimes for genetic algorithms
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler's device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solution reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.
GAMPMS: Genetic algorithm managed peptide mutant screening.
Long, Thomas; McDougal, Owen M; Andersen, Tim
2015-06-30
The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor-ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure-based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) ?3?2-isoform with a library of 64,000 ?-conotoxin (?-CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the ?-CTx MII peptide mutants with the ?3?2-nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 ?-CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening. © 2015 Wiley Periodicals, Inc. PMID:25975567
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
David E. Goldberg; Kalyanmoy Deb
1990-01-01
This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, rank- ing selection, tournament selection, and Genitor (or «steady state\\
Spang, Rainer
Max Planck Molecular Genetics: Algorithmics Group http://algorithmics.molgen.mpg.de Ivan G. Costa Filho Stefan Roepcke Alexander Schliep Computational Biology Department Max Planck Institute for Molecular Genetics, Berlin Analysis of Gene Expression Trees in Blood Cell Development #12;Max Planck
A Constructive Algorithm for the Training of a Multilayer Perceptron Based on the Genetic Algorithm
Hans Christian Andersen; Ah Chung Tsoi
1993-01-01
In this paper, we propose a genetic algorithm for the training and construction of amultilayer perceptron. It is based on the genetic algorithm and works on a layer-bylayerbasis. For each layer, it automatically chooses the number of neurons required,computes the synaptic weights between the present layer of neurons and the nextlayer, and gives a set of training patterns for the
An Anytime Algorithm for Scheduling of Aircraft Landing Times Using Genetic Algorithms \\Lambda
Ciesielski, Vic
between leaving wide safety margins between aircraft and maximizing the number of aircraft that take offAn Anytime Algorithm for Scheduling of Aircraft Landing Times Using Genetic Algorithms \\Lambda Vic of the computation time. We argue that for some kinds of problems, such as optimizing aircraft landing times, genetic
A Genetic Algorithm for the Point to Multipoint Routing Problem with Varying Number of Requests
Wainwright, Roger L.
A Genetic Algorithm for the Point to Multipoint Routing Problem with Varying Number of Requests Problem that uses a genetic algorithm and a heuristic Steiner tree algorithm. Our genetic algorithm allows to Multipoint Routing Problem that uses a genetic algorithm and a heuristic Steiner tree algorithm. Using both
INVESTIGATION OF IMAGE FEATURE EXTRACTION BY A GENETIC ALGORITHM
Steven P. Brumby; S. J. PERKINS
1999-01-01
We describe the implementation and performance of a genetic algorithm (GA) which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of
A New Challenge for Compression Algorithms: Genetic Sequences.
ERIC Educational Resources Information Center
Grumbach, Stephane; Tahi, Fariza
1994-01-01
Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…
Genetically derived fuzzy c-means clustering algorithm for segmentation
Nezamoddin N. Kachouie; Javad Alirezaie; Kaamran Raahemifar
2003-01-01
The proper classification of pixels is an important step in the realm of satellite imagery, to partition different land cover regions. This paper describes a clustering method that utilizes hard and fuzzy clustering algorithms. The performance of the algorithm is optimized using genetic algorithm, which searches the best cluster centers to initialize the fuzzy partition matrix in place of random
Genetic clustering algorithm for searching the nonspherically shaped clusters
NASA Astrophysics Data System (ADS)
Yang, Shiueng B.; Lee, Yi L.
2003-04-01
The K-means algorithm is a well-known method for searching the clustering. However, the K-means algorithm is suitable to find the clustering that contains compact spherical clusters. If the shape of clusters is not spherical, the K-means algorithm is failure to find the clustering result. Therefore, in this study, the genetic clustering algorithm is proposed to find the clustering whether the shape of clusters is spherical or not. Also, the genetic clustering algorithm can automatically find the number of clusters in the data set. Thus, the users need not to pre-dine the number of clusters in the data set. Experimental results show our proposed genetic clustering algorithm achieves better performance than the traditional clustering algorithms.
Proposal of Adjustment Type Genetic Algorithm for Knapsack Problem
NASA Astrophysics Data System (ADS)
Ida, Kenichi; Suga, Ryouhei; Gen, Mitsuo
The greedy algorithm is one of solution methods for knapsack problem. Although this algorithm does not necessarily obtain the optimal solution, it can obtain a good solution in short time. We consider this algorithm is very effective at judging the importance of each item. In this paper, we propose a new genetic algorithm for solving a knapsack problem. The algorithm can adjust a search area in consideration of the stability of each item which can obtain from the greedy algorithm. Moreover, we apply the proposed method to a multi-objective problem and a large-scale problem, and test the effectiveness.
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.
Dun-wei Gong; Guo-sheng Hao; Yong Zhou; Xiao-yan Sun
2007-01-01
Limitations of existing interactive genetic algorithms are analyzed and interactive genetic algorithms with multi-population adaptive hierarchy proposed. A model for interactive genetic algorithms with multi-population is established and a strategy for individuals’ migration is designed. Adaptive genetic operators are applied to interactive genetic algorithms with a single population, and when a condition for hierarchy is met, the algorithms will evolve
Terrainosaurus: realistic terrain synthesis using genetic algorithms
Saunders, Ryan L.
2007-04-25
Synthetically generated terrain models are useful across a broad range of applications, including computer generated art & animation, virtual reality and gaming, and architecture. Existing algorithms for terrain generation ...
Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling
NASA Technical Reports Server (NTRS)
Lohn, Jason; Colombano, Silvano
1997-01-01
We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.
LISA data analysis using genetic algorithms
Crowder, Jeff; Cornish, Neil J.; Reddinger, J. Lucas [Department of Physics, Montana State University, Bozeman, Montana 59717 (United States)
2006-03-15
This work presents the first application of the method of genetic algorithms (GAs) to data analysis for the Laser Interferometer Space Antenna (LISA). In the low frequency regime of the LISA band there are expected to be tens of thousands of galactic binary systems that will be emitting gravitational waves detectable by LISA. The challenge of parameter extraction of such a large number of sources in the LISA data stream requires a search method that can efficiently explore the large parameter spaces involved. As signals of many of these sources will overlap, a global search method is desired. GAs represent such a global search method for parameter extraction of multiple overlapping sources in the LISA data stream. We find that GAs are able to correctly extract source parameters for overlapping sources. Several optimizations of a basic GA are presented with results derived from applications of the GA searches to simulated LISA data.
Genetic algorithm solution of economic dispatch with valve point loading
Walters, D.C. (Eastman Chemical Co., Kingsport, TN (United States)); Sheble, G.B. (Iowa State Univ., Ames, IA (United States))
1993-08-01
A genetics-based algorithm is used to solve an economic dispatch problem for valve point discontinuities. The algorithm utilizes payoff information of candidate solutions to evaluate their optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are circumvented. The formulations of an economic dispatch computer program using genetic algorithms are presented and the program's performance using two different encoding techniques is compared. The results are verified for a sample problem using a dynamic programming technique.
Mining data streams with concept drifts using genetic algorithm
Periasamy Vivekanandan; Raju Nedunchezhian
Recent research shows that rule based models perform well while classifying large data sets such as data streams with concept\\u000a drifts. A genetic algorithm is a strong rule based classification algorithm which is used only for mining static small data\\u000a sets. If the genetic algorithm can be made scalable and adaptable by reducing its I\\/O intensity, it will become an
Further Research on Feature Selection and Classification Using Genetic Algorithms
William F. Punch III; Erik D. Goodman; Min Pei; Lai Chia-shun; Paul D. Hovland; Richard J. Enbody
1993-01-01
. This paper summarizes work onan approach that combines feature selectionand data classification using Genetic Algorithms.First, it describes our use of GeneticAlgorithms combined with a K-nearestneighbor algorithm to optimize classificationby searching for an optimal feature weighting,essentially warping the feature spaceto coalesce individuals within groups andto separate groups from one another. Thisapproach has proven especially useful withlarge data sets where standard
A Novel Quantum Genetic Algorithm for PID Controller
Jindong Wang; Rigui Zhou
2010-01-01
\\u000a Based on subpopulation parallel computing, a novel quantum genetic algorithm (NQGA) is presented. In the algorithm, each axis\\u000a of solution is divided into k parts, so m dimensional space is partitioned km subspaces, the individual (or chromosome) from\\u000a different subspace code differently. Finally, NQGA and the classical quantum genetic algorithm (QGA) are applied to parameter\\u000a optimization of PID controller, simulation
Minimizing multimodal functions by simplex coding genetic algorithm
ABDEL-RAHMAN HEDAR; MASAO FUKUSHIMA
2003-01-01
Combining meta-heuristics with local search methods is one approach that recently has drawn much attention to design more e-cient methods for solving continuous global optimization problems. In this paper, a new algorithm called Simplex Coding Genetic Algorithm (SCGA) is proposed by hybridizing genetic algorithm and simplex- based local search method called Nelder-Mead method. In the SCGA, each chromo- some in
Optimal reactive power dispatch using an adaptive genetic algorithm
Q. H. Wu; Y. J. Cao; J. Y. Wen
1998-01-01
This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions and the normalized fitness distances between the solutions in the evolution process to prevent premature convergence and
A hybrid genetic algorithm for the job shop scheduling problem
José Fernando Gonçalves; Jorge José De Magalhães Mendes; Maur??cio G. C. Resende
2005-01-01
This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local
Path planning using genetic algorithms for mini-robotic tasks
Víctor Ayala-ramírez; Arturo Pérez-garcía; F. J. Montecillo-puente; E. Martinez-labrada; Raúl Enrique Sánchez-yáñez
2004-01-01
We present a genetic algorithm-based method to optimize trajectory planning for mini-robotic tasks. Codifying a number of motion primitive parameters into computational chromosomes does this. Each trajectory is composed of a fixed number N of straight segments. We search with a genetic algorithm the length and direction parameters of the N path segments that let us to arrive a target
MultiObjective Optimization by Genetic Algorithms : A Review
Coello, Carlos A. Coello
MultiObjective Optimization by Genetic Algorithms : A Review Hisashi Tamaki Department of Electrical and Electronics Engineering, Kobe University, Rokkodai, Nadaku, Kobe 657, Japan. tamaki, Japan. kobayasi@int.titech.ac.jp Abstract--- This paper reviews several genetic algorithm (GA
Genetic Algorithm in Solution of Inverse Heat Conduction Problems
Miroslav Raudenský; Keith A. Woodbury; J. Kral; T. Brezina
1995-01-01
This report demonstrates the use of a genetic algorithm search in the solution of an inverse problem. The genetic algorithm is used to solve the one-dimensional inverse heat conduction problem using numerical data generated by solution of the corresponding direct problem. Both “pure” and noisy data are considered. If used with regularization, the method is shown to yield reasonable results
Delta Coding: An Iterative Search Strategy for Genetic Algorithms
L. Darrell Whitley; Keith E. Mathias; Patrick A. Fitzhorn
1991-01-01
A new search strategy for genetic algorithms is introducedwhich allows iterative searches with completereinitialization of the population preservingthe progress already made toward solving an optimizationtask. Delta coding is a simple searchstrategy based on the idea that the encoding usedby a genetic algorithm can express a distance awayfrom some previous partial solution. Delta valuesare added to a partial solution before evaluatingthe
A genetic algorithm solution to the unit commitment problem
S. A. Kazarlis; A. G. Bakirtzis; V. Petridis
1996-01-01
This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but
Use of a genetic algorithm for compact stellarator coil design
Use of a genetic algorithm for compact stellarator coil design W.H. Miner, Jr., P.M. Valanju Fusion than those obtained with traditional methods. A new coil design procedure which uses a genetic algorithm as the core optimization method is described and the resulting advanced coil designs presented. 1
Model-based image interpretation using genetic algorithms
Andrew Hill; Christopher J. Taylor
1992-01-01
We describe the application of genetic algorithms in model-based image inter- pretation. The delineation of left ventricular boundaries in apical 4-chamber echocardiograms is used as an illustrative exemplar. The suitability of genetic algorithms for the model\\/objective-function\\/search procedure is presented.
Adaptive probabilities of crossover and mutation in genetic algorithms
M. Srinivas; Lalit M. Patnaik
1994-01-01
In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, pc and
Classification Techniques of Neural Networks Using Improved Genetic Algorithms
Ming Chen; Zhengwei Yao
2008-01-01
Classification is an important problem in data mining. This paper focuses on a method of optimizing classifiers of neural network by Genetic Algorithm based on principle of gene reconfiguration, and implement classification by training the weight. The paper uses shift reverse logic crossover operation and the improved genetic algorithm The article using the typical method for optimizing BP neural network
Classification of MR and CT images using genetic algorithms
Zumray Dokur; Tamer Olmez; ErtugrUl Yazgan
1998-01-01
A modified restricted Coulomb energy (MoRCE) network trained by the genetic algorithm is presented. Each neuron of the network forms a closed region in the input space. The closed regions which are formed by the neurons overlap each other, like STAR. Genetic algorithms are used to improve the classification performances of the magnetic resonance (MR) and computer tomography (CT) images
Neuro-control of an inverted pendulum using Genetic Algorithm
Najib Metni
2009-01-01
The inverted pendulum is a highly nonlinear and open-loop unstable system. This means that standard linear techniques cannot exactly model and control the nonlinear dynamics of the system. This paper presents the neuro-control of an inverted pendulum using genetic algorithm. The system will be controlled via merging both neural networks and genetic algorithm. This paper focuses on training the neural
Classification of magnetic resonance images by using genetic algorithms
Ziimray Dokur; Tamer Olmez; E. Yazgan
1997-01-01
A neural network trained by genetic algorithms (GANN) is presented. Each neuron of the network forms a closed region in the input space. The closed regions which are formed by the neurons overlap each other, like STAR. Genetic algorithms are used to improve the classification performances of the magnetic resonance (MR) images with minimized number of neurons. GANN is examined
Human-Competitive Machine Intelligence by Means of Genetic Algorithms
Fernandez, Thomas
Human-Competitive Machine Intelligence by Means of Genetic Algorithms John R. Koza Section Holland's expectation that the genetic algorithm would have "applications to ... artificial intelligence" by showing examples of the automatic creation of human-competitive computer programs from a high
Linear Programming With Fuzzy Constraints by Genetic Algorithms
Feng-Tse Lin; Ming-Gar Lee
This paper shows how genetic algorithms provide a simple tool for solving fuzzy linear programming problem. In this study, we examine a formulation of the linear programming problem where the coefficients in the constraints may not be given precisely. We investigate the application of genetic algorithms for the possibility to solve fuzzy linear programming problem without the need to define
Convergence Analysis of Canonical Genetic Algorithms GUNTER RUDOLPH
Rudolph, GÃ¼nter
Convergence Analysis of Canonical Genetic Algorithms GUNTER RUDOLPH Abstract This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional chain analysis that a CGA will never converge to the global optimum regardless of the initialization
Generational PipeLined Genetic Algorithm (PLGA) using Stochastic Selection
De, Rajat Kumar
Generational PipeLined Genetic Algorithm (PLGA) using Stochastic Selection Malay K. Pakhira and Rajat K. De Abstract-- In this paper, a pipelined version of genetic algorithm, called PLGA (PLGA). A number of benchmark problems are used to compare the performances of conventional roulette
Dynamic scheduling of manufacturing job shops using genetic algorithms
George Chryssolouris; Velusamy Subramaniam
2001-01-01
Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule
Genetic algorithm for assembly line balancing
J. Rubinovitz; G. Levitin
1995-01-01
Research on single-model assembly line balancing has produced several good algorithms for solving large problems. The majority of these algorithms generate just one solution to the problem, whereas the real line design faces the need to investigate alternative solutions, where preferences for work allocation to stations are considered, or constraints other than technological precedence are taken into account. The MUST
Genetic algorithms for genetic neural nets. Research report
Sharp, D.H.; Reinitz, J.; Mjolsness, E.
1991-01-01
In contrast to most synthetic neural nets, biological neural networks have a strong component of genetic determination which acts before and during experiential learning. Three broad levels of phenomena are present: long-term evolution, involving crossover as well as point mutation; a developmental process mapping genetic information to a set of cells and their internal states of gene expression (genotype to phenotype); and the subsequent synaptogenesis. We describe a very simple mathematical idealization of these three levels which combines the crossover search method of genetic algorithms with the developmental models used in our previous work on 'genetic' or 'recursively generated' artificial neural nets and elaborated into a connectionist model of biological development. Despite incorporating all three levels (evolution on genes; development of cells; synapse formation) the model may actually be far cheaper to compute with than a comparable search directly in synaptic weight space.
Higher-Order Quantum-Inspired Genetic Algorithms
Robert Nowotniak; Jacek Kucharski
2014-07-02
This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order QIGAs quantum registers are used to represent genes strings which allows modelling of genes relations using quantum phenomena. Performance comparison has been conducted on a benchmark of 20 deceptive combinatorial optimization problems. It has been presented that using higher quantum orders is beneficial for genetic algorithm efficiency, and the new QIGA2 algorithm outperforms the old QIGA algorithm which was tuned in highly compute intensive metaoptimization process.
Smith, Alice E.
SOLVING THE REDUNDANCY ALLOCATION PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM reliability constraint. The genetic algorithm searches among candidate designs of the system configuration;2 SOLVING THE REDUNDANCY ALLOCATION PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM APPROACH
Julstrom, Bryant A.
Seeding the Population: Improved Performance in a Genetic Algorithm for the Rectilinear Steiner Problem Bryant A. Julstrom St. Cloud State University Keywords|Combinatorial optimization, rectilinear Steiner problem, genetic algorithms, seeding the pop- ulation. Abstract|A hybrid genetic algorithm
NASA Technical Reports Server (NTRS)
Rogers, David
1991-01-01
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
Suresh Chandra Satapathy; J. V. R. Murthy; P. V. G. D. Prasada Reddy
2007-01-01
Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper presents data clustering using improved genetic algorithm (IGA) and the popular Nelder-Mead(NM) Simplex search . To improve the accuracy of data clustering, an improved GA
Genetic algorithm for bundle adjustment in aerial panoramic stitching
NASA Astrophysics Data System (ADS)
Zhang, Chunxiao; Wen, Gaojin; Wu, Chunnan; Wang, Hongmin; Shang, Zhiming; Zhang, Qian
2015-03-01
This paper presents a genetic algorithm for bundle adjustment in aerial panoramic stitching. Compared with the conventional LM (Levenberg-Marquardt) algorithm for bundle adjustment, the proposed bundle adjustment combining the genetic algorithm optimization eliminates the possibility of sticking into the local minimum, and not requires the initial estimation of desired parameters, naturally avoiding the associated steps, that includes the normalization of matches, the computation of homography transformation, the calculations of rotation transformation and the focal length. Since the proposed bundle adjustment is composed of the directional vectors of matches, taking the advantages of genetic algorithm (GA), the Jacobian matrix and the normalization of residual error are not involved in the searching process. The experiment verifies that the proposed bundle adjustment based on the genetic algorithm can yield the global solution even in the unstable aerial imaging condition.
A Genetic Algorithm Approach for Technology Characterization
Galvan, Edgar
2012-10-19
lies in a new concept termed predicted dominance. The proposed algorithm uses fundamental concepts from multi-objective optimization and machine learning to generate a model of the technology frontier....
B. The Multi-objective Genetic Algorithm
Isaac Siwale
2015-03-05
or smoothness assumptions, and no constraint qualifications are necessary .... Rather than an algorithm that attempts to solve or is based on the complete KKT system in .... Second, the binary number ?´ is notionally converted into its base-10
Decision Support for Road Decommissioning and Restoration by Using Genetic Algorithms
Standiford, Richard B.
Decision Support for Road Decommissioning and Restoration by Using Genetic Algorithms and Dynamic are developed using dynamic programming and genetic algorithms. Each model accepts road survey data from
Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
Genetic-algorithm cancellation of sinusoidal powerline interference in electrocardiograms.
Kumaravel, N; Nithiyanandam, N
1998-03-01
The paper describes a method, based on a genetic algorithm, to remove sinusoidal powerline interference in electrocardiograms. There is a report on the use of the genetic algorithm to remove powerline interference for two different types of interference, powerline interference with frequency drift, and interference with frequency drift as well as third- harmonic distortion. The studies are conducted on electrocardiograms with simulated interference and also on actual noisy electrocardiogram records. The results obtained using the genetic algorithm in these cases of interference are presented. PMID:9684459
Genetic Algorithms for Open Shop Scheduling and ReScheduling Sushil J. Louis Zhijie Xu
Louis, Sushil J.
Genetic Algorithms for Open Shop Scheduling and ReScheduling Sushil J. Louis Zhijie Xu Department into the genetic algorithm's population to speed up and augment genetic search on a related open shop re system quickly finds better solutions than the genetic algorithm alone. Keywords: Genetic Algorithms
Research on Laser Marking Speed Optimization by Using Genetic Algorithm
Wang, Dongyun; Yu, Qiwei; Zhang, Yu
2015-01-01
Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831
GENETIC ALGORITHMS FOR A SINGLE-TRACK VEHICLE AUTONOMOUS PILOT
Vrajitoru, Dana
GENETIC ALGORITHMS FOR A SINGLE-TRACK VEHICLE AUTONOMOUS PILOT Dana Vrajitoru Intelligent Systems algorithms to an autonomous pilot designed for motorized single-track vehicles (motorcycles). The pilot-agents, autonomous pilot. 1 #12;1 Introduction Single track vehicles (STV) present somewhat different challenges than
Calculation of substructural analysis weights using a genetic algorithm.
Holliday, John D; Sani, Nor; Willett, Peter
2015-02-23
This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier. PMID:25615712
Maximum Entropy Spectral estimation based on accelerating genetic algorithm
Zhang Ming; Zhang Jian-yun; Jin Ju-liang; Wang Guo-qing; He Rui-min
2009-01-01
The purpose of this paper was to solve the problems of spectral peak shifting and line splitting existing in Burg's Maximum Entropy Spectral Analysis method (MESA), to enhance the resolution of entropy spectral, and to increase the adaptability of spectral estimation algorithm to signal length, signal noise ratio and initial phase. A method of accelerating Genetic algorithm based maximum Entropy
A Genetic Algorithm Method for Optical Wireless Channel Control
Matthew D. Higgins; Roger J. Green; Mark S. Leeson
2009-01-01
A genetic algorithm controlled multispot transmitter is proposed as an alternative approach to optimizing the power distribution for single element receivers in fully diffuse mobile indoor optical wireless communication systems. By specifically tailoring the algorithm, it is shown that by dynamically altering the intensity of individual diffusion spots, a consistent power distribution, with negligible impact on bandwidth and rms delay
Using Genetic Algorithms to Evolve Behavior in Cellular Automata
Thomas Bäck; Ron Breukelaar
2005-01-01
It is an unconventional computation approach to evolve so- lutions instead of calculating them. Although using evolutionary compu- tation in computer science dates back to the 1960s, using an evolutionary approach to program other algorithms is not that well known. In this pa- per a genetic algorithm is used to evolve behavior in cellular automata. It shows how this approach
A Pareto Frontier for Full Stern Submarines via Genetic Algorithm
Coello, Carlos A. Coello
by : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Justin E. Kerwin Professor of Naval Architecture Thesis Supervisor Accepted: Professor of Naval Architecture #12; #12; A Pareto Frontier for Full Stern Submarines via Genetic Algorithm Institute of Technology, 1996 Naval Engineer, Massachusetts Institute of Technology, 1996 Submitted
Novel geometry gradient coils for MRI designed by genetic algorithm
Williams, Guy Barnett
2001-06-19
This thesis concerns the design of gradient coils for magnetic resonance imaging systems. The method of design by genetic algorithm optimisation is applied to novel gradient geometries both by use of conventional computer facilities, and...
Mobile transporter path planning using a genetic algorithm approach
NASA Technical Reports Server (NTRS)
Baffes, Paul; Wang, Lui
1988-01-01
The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.
Reliability assessment of electrical power systems using genetic algorithms
Samaan, Nader Amin Aziz
2004-11-15
The first part of this dissertation presents an innovative method for the assessment of generation system reliability. In this method, genetic algorithm (GA) is used as a search tool to truncate the probability state space and to track the most...
Genetic Algorithms applications to optimization and system identification
Lin, Yun-Jeng
1998-01-01
Genetic Algorithms (GA) are very different from the traditional optimization techniques. GA is a new generation of artificial intelligence and its principles mimic the behavior of the biologic genes in the natural world. Its execution is simple...
An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions
Whitley, Darrell
and Darrell Whitley Anthony Kusuma and Christof Stork Department of Computer Science Advance Geophysical, Colorado 80209 mathiask/whitley@cs.colostate.edu kusuma/stork@advance.com Abstract Genetic algorithms have
Resolution of Overlapping Chromatographic Peaks Using a Genetic Algorithm
Wensheng Cai; Fang Yu; Xueguang Shao; Zhongxiao Pan
2000-01-01
A genetic algorithm for resolution of overlapping chromatographic peaks (GAROCP) using real-number coding, non-uniform mutation and arithmetical crossover methods is described in this paper. It was applied to resolution of highly overlapped multicomponent high-performance liquid chromatographic peaks by fitting experimental chromatogram to the exponentially modified Gaussian (EMG) model. The genetic algorithm was used to find the minimum of fitting error
Collaborative supply chain network using embedded genetic algorithms
C. Y. Lam; S. L. Chan; W. H. Ip; C. W. Lau
2008-01-01
Purpose – The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA-SCN), so as to increase the efficiency and effectiveness of a supply chain network. Design\\/methodology\\/approach – The methodologies of the GA-SCN are illustrated through a case study of a supply
Boiler-turbine control system design using a genetic algorithm
Robert Dimeo; Kwang Y. Lee
1995-01-01
This paper discusses the application of a genetic algorithm to control system design for boiler-turbine plant. In particular we study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a nonlinear multi-input\\/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller
Genetic-Algorithm Tool For Search And Optimization
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven
1995-01-01
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
Internal quantum efficiency analysis of solar cell by genetic algorithm
Xiong, Kanglin; Yang, Hui [Institute of Semiconductors, CAS, No. A35, Qing Hua East Road, Beijing 100083 (China); Suzhou Institute of Nano-tech and Nano-bionics, CAS, Ruoshui Road 398, Suzhou 215125 (China); Lu, Shulong; Zhou, Taofei; Wang, Rongxin; Qiu, Kai; Dong, Jianrong [Suzhou Institute of Nano-tech and Nano-bionics, CAS, Ruoshui Road 398, Suzhou 215125 (China); Jiang, Desheng [Institute of Semiconductors, CAS, No. A35, Qing Hua East Road, Beijing 100083 (China)
2010-11-15
To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of genetic algorithm is illustrated. (author)
A genetic algorithm for the set covering problem
J. E. Beasley; P. C. Chu
1996-01-01
In this paper we present a genetic algorithm-based heuristic for non-unicost set covering problems. We propose several modifications to the basic genetic procedures including a new fitness-based crossover operator (fusion), a variable mutation rate and a heuristic feasibility operator tailored specifically for the set covering problem. The performance of our algorithm was evaluated on a large set of randomly generated
Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos
Chun-Tian Cheng; Wen-Chuan Wang; Dong-Mei Xu; K. W. Chau
2008-01-01
Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity\\u000a and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence,\\u000a slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm\\u000a (CGA)
Method of mechanism synthesis by hybrid genetic algorithm
O'Neil, Robert Anthony
1999-01-01
METHOD OF MECHANISM SYNTHESIS BY HYBRID GENETIC ALGORITHM A Thesis by ROBERT ANTHONY O' NEIL Jr. Submitted to the Office of Graduate Studies of Texas A8 M University in the partial fulfillment of the requirements for the degree of MASTER... OF SCIENCE December 1999 Major Subject: Mechanical Engineering Method of Mechanism Synthesis by Hybrid Genetic Algorithm A Thesis by ROBERT ANTHONY O' NEIL Jr. Submitted to Texas A8 M University in partial fulfillment of the requirements...
3D Protein structure prediction with genetic tabu search algorithm
2010-01-01
Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256
An improved localization algorithm based on genetic algorithm in wireless sensor networks.
Peng, Bo; Li, Lei
2015-04-01
Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms. PMID:25852782
Acoustic lens design by genetic algorithms
A. Håkansson; J. Sánchez-Dehesa; L. Sanchis
2004-01-01
A survey of acoustic devices for focusing airborne sound is presented. We introduce a new approach to design high quality acoustic lenses based on arrays of cylindrical rigid scatterers in air. A population based stochastic search algorithm is used in conjunction with the multiple scattering theory to optimize a cluster of cylinders that focuses the sound in a prefixed focal
Genetic Algorithms for Real Parameter Optimization
Alden H. Wright
1991-01-01
This paper is concerned with the application of gen etic algorithms to optimization problems over several real parameters. It is shown that k-point crossover (for k small relative to the number of parameters) can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters. Mutation can also be co nsidered as a
OFFLINE HANDWRITING RECOGNITION USING GENETIC ALGORITHM
Shashank Mathur; Vaibhav Aggarwal; Himanshu Joshi; Anil Ahlawat
In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into
REPRESENTING RECTILINEAR STEINER TREES IN GENETIC ALGORITHMS
Julstrom, Bryant A.
of Computer Science St. Cloud State University 720 Fourth Avenue South St. Cloud, MN 56301 julstrom and with popula- tions seeded with a single chromosome that represented a short rectilinear Steiner tree. The algorithm identi- #12;ed much shorter trees using the weighted coding, and seeding the population improved
Chang-Wook Han; Jung-Il Park
2001-01-01
Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the integration of the genetic algorithm into the random signal-based learning employing simulated annealing which is used as an additional
Forecasting non-stationary financial time series through genetic algorithm
M. B. Porecha; P. K. Panigrahi; J. C. Parikh; C. M. Kishtawal; Sujit Basu
2005-07-18
We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm's predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.
The search for black hole binaries using a genetic algorithm
Antoine Petiteau; Yu Shang; Stanislav Babak
2009-08-25
In this work we use genetic algorithm to search for the gravitational wave signal from the inspiralling massive Black Hole binaries in the simulated LISA data. We consider a single signal in the Gaussian instrumental noise. This is a first step in preparation for analysis of the third round of the mock LISA data challenge. We have extended a genetic algorithm utilizing the properties of the signal and the detector response function. The performance of this method is comparable, if not better, to already existing algorithms.
Immune allied genetic algorithm for Bayesian network structure learning
NASA Astrophysics Data System (ADS)
Song, Qin; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.
hal-00164697,version1-16Feb2008 Anisotropic Selection in Cellular Genetic Algorithms
Paris-Sud XI, UniversitÃ© de
hal-00164697,version1-16Feb2008 Anisotropic Selection in Cellular Genetic Algorithms David In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection in cellular genetic algorithms (cGAs) which are a subclass of Genetic Algorithms where the population
The Genetic Algorithm is Useful to Fitting Input Probability Distributions for Simulation Models
Strelen, Christoph
The Genetic Algorithm is Useful to Fitting Input Probability Distributions for Simulation Models, Genetic Algorithm ABSTRACT The genetic algorithm can be applied to selecting theoretical probability the genetic algorithm, one can decide which one of some different families of prob- ability distributions
Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System
Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System Behrouz Minaei-Bidgoli1 , William F. Punch III 1 1 Genetic Algorithms Research and Applications Group (GARAGe to the learner as to how to best proceed. 2 Map the problem to Genetic Algorithm Genetic Algorithms have been
Nelson, Brent E.
Genetic Algorithms In Software and In Hardware --- A Performance Analysis Of Workstation and Custom implementation we found that a simple fourFPGA genetic algorithm design outperforms a state. 1.1 Genetic Algorithms Genetic algorithms are probabilistic search tech niques frequently applied
AUTONOMOUS ROBOT NAVIGATION USING A GENETIC ALGORITHM WITH AN EFFICIENT GENOTYPE ADITIA HERMANU
Wainwright, Roger L.
) by the same research team. This paper presents the research and simulation results of a genetic algorithm1 AUTONOMOUS ROBOT NAVIGATION USING A GENETIC ALGORITHM WITH AN EFFICIENT GENOTYPE STRUCTURE ADITIA such as genetic algorithms. An important part of the genetic algorithm solution is the structure of the genotype
Automatic Adjustments of a Michelson Interferometer Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Nosato, Hirokazu; Sasage, Toshimichi; Murakawa, Masahiro; Itatani, Taro; Higuchi, Tetsuya; Furuya, Tatsumi
This paper describes automatic adjustment of a michelson interferometer using genetic algorithms. The michelson interferometer consists of optical components (such as mirrors, lens, and prisms) that must be physically positioned with micron-meter precision to obtain optimal performance. Therefore, it is very difficult to use an interferometer outside for environmental measurement such as air pollution, because outside use causes mis-alignment of optical components. In order to overcome this problem, we propose automatic adjustment method using genetic algorithms that realize the optimal and quick alignment of optical components of interferometer. We also develop new compact mirror holder that allows portable and on-site use of interferometer. We confirmed the advantage of this system by the comparison with conventional adjustment algorithms. The proposed interferometer including the new compact mirror holders has been successfully adjusted by genetic algorithm in three minutes. The quick adjustment time indicates the possibility that the system can be used for on-site measurement.
Optimization of a genetic algorithm for searching molecular conformer space
NASA Astrophysics Data System (ADS)
Brain, Zoe E.; Addicoat, Matthew A.
2011-11-01
We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA - a meta-genetic algorithm - was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.
A Genetic Algorithm Approach for Technology Characterization
Galvan, Edgar
2012-10-19
, a kernel function is used to nonlinearly remap the training data into a higher-dimensional feature space where a hypersphere is a good model. There are several valid kernel functions common in the literature [21]. The proposed algorithm uses... the Gaussian kernel function ( ) ( ) ( ) ? ? (4) where ( ) is the nonlinear mapping from the data space to the feature space. The parameter determines how “tightly” or “loosely” the domain description is fit around...
Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.
ERIC Educational Resources Information Center
Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand
2003-01-01
Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Carlos M. Fonseca; Peter J. Fleming
1993-01-01
The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic
Fuzzy Information Retrieval Using Genetic Algorithms and Relevance Feedback.
ERIC Educational Resources Information Center
Petry, Frederick E.; And Others
1993-01-01
Describes an approach that combines concepts from information retrieval, fuzzy set theory, and genetic programing to improve weighted Boolean query formulation via relevance feedback. Highlights include background on information retrieval systems; genetic algorithms; subproblem formulation; and preliminary results based on a testbed. (Contains 12…
Genetic Algorithms for Gait Synthesis in a Hexapod Robot
Portland State University
Genetic Algorithms for Gait Synthesis in a Hexapod Robot M. Anthony Lewis, Andrew H. Fagg-legged walking robot. The CPG is composed of a network of neurons. In contrast to the main stream work in neural networks, the interconnection weights are altered by a Genetic Algo- rithm (GA), rather than a learning
Cyclic and Chaotic Behavior in Genetic Algorithms Alden H. Wright
Wright, Alden H.
Cyclic and Chaotic Behavior in Genetic Algorithms Alden H. Wright Computer Science Department University of Montana Missoula, MT 59812 USA wright@cs.umt.edu Alexandru Agapie Laboratory of Computational and Wright, 1994] and [Vose, 1999] for examples.) They have developed an elegant theory of simple genetic
Using Genetic Algorithm to Improve Information Retrieval Systems
Ahmed A. A. Radwan; Bahgat A. Abdel Latef; Abdel Mgeid; A. Ali; Osman A. Sadek
2006-01-01
This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and very flexible, than cosine similarity fitness function.
Genetic-Annealing Algorithm in Grid Environment for Scheduling Problems
NASA Astrophysics Data System (ADS)
Cruz-Chávez, Marco Antonio; Rodríguez-León, Abelardo; Ávila-Melgar, Erika Yesenia; Juárez-Pérez, Fredy; Cruz-Rosales, Martín H.; Rivera-López, Rafael
This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using simulated annealing within a Genetic Algorithm to solve the job shop scheduling problem. Experimental results of the algorithm obtained in the "Tarantula MiniGrid" are shown. Tarantula was implemented by linking two clusters from different geographic locations in Mexico (Morelos-Veracruz). The technique used to link the two clusters and configure the Tarantula MiniGrid is described. The effects of latency in communication between the two clusters are discussed. It is shown that the evolutionary algorithm presented is more efficient working in Grid environments because it can carry out major exploration and exploitation of the solution space.
A parallel genetic algorithm for the set partitioning problem
Levine, D. [Argonne National Lab., IL (United States). Mathematics and Computer Science Division.
1994-05-01
In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.
Optimization of experimental design in fMRI: a general framework using a genetic algorithm
Optimization of experimental design in fMRI: a general framework using a genetic algorithm Tor D uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect genetic algorithms may be applied to experimental design for fMRI, and we use the framework to explore
Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors
Mayer, Alexandre
Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors of Namur, Rempart de la Vierge 8, 5000 Namur, Belgium ABSTRACT We present a genetic algorithm (GA) we algorithms for addressing complex problems in physics. Keywords: genetic algorithm, optimization, light
Application of Genetic Algorithms to Molecular Biology: Locating Putative Protein Signal Sequences
Levin, Michael
Application of Genetic Algorithms to Molecular Biology: Locating Putative Protein Signal Sequences-7758 mlevin@husc.harvard.edu #12;Summary This paper presents an application of genetic algorithms to a problem difficult task. No good algorithm currently exists for locating brand new signals. A genetic algorithm
A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with
Wainwright, Roger L.
A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with Single Split Paths Words: Genetic Algorithm, Steiner Trees, Point to Multipoint Routing, Telecommunications Network to Multipoint Routing Problem with Single Split Paths. Our hybrid algorithm uses a genetic algorithm
Constrained minimization of smooth functions using a genetic algorithm
NASA Technical Reports Server (NTRS)
Moerder, Daniel D.; Pamadi, Bandu N.
1994-01-01
The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.
HW-SW partitioning based on genetic algorithm
Yi Zou; Zhenquan Zhuang; Huanhuan Chen
2004-01-01
HW-SW partitioning is an important problem in HW-SW codesign of embedded systems. We establish a HW-SW partitioning model based on system's basic scheduling block (BSB) graph and propose a modified genetic partitioning algorithm (MGPA). By adopting an adaptive fitness function definition and a novel evolving strategy, we enhance the stability, efficiency and result quality of our partitioning algorithm. Experiment results
Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR
Ashok Ajoy; Anil Kumar
2009-12-04
We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast convergence, and running on a MATLAB platform takes about 20 minutes on a standard personal computer to derive efficient pulse sequences for any target 8X8 matrix $U$.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-05-16
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.
Neural-network-assisted genetic algorithm applied to silicon clusters
Marim, L.R.; Lemes, M.R.; Pino, A. Jr. dal [Department of Physics, Instituto Tecnologico de Aeronautica, Pca. Marechal Eduardo Gomes, 50-Sao Jose dos Campos, Sao Paulo 12228-900 (Brazil)
2003-03-01
Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Si{sub n} (10{<=}n{<=}15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.
Distributed genetic algorithms for the floorplan design problem
NASA Technical Reports Server (NTRS)
Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.
1991-01-01
Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.
Rules extraction in short memory time series using genetic algorithms
NASA Astrophysics Data System (ADS)
Fong, L. Y.; Szeto, K. Y.
2001-04-01
Data mining is performed using genetic algorithm on artificially generated time series data with short memory. The extraction of rules from a training set and the subsequent testing of these rules provide a basis for the predictions on the test set. The artificial time series are generated using the inverse whitening transformation, and the correlation function has an exponential form with given time constant indicative of short memory. A vector quantization technique is employed to classify the daily rate of return of this artificial time series into four categories. A simple genetic algorithm based on a fixed format of rules is introduced to do the forecasting. Comparing to the benchmark tests with random walk and random guess, genetic algorithms yield substantially better prediction rates, between 50% to 60%. This is an improvement compared with the 47% for random walk prediction and 25% for random guessing method.
NASA Astrophysics Data System (ADS)
Zu, Yun-Xiao; Zhou, Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.
The multi-niche crowding genetic algorithm: Analysis and applications
Cedeno, W.
1995-09-01
The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.
Acoustic lens design by genetic algorithms
NASA Astrophysics Data System (ADS)
Håkansson, A.; Sánchez-Dehesa, J.; Sanchis, L.
2004-12-01
A survey of acoustic devices for focusing airborne sound is presented. We introduce a new approach to design high quality acoustic lenses based on arrays of cylindrical rigid scatterers in air. A population based stochastic search algorithm is used in conjunction with the multiple scattering theory to optimize a cluster of cylinders that focuses the sound in a prefixed focal point. Various lenses of different sized clusters, for different frequencies and with different focal lengths are presented. In general three focusing phenomena are remarked, focusing due to refraction, diffraction and focusing due to multiple scattering. The dependency on the frequency of the incident sound and the focal distance is analyzed indicating that higher frequencies and smaller focal distances favor larger amplifications in thin lenses based on multiple scattering. Furthermore, the robustness of a designed acoustic lens is studied, examining the focusing effect against errors in the cylinders’ positions and their radius.
Genetic algorithm approach to aircraft gate reassignment problem
Gu, Y.; Chung, C.A.
1999-10-01
The aircraft gate reassignment problem occurs when the departure of an incoming aircraft is delayed or a delay occurs in flight. If the delay is significant enough to delay the arrival of subsequent incoming aircraft at the assigned gate, the airline must revise the gate assignments to minimize extra delay times. This paper describes a genetic algorithm approach to solving the gate reassignment problem. By using a global search technique on quantified information, this genetic algorithm approach can efficiently find minimum extra delayed time solutions that are as effective or more effective than solutions generated by experienced gate managers.
Genetic Algorithm Modeling with GPU Parallel Computing Technology
Cavuoti, Stefano; Brescia, Massimo; Pescapé, Antonio; Longo, Giuseppe; Ventre, Giorgio
2012-01-01
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.
Acoustic design of rotor blades using a genetic algorithm
NASA Technical Reports Server (NTRS)
Wells, V. L.; Han, A. Y.; Crossley, W. A.
1995-01-01
A genetic algorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that genetic algorithm successfully searches for 'good' designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.
Forecasting summer rainfall over India using genetic algorithm
NASA Astrophysics Data System (ADS)
Kishtawal, C. M.; Basu, Sujit; Patadia, Falguni; Thapliyal, P. K.
2003-12-01
In this study we have assessed the feasibility of a nonlinear technique based on genetic algorithm for the prediction of summer rainfall over India. The genetic algorithm finds the equations that best describe the temporal variations of the seasonal rainfall over India, and therefore enables the forecasting of the future rainfall. The forecast equation developed in the present study uses the monthly mean rainfall during June, July, and August for past years over five rainfall homogeneous zones of India to predict the seasonal rainfall (JJA combined) over the Indian landmass.
Genetic algorithms and the search for viable string vacua
NASA Astrophysics Data System (ADS)
Abel, Steven; Rizos, John
2014-08-01
Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 1010 models, and yet a Genetic Algorithm can find them after constructing only 105 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.
Air data system optimization using a genetic algorithm
NASA Technical Reports Server (NTRS)
Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III
1992-01-01
An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.
Genetic algorithm dose minimization for an operational layout.
McLawhorn, S. L. (Steve L.); Kornreich, D. E. (Drew E.); Dudziak, Donald J.
2002-01-01
In an effort to reduce the dose to operating technicians performing fixed-time procedures on encapsulated source material, a program has been developed to optimize the layout of workstations within a facility by use of a genetic algorithm. Taking into account the sources present at each station and the time required to complete each procedure, the program utilizes a point kernel dose calculation tool for dose estimates. The genetic algorithm driver employs the dose calculation code as a cost function to determine the optimal spatial arrangement of workstations to minimize the total worker dose.
X. H. Shi; L. M. Wan; H. P. Lee; X. W. Yang; L. M. Wang; Y. C. Liang
2003-01-01
This paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the
OPTIMISATION OF TIME DOMAIN CONTROLLERS FOR SUPPLY SHIPS USING GENETIC ALGORITHMS
Fernandez, Thomas
OPTIMISATION OF TIME DOMAIN CONTROLLERS FOR SUPPLY SHIPS USING GENETIC ALGORITHMS AND GENETIC of this work is a study of the optimisation, using Genetic Algorithms, of controller designs based on a number the variety in the number of parameters to optimise and the controller structures, the Genetic Algorithm
Modeling Simple Genetic Algorithms for Permutation Darrell Whitley and NamWook Yoo
Whitley, Darrell
Modeling Simple Genetic Algorithms for Permutation Problems Darrell Whitley and NamWook Yoo@cs.colostate.edu Abstract An exact model of a simple genetic algorithm is developed for permutation based representations INTRODUCTION Several exact models of simple genetic algorithms have been introduced that assume the genetic
Lecture 15 Simulated Annealing and Genetic Algorithm Weinan E1,2
Li, Tiejun
Lecture 15 Simulated Annealing and Genetic Algorithm Weinan E1,2 and Tiejun Li2 1 Department, tieli@pku.edu.cn No.1 Science Building, 1575 #12;Introduction Simulated Annealing Genetic Algorithm Outline Introduction Simulated Annealing Genetic Algorithm #12;Introduction Simulated Annealing Genetic
Economic Dispatch Using Genetic Algorithm Based Hybrid Approach
Tahir Nadeem Malik; Aftab Ahmad [University of Engineering and Technology, Taxila (Pakistan); Shahab Khushnood [National Power Construction Corporation - NPCC, 9-Shadman II, Lahore -54000 (Pakistan)
2006-07-01
Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)
Selection of relevant features in a fuzzy genetic learning algorithm.
Gonzalez, A; Perez, R
2001-01-01
Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working with large databases the search space is too large and the running time can sometimes be excessive. We propose to improve SLAVE by including a feature selection model in which the genetic algorithm works with individuals (representing individual rules) composed of two structures: one structure representing the relevance status of the involved variables in the rule, the other one representing the assignments variable/value. For this general representation, we study two alternatives depending on the information coded in the first structure. When compared with the initial algorithm, this new approach of SLAVE reduces the number of rules, simplifies the structure of the rules and improves the total accuracy. PMID:18244806
The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm
Ahmed, Zakir Hussain
2014-01-01
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148
Selection Intensity in Genetic Algorithms with Generation Gaps
Cantu-Paz, E.
2000-01-19
This paper presents calculations of the selection intensity of common selection and replacement methods used in genetic algorithms (GAs) with generation gaps. The selection intensity measures the increase of the average fitness of the population after selection, and it can be used to predict the average fitness of the population at each iteration as well as the number of steps until the population converges to a unique solution. In addition, the theory explains the fast convergence of some algorithms with small generation gaps. The accuracy of the calculations was verified experimentally with a simple test function. The results of this study facilitate comparisons between different algorithms, and provide a tool to adjust the selection pressure, which is indispensable to obtain robust algorithms.
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
An Adaptive Immune Genetic Algorithm for Edge Detection
NASA Astrophysics Data System (ADS)
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Stochastic search in structural optimization - Genetic algorithms and simulated annealing
NASA Technical Reports Server (NTRS)
Hajela, Prabhat
1993-01-01
An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.
Genetic algorithms for assembly line balancing with various objectives
Y Yeo Keun Kim; Yong Ju Kim; Yeongho Kim
1996-01-01
This article presents genetic algorithms (GAs) to solve assembly line balancing (ALB) problems with various objectives: 1.(1) minimizing number of workstations;2.(2) minimizing cycle time;3.(3) maximizing workload smoothness;4.(4) maximizing work relatedness; and5.(5) a multiple objective with (3) and (4).Some major aspects of the proposed GAs are discussed, with emphasis on representation, decoding and genetic operators. A repair method is newly developed
Minimizing multimodal functions by simplex coding genetic algorithm
Abdel-Rahman Hedar; Masao Fukushima
2003-01-01
Combining meta-heuristics with local search methods is one approach that recently has drawn much attentionto design more efficient methods for solving continuous global optimization problems. In this article, a newalgorithm called Simplex Coding Genetic Algorithm (SCGA) is proposed by hybridizing genetic algorithmand dmplex-based local search method called Nelder-Mead method. In the SCGA, each chromosome inthe population is a simplex and
NASA Astrophysics Data System (ADS)
Windarto, Indratno, S. W.; Nuraini, N.; Soewono, E.
2014-02-01
Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The algorithm begins by defining the optimization variables, defining the cost function (in a minimization problem) or the fitness function (in a maximization problem) and selecting genetic algorithm parameters. The main procedures in genetic algorithm are generating initial population, selecting some chromosomes (individual) as parent's individual, mating, and mutation. In this paper, binary and continuous genetic algorithms were implemented to estimate growth rate and carrying capacity parameter from poultry data cited from literature. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, both algorithms can estimate these parameters well. Suitable range for mutation rate in continuous genetic algorithm is wider than the binary one.
A GENETIC ALGORITHM APPROACH TO DESIGN EVOLUTION USING DESIGN
Waterloo, University of
A GENETIC ALGORITHM APPROACH TO DESIGN EVOLUTION USING DESIGN PATTERN TRANSFORMATION Mehdi AMOUI 1 software transfor- mations in a form of GOF patterns to UML design model and evalu- ated the quality of the transformed design according to Object-Oriented metrics, particularly 'Distance from the Main Sequence
A genetic algorithm based method for product family design optimization
Bryan DSouza; Timothy W. Simpson
2003-01-01
Increased commonality in a family of products can simplify manufacturing and reduce the associated costs and lead-times. There is a tradeoff, however, between commonality and individual product performance within a product family, and this paper introduces a genetic algorithm based method to help find an acceptable balance between commonality in the product family and desired performance of the individual products
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1996-12-31
This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
An Experimental Application of Learnable Evolution Model and Genetic Algorithms
Michalski, Ryszard S.
an application of LEM1, a preliminary implementation of Learnable Evolution Model (LEM), and two canonical genetic algorithms, GA1 and GA2, to parameter estimation in digital signal filter design. LEM1 alternates of a population has not improved sufficiently during one mode, LEM1 switches to another mode. LEM1 alternates
Inference of gene regulatory model by genetic algorithms
HITOSHI IBA; Shin Ando
2001-01-01
Presents an application of genetic algorithms (GAs) to the gene network inference problem; this is one of the active topics in recent bioinformatics. The objective is to predict a regulating network structure of the interacting genes from the observed outcome, i.e. expression pattern. The task consists of modeling the rules of regulation and inferring the network structure from the observed
Tuning of a neuro-fuzzy controller by genetic algorithm
Teo Lian Seng; Marzuki Bin Khalid; Rubiyah Yusof
1999-01-01
Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the Radial Basis Function neural network (RBF) with Gaussian
Collision avoidance of moving obstacles for ship with genetic algorithm
Xiao-ming Zeng; Masanori Ito; Etsuro Shimizu
2000-01-01
By use of a path planning method-genetic algorithms (GA), collisions with navigation obstacles and other moving ships can be avoided according to navigation traffic rule (the ship must pass on the right). Navigation obstacles and moving ships are identified by automatic radar plotting aids (ARPA), and then the future danger which is generated by the obstacles is predicted using a
Optimal path planning in Rapid Prototyping based on genetic algorithm
Yang Weidong
2009-01-01
One of important researches in rapid prototyping (RP) is to optimize the path planning which affects the efficiency and building quality of RP system. But it is very difficult to solve its optimization by traditional methods. Genetic algorithms (GAs) are excellent approaches to solving these complex problems in optimization with difficult constraints. The classic path-planning optimization problem has been shown
Genetic algorithm applied to the selection of principal components
A. S. Barros; D. N. Rutledge
1998-01-01
The application of a genetic algorithm (GA) to the selection of principal components (PCs) is proposed as an efficient method to determine the optimal multivariate regression model. This stochastic method was compared with other deterministic methods such as: exhaustive search (here taken as a validation procedure), forward and backward-stepwise variable selection and correlation principal components regression (CPCR). It is shown
Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma
Krings, Axel W.
], Goldberg (1989) [3], already studied the population sizing issues from the very beginning of EC. WhatDynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma University of Idaho Computer Science. Moscow, ID 83843, USA krings@cs.uidaho.edu ABSTRACT Biological populations are dynamic in both space
Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function
Coello, Carlos A. Coello
of redundant individuals in the population, thereby decrease the efficiency of the EAs. Our idea in this studyAdaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization Kwong called adaptive elitist- population search method for allowing unimodal function optimization methods
A portfolio selection strategy using Genetic Relation Algorithm
Yan Chen; Shingo Mabu; Kotaro Hirasawa
2010-01-01
This paper proposes a new strategy ?-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta ? efficiently measures the volatility relative to the benchmark index or the capital market, ? is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process.
Exploring Very Large State Spaces Using Genetic Algorithms
Khurshid, Sarfraz
Exploring Very Large State Spaces Using Genetic Algorithms Patrice Godefroid1 and Sarfraz Khurshid2 this frame- work in conjunction with VeriSoft, a tool for exploring the state spaces of software applications, thereby mak- ing exhaustive state-space exploration intractable. Several approaches have been proposed
Allocating optimal index positions on tool magazines using genetic algorithms
Türkay Dereli; I. Hüseyin Filiz
2000-01-01
This paper presents an optimisation system software developed for the determination of optimal index positions of cutting tools on the automatic tool changer (ATC) or turret magazine of CNC machine tools. Position selection is performed using a genetic algorithm (GA) which takes a list of cutting tools assigned to certain machining operations together with total number of index positions available
Constrained Optimization with Genetic Algorithm: Improving Profitability of Targeted Marketing
Geng Cui; Man Leung Wong; Xiang Wan
2010-01-01
Direct marketing forecasting models have focused on estimating the response probabilities of consumer purchases and neglected the profitability of customers. This study proposes a method of constrained optimization using genetic algorithm to maximize the profitability at the top deciles of a customer list. We apply this method to a direct marketing dataset using tenfold cross validation. The results from this
Genetic Algorithm Design of Electronic Analogue Circuits Including Parasitic Effects
D. H. Horrocks; Y. M. A. Khalifa
1996-01-01
An extended Genetic Algorithm method is described that allows parasitic effects in components to be included in the design of analogue electronic circuits built from components with values selected from a set of predetermined 'preferred' values. An example is given showing that successful circuit solutions can be obtained that fail with previous methods.
GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS
David L. Carroll
1996-01-01
This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA
An application of genetic algorithm for university course timetabling problem
Xinyang Deng; Yajuan Zhang; Bingyi Kang; Jiyi Wu; Xiaohong Sun; Yong Deng
2011-01-01
Timetabling problems are a process of assigning a given set of events and resources to the limited space and time under hard constraints which are rigidly enforced and soft constraints which are satisfied as nearly as possible. As a kind of timetabling problems, university course timetabling is a very important administrative activity for a wide variety of schools. Genetic algorithm
An informed genetic algorithm for the high school timetabling problem
Rushil Raghavjee; Nelishia Pillay
2010-01-01
The high school timetabling problem differs drastically from one school to another and from country to country. The South African high school problem has not been researched. This paper presents a genetic algorithm (GA) to solve this problem for a particular high school. A two-phase approach is taken. The first phase uses a GA to evolve a timetable that meets
A Genetic Algorithm Approach to Focused Software Usage Testing
Wu, Annie S.
A Genetic Algorithm Approach to Focused Software Usage Testing Robert M. Patton, Annie S. Wu Orlando, FL, U.S.A. ABSTRACT Because software system testing typically consists of only a very small the test results from a limited amount of testing based on high-level usage models. It can also be very
SEXUAL SELECTION WITH COMPETITIVE/COOPERATIVE OPERATORS FOR GENETIC ALGORITHMS
Bullinaria, John
well known components: the standard GA [16, 18, 12], and the theory of sexual selection [8, 23, 13SEXUAL SELECTION WITH COMPETITIVE/COÂOPERATIVE OPERATORS FOR GENETIC ALGORITHMS Josâ??e S be parents in crossover. Gender separation and sexual selection here inÂ spire a model of gendered GA
An Efficient Constraint Handling Method for Genetic Algorithms
Kalyanmoy Deb
1998-01-01
Many real-world search and optimization problems involve inequality and\\/or equality con- straints and are thus posed as constrained optimization pro blems. In trying to solve con- strained optimization problems using genetic algorithms ( GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function
An efficient constraint handling method for genetic algorithms
Kalyanmoy Deb
2000-01-01
Many real-world search and optimization problems involve inequality and\\/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using genetic algorithms (GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function approach is generic and
Evolving Networks: Using the Genetic Algorithm with Connectionist Learning
Richard K. Belew; John Mcinerney; Nicol N. Schraudolph
1990-01-01
It is appealing to consider hybrids of neural-network learning algorithmswith evolutionary search procedures, simply because Nature hasso successfully done so. In fact, computational models of learning andevolution offer theoretical biology new tools for addressing questionsabout Nature that have dogged that field since Darwin [Belew, 1990].The concern of this paper, however, is strictly artificial: Can hybridsof connectionist learning algorithms and genetic
USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...
Crossover Improvement for the Genetic Algorithm in Information Retrieval.
ERIC Educational Resources Information Center
Vrajitoru, Dana
1998-01-01
In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
A Niched Pareto Genetic Algorithm for Multiobjective Optimization
Jeffrey Horn; Nicholas Nafpliotis; David E. Goldberg
1994-01-01
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination
Comparison between Genetic Algorithms and Particle Swarm Optimization
Russell C. Eberhart; Yuhui Shi
1998-01-01
This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from
Genetic Algorithm Enlarges the Capacity of Associative Memory
Akira Imada; Keijiro Araki
1995-01-01
We propose a genetic algorithm for mutuallyconnected neural networks to obtain ahigher capacity of associative memory. InHopfield network as an associative memorysystem, the memory capacity is at most 15%of the number of neurons. Here we appliedour method to the Hopfield network, andobtained the capacity of 33%. We conjecturedthat this is due to both asymmetryand sparseness of the connection matrix introducedby
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
R. J. Abrahart
2004-01-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\\\begin{itemize} more accurate in terms of output precision and model
Genetic algorithm based neural networks for dynamical system modeling
Stephan Dreiseitl; Witold Jacak
1995-01-01
The modeling of nonlinear dynamical systems is one of the emergent application areas of artificial neural networks. In this paper, we present a general methodology based on neural networks and genetic algorithms that can be applied to modeling of nonlinear dynamical systems. We describe a general methodology for modeling nonlinear systems with known rank (i.e. state-space dimension) by feedforward networks
Evolving radial basis function neural networks using a genetic algorithm
Brian Carse; Anthony G. Pipe; Terence C. Fogarty; Terence Hill
1995-01-01
Most research to date using genetic algorithms to evolve neural networks has focused on the multi-layer perceptron. Alternative neural network approaches such as the radial basis function network, and their representations appear to have received relatively little attention as grist for the GA mill. This is perhaps surprising since, for example, the radial basis function network has also been proved
GENETIC ALGORITHM DESIGN OF NETWORKS CONSIDERING ALL-TERMINAL RELIABILITY
Smith, Alice E.
GENETIC ALGORITHM DESIGN OF NETWORKS CONSIDERING ALL-TERMINAL RELIABILITY Berna Dengiz and Fulya. The reliability and the cost of these systems are important considerations that are largely determined to solve the all-terminal network design problem when considering cost and reliability. The GA
Genetic algorithm for true negative index in plasmonic metamaterials
NASA Astrophysics Data System (ADS)
Goforth, Ian A.; Fullager, Daniel B.; Alisafaee, Hossein; Fiddy, Michael A.
2015-02-01
We investigate negative index of refraction in plasmonic metamaterials with an emphasis on distinguishing and isolating contributions to negative refraction from spatial dispersion, as a function of metamaterial dimensions on the scale of the wavelength. We explain the design approach using genetic algorithm and provide sample applications including negative refraction.
Automated Design of Algorithms and Genetic Improvement: Contrast and Commonalities
Woodward, John
. Haraldsson University of Stirling Stirling, Scotland, UK soh@cs.stir.ac.uk John R. Woodward University of Stirling Stirling, Scotland, UK jrw@cs.stir.ac.uk Categories and Subject Descriptors I.2.2 [Automatic Based Soft- ware Engineering (SBSE), Genetic Algorithm (GA) 1. INTRODUCTION Recent decades has seen
A genetic algorithm approach for regrouping service sites
Nashat Mansour; Hiba Tabbara; Tarek Dana
2004-01-01
We address the problem of regrouping service sites into a smaller number of service centers, where each center serves a region. We propose a two-phase method, based on a weighted-graph problem formulation, for providing good suboptimal solutions. In the first phase, the graph is decomposed into the required number of subgraphs (regions) using a tuned hybrid genetic algorithm. The second
Parameter Control Methods for Selection Operators in Genetic Algorithms
Eiben, A.E. "Guszti"
Parameter Control Methods for Selection Operators in Genetic Algorithms P. Vajda, A.E. Eiben of such methods on three groups of test functions and conclude that varying se- lection pressure during a GA run often yields performance benefits, and therefore is a recommended option for designers and users
Cyclic Genetic Algorithm with Conditional Branching PredatorPrey Scenario
Parker, Gary B.
Cyclic Genetic Algorithm with Conditional Branching PredatorÂPrey Scenario Gary Parker Computer Science Connecticut College New London, 06320 parker@conncoll.edu Parashkevov Computer Science Connecticut) found to a successful method evolving single control programs legged robots. major limitation
A Clustering Genetic Algorithm for Actuator Optimization in Flow Control
Michele Milano; Petros Koumoutsakos
2000-01-01
Active flow control can provide a leap in the perform- nace of engineering configurations. Although a number of sensor and actuator configurations have been proposed the task of identifying optimal parameters for control devices is based on engineering intuition usually gathered from un- controlled flow experiments. Here we propose a clustering genetic algorithm that adaptively identifies critical points in the
A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and
Coello, Carlos A. Coello
A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and Electromagnetics R. The objective functions in the optimization problem measure the aerodynamic feasibil ity based on the drag been optimized with respect to only one discipline such as aerodynamics or electromagnetics. Although
Stereo camera calibration using real coded genetic algorithm
Sanjeev Kumar; Manoj Thakur; Balasubramanian Raman; N. Sukavanam
2008-01-01
In this paper, the problem of obtaining calibration parameters for a stereo camera system is considered. A real coded genetic algorithm is used to solve this problem. The solution of extrinsic and intrinsic parameters of the left and right cameras are encoded in the vectors of real numbers. The Laplace crossover and power mutation operators are used to obtain next
A genetic algorithm model for high heat flux flow boiling
Pasquale M. Sforza
1997-01-01
A new genetic algorithm model is introduced in a recently developed turbulent-boundary-layer scheme for the calculation of heat transfer in high heat flux subcooled boiling flows. Such flows, often desired for cooling of rocket nozzles and nuclear components, are characterized by high fluid velocities and extremely small bubbles that exist in a thin layer adjacent to the heated wall. The
ASGA: Improving the Ant System by Integration with Genetic Algorithms
Tony White; Bernard Pagurek; Franz Oppacher
1998-01-01
This paper describes how the Ant System can be improved by self- adaptation of its controlling parameters. Adaptation is achieved by integrating a genetic algorithm with the ant system and maintaining a population of agents (ants) that have been used to generate solutions. These agents have behavior that is inspired by the foraging activities of ants, with each agent capable
Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water
Coello, Carlos A. Coello
for determining the optimal schedule of chlorine dosing within a water distribution system considering multiple-based method), is in progress. INTRODUCTION Controlling the levels of chlorine within the distribution systemoz343 Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water Distribution Systems
EVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM
Parker, Gary B.
as possible. In addition, learning reduces the human engineering required to develop the intricaciesEVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM GARY B. PARKER, DAVID W. BRAUN, AND INGO with the differences between and within robots would greatly reduce engineering calculations and increase robot
Tool Support for Software Architecture Design with Genetic Algorithms
H. Hadaytullah; S. Vathsavayi; O. Ra?iha?; K. Koskimies
2010-01-01
Automated support for software architecture design is discussed. The proposed approach is based on a tool applying genetic algorithms for producing potential architecture proposals. The tool requires a basic functional decomposition of the system and the specification of the quality requirements as input, relying on a repository of standard solutions like patterns and architectural styles. The underlying techniques and the
Optimum design of rolling element bearings using genetic algorithms
B. Rajeswara Rao; Rajiv Tiwari
2007-01-01
A constraint non-linear optimization procedure based on genetic algorithms has been developed for designing rolling element bearings. Based on maximum fatigue life as objective function and associated kinematic constrains have been formulated. The design parameters include the bearing pitch diameter, the rolling element diameter, number of rolling elements and inner and outer-race groove curvature radii. The constraints contain unknown constants,
Scheduling trucks in container terminals using a genetic algorithm
W. C. Ng; K. L. Mak; Y. X. Zhang
2007-01-01
Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm
Elitist Genetic Algorithm Models: Optimization of High Performance Concrete Mixes
M. A. Jayaram; M. C. Nataraja; C. N. Ravikumar
2009-01-01
This article elaborates the development of elitist Genetic Algorithm (GA) models for the optimization of high volume fly ash concrete (HVFAC) mix. The model consists of two stages. In the first stage, a huge database of 350 mix designs garnered through standard research publications were statstistically analyzed to elicit upper and lower bounds of certain range constraints and rational ratio
Genetic algorithms: What computers can learn from Darwin
Walbridge, C.T. (US Government on Aquatic Toxicology (US))
1989-01-01
In this article the author posits a field of computing based on the genetic algorithm. This approach to programming mimics evolution by utilizing a computer to solve problems on a trial and error basis and ascertain the best answer through natural selection of the best of the computer's guesses. The author discusses the viability of this system in comparison to that of artificial intelligence.
Emotional Image and Musical Information Retrieval With Interactive Genetic Algorithm
Cho, Sung-Bae
Emotional Image and Musical Information Retrieval With Interactive Genetic Algorithm SUNG-BAE CHO to develop useful humancomputer interfaces, but it is still quite far from realizing a system of matching- mization with human evaluation, and with which the user can ob- tain what he has in mind through repeated
Emotional image and musical information retrieval with interactive genetic algorithm
Sung-Bae Cho
2004-01-01
Several techniques in artificial intelligence have shown a great potential to develop useful human-computer interfaces, but it is still quite far from realizing a system of matching the human performance, especially in terms of emotion, intuition and inspiration. To overcome this shortcoming, we present a promising technique called interactive genetic algorithm (IGA), which performs optimization with human evaluation, and with
Calibration of VISSIM for shanghai expressway using genetic algorithm
Wu Zhizhou; Sun Jian; Yang Xiaoguang
2005-01-01
This paper presents how an optimal optimization method, Genetic Algorithm (GA), is applied for finding a suitable combination of VISSIM parameters. The North-South (N- S) Expressway is investigated and simulated in VISSIM platform using field data obtained from Traffic Information Collecting System (TICS) in Shanghai. Numerous simula- tion tests indicate that the following main parameters have affected simulation precision most
Calibration of VISSIM for Shanghai expressway using genetic algorithm
Wu Zhizhou; Sun Jian; Yang Xiaoguang
2005-01-01
This paper presents how an optimal optimization method, genetic algorithm (GA), is applied for finding a suitable combination of VISSIM parameters. The north-south (N-S) expressway is investigated and simulated in VISSIM platform using field data obtained from traffic information collecting system (TICS) in Shanghai. Numerous simulation tests indicate that the following main parameters have affected simulation precision most deeply, such
Using Genetic Algorithms for Solving Hard Problems in GIS
Utrecht, Universiteit
Using Genetic Algorithms for Solving Hard Problems in GIS Steven van Dijk Dirk Thierens Mark de in Geographical Information Systems (GIS's). The framework is especially suited for geographical problems since as well. 1 Introduction Geographic Information Systems (GIS's for short) combine a geographical database
Automatic Optical Fiber Alignment System Using Genetic Algorithms
Masahiro Murakawa; Hirokazu Nosato; Tetsuya Higuchi
2003-01-01
We propose and demonstrate an automatic optical fiber align- ment system using genetic algorithms. Connecting optical fibers is dif- ficult because the connecting edges should be aligned with sub-micron- meter resolution. It, therefore, takes long time even for a human expert. Although automatic fiber alignment systems are being developed, they cannot be used practically if the degrees of freedom of
An improved genetic algorithm based on J1 triangulation and fixed point theory
Jingjun Zhang; Yanmin Shang; Ruizhen Gao; Yuzhen Dong
2009-01-01
An improved genetic algorithm based on J1 triangulation is proposed for multimodal optimization problems. And the fixed point theory is introduced into this improved algorithm. The optimal problems are conversed to fixed point problems. In this paper, several typical functions are used to demonstrate the effectiveness of this algorithm, and the testing results show that the improved genetic algorithm is
Application of Genetic Algorithm in the Optimization of Water Pollution Control Scheme
Rui-Ming Zhao; Dong-Ping Qian
2007-01-01
Genetic Algorithm (Genetic Algorithm Chine write for the GA) is a kind of hunting Algorithm bionic global optimization imitating the Darwinian biological evolution theories, is advancing front of complex nonlinear science and artificial intelligence science. In the basic of introducing the GA basic principle and optimization Algorithm, this text leads the GA into the domain of the water pollution control
Algorithms and Algorithmic Languages.
ERIC Educational Resources Information Center
Veselov, V. M.; Koprov, V. M.
This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…
JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms
NASA Technical Reports Server (NTRS)
Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve
2000-01-01
A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-05-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al{sub n} (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of {radical}3 x {radical}3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh; genetic algorithm; adaptive mesh; finite element method #12;
Martin, Roland
Annealing and Genetic Algorithms to the Reconstruction of Electrical Permittivity Images in Capacitance annealing (SA) and genetic algorithms (GA) inversion methods to the reconstruction of permittivity images reconstructions. Keywords Simulated annealing, Genetic algorithms, Capacitance tomography, Global optimisation
A Genetic Algorithm Approach to Multiple-Response Optimization
Ortiz, Francisco; Simpson, James R.; Pignatiello, Joseph J.; Heredia-Langner, Alejandro
2004-10-01
Many designed experiments require the simultaneous optimization of multiple responses. A common approach is to use a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors. However, as the problem grows even moderately in either the number of factors or the number of responses, conventional optimization algorithms can fail to find the global optimum. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). This paper proposes and develops a multiple-response solution technique using a GA in conjunction with an unconstrained desirability function. The GA requires that several parameters be determined in order for the algorithm to operate effectively. We perform a robust designed experiment in order to tune the genetic algorithm to perform well regardless of the complexity of the multiple-response optimization problem. The performance of the proposed GA method is evaluated and compared with the performance of the method that combines the desirability with the generalized reduced gradient (GRG) optimization. The evaluation shows that only the proposed GA approach consistently and effectively solves multiple-response problems of varying complexity.
Mehmet Metin Kunt; Iman Aghayan; Nima Noii
2011-01-01
This paper focuses on predicting the severity of freeway traffic accidents by employing twelve accident-related parameters in a genetic algorithm (GA), pattern search and artificial neural network (ANN) modelling methods. The models were developed using the input parameters of driver's age and gender, the use of a seat belt, the type and safety of a vehicle, weather conditions, road surface,
Grimbleby, James
that GAs are both practical and robust. Application of GAs to Network Synthesis Each individualAutomatic Analogue Network Synthesis using Genetic Algorithms IEE/IEEE International Conference provide a basis for automatic synthesis of analogue electronic networks. Passive linear networks have been
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied to obtain the solution of problem. In this research, the first step is to design a geodetic network and do the observations of the distances and angles between network's stations. The second step is to use the optimization algorithms to estimate unknown values of stations' coordinates, with regards to calculation equations of length and angle. The result indicates that The Genetic algorithms have been successfully employed for solving inverse problems in engineering disciplines. And it seems that many complex problems can be better solved using genetic algorithms than those of using conventional methods.
Large-scale economic dispatch by genetic algorithm
Chen, P.H.; Chang, H.C. [National Taiwan Inst. of Tech., Taipei (Taiwan, Province of China). Dept. of Electrical Engineering
1995-11-01
This paper presents a new genetic approach for solving the economic dispatch problem in large-scale systems. A new encoding technique is developed. The chromosome contains only an encoding of the normalized system incremental cost in this encoding technique. Therefore, the total number of bits of chromosome is entirely independent of the number of units. The salient feature makes the proposed genetic approach attractive in large and complex systems which other methodologies may fail to achieve. Moreover, the approach can take network losses, ramp rate limits, and prohibited zone avoidance into account because of genetic algorithm`s flexibility. Numerical results on an actual utility system of up to 40 units show that the proposed approach is faster and more robust than the well-known lambda-iteration method in large-scale systems.
Implementing Genetic Algorithms on Arduino Micro-Controllers
Alves, Nuno
2010-01-01
Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimization and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimizations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.
Strain gage selection in loads equations using a genetic algorithm
NASA Technical Reports Server (NTRS)
1994-01-01
Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.
Using genetic algorithms to construct a network for financial prediction
NASA Astrophysics Data System (ADS)
Patel, Devesh
1996-03-01
Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.
Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Panicker, Shina; Vijay Kumar, T. V.
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513
Genetic algorithm based tomographic flow visualization
Lyons, Donald Paul
1997-01-01
A new nonlinear tomographic flow visualization technique for use in limited data situations is developed using techniques from the emerging field of evolutionary computing. The new technique uses both pure and hybrid genetic ...
Genetic algorithms and their use in Geophysical Problems
Parker, Paul B.
1999-04-01
Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.
Network Intrusion Detection Method Based on High Speed and Precise Genetic Algorithm Neural Network
Jingwen Tian; Meijuan Gao
2009-01-01
Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of neural network, an intrusion detection method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP
Real-time recognition of road traffic sign in motion image based on genetic algorithm
Han Liu; Ding Liu; Jing Xin
2002-01-01
In this paper, a new technology based on the genetic algorithm and new image filter for recognizing road traffic sign from motion image captured by a CCD camera in a car was developed. In order to realize a real-time position recognition, the step genetic algorithm with search region limits and image filter (denoted by SVF) were proposed. The genetic algorithm
Luhua, Lai
Beyond the Rotamer Library: Genetic Algorithm Combined with the Disturbing Mutation Process, China ABSTRACT The disturbing genetic algorithm, incorporating the disturbing mutation process into the genetic algorithm flow, has been developed to extend the searching space of side-chain conforma- tions
Simulated annealing, weighted simulated an nealing and genetic algorithm at work
Besse, Philippe
Simulated annealing, weighted simulated an nealing and genetic algorithm at work Fran and genetic algorithm are compared when using a sample to minimize an objective function which efficient than the genetic algorithm. With regard to the bias problem, the randomly weighted version
A Novel Genetic Algorithms for the Automated Design of Performance Driven Digital Circuits
Arslan, Tughrul
A Novel Genetic Algorithms for the Automated Design of Performance Driven Digital Circuits ` Ben. I.ed.ac.uk; TughruI.Arslan@ee.ed.ac.uk Abstract- The authors present a genetic algorithm for the design of high. The paper describes the genetic algorithm and the hardware evaluation environment, and provides results
A Hardware Genetic Algorithm for the Traveling Salesman Problem on Splash2
Nelson, Brent E.
A Hardware Genetic Algorithm for the Traveling Salesman Problem on Splash2 Paul Graham and Brent in hardware. In this paper, we describe the Splash 2 Parallel Genetic Algorithm (SPGA), which is a parallel quickly than single processor and softwarebased implementations of the genetic algorithm. The four
New applications of the genetic algorithm for the interpretation of high-resolution spectra1
Nijmegen, University of
804 New applications of the genetic algorithm for the interpretation of high-resolution spectra1 W. An alternative approach is unassigned fits of the spectra using genetic algorithms (GAs) with special cost, genetic algorithm, biomolecules, structure, van der Waals clusters. Résumé : La spectroscopie électronique
Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks
Smith, Alice E.
Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks Berna Turkey berna@rorqual.cc.metu.edu.tr Abstract This paper presents a genetic algorithm (GA. Genetic algorithms (GA) have recently found their way in combinatorial optimization approaches to reliable
Polygonal Approximation of Digital Curves Using a Multi-objective Genetic Algorithm
Paris-Sud XI, UniversitÃ© de
Polygonal Approximation of Digital Curves Using a Multi-objective Genetic Algorithm Herve Locteau, a polygonal approximation approach based on a multi- objective genetic algorithm is proposed. In this method][17][18]. It consists in using Genetic Algorithms in order to find a near- optimal polygonal approximation
Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence
Moreira, Bruno Contreras
Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence Laboratories, London, United Kingdom ABSTRACT A novel genetic algorithm was ap- plied to all CASP5 targets recognition; comparative model- ing; genetic algorithms; template selec- tion; alignment errors INTRODUCTION
09s1: COMP9417 Machine Learning and Data Mining Genetic Algorithms
Bain, Mike
09s1: COMP9417 Machine Learning and Data Mining Genetic Algorithms April 22, 2009 Acknowledgement learning approaches using genetic algorithms. Following it you should be able to: · outline evolutionary computation · reproduce the basic form of a genetic algorithm · describe a representation for rule learning
Smith, Alice E.
Local Search Genetic Algorithm for Optimal Design of Reliable Networks Berna Dengiz and Fulya Pittsburgh, Pennsylvania 15261 USA aesmith@engrng.pitt.edu Abstract This paper presents a genetic algorithm Corresponding author. #12;1 Local Search Genetic Algorithm for Optimal Design of Reliable Networks Abstract
Graph Classification Using Genetic Algorithm and Graph Probing Application to Symbol Recognition
Paris-Sud XI, UniversitÃ© de
Graph Classification Using Genetic Algorithm and Graph Probing Application to Symbol Recognition classification approach using genetic algorithm and a fast dissimilarity measure between graphs called graph this learning set, a graph based Genetic Algorithm (GA) is applied. Its aim is to generate a set of K graph
Journal of Computational Acoustics, SUBSPACE APPROACH TO INVERSION BY GENETIC ALGORITHMS
Gerstoft, Peter
Journal of Computational Acoustics, fc IMACS SUBSPACE APPROACH TO INVERSION BY GENETIC ALGORITHMS;2 and genetic algorithms3;4 to search over the space of likely values of the unknown parameters. The ease is computed using the OASES wavenumber integration code8;9 as the forward model. 2.2. Genetic algorithms
Recombination and Self-Adaptation in Multi-objective Genetic Algorithms
Paris-Sud XI, UniversitÃ© de
Recombination and Self-Adaptation in Multi-objective Genetic Algorithms Bruno Sareni, JÃ©rÃ©mi of recombination and self- adaptation in real-encoded Multi-Objective Genetic Algorithms (MOGAs). NSGA-II and SPEA2 their robustness. The second versions of the Non-dominated Sorting Genetic Algorithm (NSGA-II) [3
A Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems
Kansas, University of
A Genetic Algorithm-Based Controller for Decentralized Multi-Agent Robotic Systems Proceedings, using genetic algorithms, producing a generation of robots with superior task performance, compared that perform specific tasks in an environment is evolved by using genetic algorithms, hence producing a better
A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in
Lanconelli, Nico
A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications obtained by applying a distributed genetic algorithm to a problem of parameter op- timization in medical the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some
Automated Design of Steel Open Web Joist Floor Framing Systems Using a Genetic Algorithm
Foley, Christopher M.
Automated Design of Steel Open Web Joist Floor Framing Systems Using a Genetic Algorithm systems utilizing a genetic algorithm (GA), which utilizes a search strategy that is modeled on the same design options are perfectly suited to the genetic algorithm. The GA has been used successfully
Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models
Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models Kevin Distributed Systems", NIST IR 7744, 2010). b. Anti-Optimization (AO) + Genetic Algorithm (GA) Â TODAY 19, 2014 GMU CS Interdisciplinary Seminar #12;Method: Genetic Algorithm (GA) steers a population
Exploring a Financial Product Model with a Two-Population Genetic Algorithm
Kimbrough, Steven Orla
Exploring a Financial Product Model with a Two-Population Genetic Algorithm Steven O. Kimbrough two-population genetic algorithm (GA) has been remarkably successful in finding good, feasible is motivated by the fact that, while evolution programs (EPs) in general and genetic algorithms in particular
INDUCING PARAMETERS OF A DECISION TREE FOR EXPERT SYSTEM SHELL MCESE BY GENETIC ALGORITHM
Franek, Frantisek
INDUCING PARAMETERS OF A DECISION TREE FOR EXPERT SYSTEM SHELL MCESE BY GENETIC ALGORITHM I. Bruha-mail: {bruha | franya}@mcmaster.ca KEYWORDS Expert system shell, genetic algorithms, rule-based sys- tems for the whole knowledge base. Genetic algorithms comprise a long process of evolution of a large population
A Genetic Algorithm to solve an Integer Goal Programming Model for the Higher Rafael Caballero
Coello, Carlos A. Coello
A Genetic Algorithm to solve an Integer Goal Programming Model for the Higher Education. Rafael.molina@uma.es ABSTRACT In this work a genetic algorithm for the resolution of an Integer Goal Programming model is shown to solve it. However, it will be shown how this new type of methods, the genetic algorithms, can solve real
Genetic algorithms for delays evaluation in networked automation systems , S. Amari, J-J. Lesage
Paris-Sud XI, UniversitÃ© de
Genetic algorithms for delays evaluation in networked automation systems B. Addad n , S. Amari, J Client-Server protocol Delays evaluation Genetic algorithms a b s t r a c t In this paper, we present problems, exhaustive and genetic algorithms, are then developed to achieve this purpose. While a formal
Hoffmann, Frank
with a hierarchical prioritized structure is proposed. A messy genetic algorithm is used to learn di erent types ones dealing with exceptional situations. Secondly we use a messy genetic algorithm 5] which process scheme. Messy genetic algorithms therefore allow a exible representation of fuzzy rules in the con
Genetic algorithms in astronomy and astrophysics Vinesh Rajpaul1,2
Masci, Frank
Genetic algorithms in astronomy and astrophysics Vinesh Rajpaul1,2 1 Astrophysics, Cosmology-mail: vinesh.rajpaul@uct.ac.za Abstract. Genetic algorithms (GAs) emulate the process of biological evolution-called genetic algorithms form one of the most successful subsets, and certainly the most popular subset
Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models
Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models Kevin, 2010). b. Anti-Optimization (AO) + Genetic Algorithm (GA) Â TODAY'S PRESENTATION Planned: investigate of complex information systems? ONGOING NIST RESEARCH FROM 2012-PRESENT #12;Method: Genetic Algorithm (GA
Application of genetic algorithm to the calculation of bound states and local density approximations
Zeiri, Yehuda
Application of genetic algorithm to the calculation of bound states and local density; accepted 16 November 1994 A novel method, based on genetic algorithms, has been developed and applied. © 1995 American Institute of Physics. I. INTRODUCTION Genetic algorithms GA are global optimization meth
Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models
Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models The 5th as Chromosomes 3. Genetic Algorithm 4. Population of Simulators 5. Dynamics of GA Search 6. Analysis Method 7 a MIN, MAX and precision. Anti-Fitness Reports GENETIC ALGORITHM Principal Components Analysis
Multi-resolution Genetic Algorithms and Markov Chain Monte Carlo June 11, 2002
West, Mike
Multi-resolution Genetic Algorithms and Markov Chain Monte Carlo June 11, 2002 Christopher H-resolution genetic algorithm that incorporates elements of simulated tempering to allow efficient estimation-scale genetic algorithm that links coarse and fine scale models together. In many situations, data naturally
Improved Genetic Algorithm for Economic Dispatch of Power Systems having Special Units
Chao-Lung Chiang; Chang-Wei Chai; Chia-An Wang
2006-01-01
This paper presents an improved genetic algorithm with multiplier updating method (IGAMUM) to solve the power economic dispatch problem (PEDP), constrained by reserve and prohibited operating zones (POZ). A genetic algorithm (GA) equipped with the improved evolutionary direction operator (IEDO) and migration called the improved genetic algorithm (IGA) is proposed, which can efficiently search and explore solutions. The multiplier updating
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
F. Herrera; M. Lozano; J. L. Verdegay
1998-01-01
Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.
Learning to Play Like a Human: Case Injected Genetic Algorithms Applied to Strategic Computer
Louis, Sushil J.
Learning to Play Like a Human: Case Injected Genetic Algorithms Applied to Strategic Computer Game as the human would. We seek to produce a genetic algorithm player (GAP) that Fig. 1. Game Screen-shot can play Systems Center San Diego, CA john.mcdonnell@navy.mil Abstract-- We use case injected genetic algorithms
Learning to Play Like a Human: Case Injected Genetic Algorithms Applied to Strategic Computer
Louis, Sushil J.
Learning to Play Like a Human: Case Injected Genetic Algorithms Applied to Strategic Computer Game as the human would. We seek to produce a genetic algorithm player (GAP) that Fig. 1. Game Screenshot can play Systems Center San Diego, CA john.mcdonnell@navy.mil Abstract--- We use case injected genetic algorithms
Back analysis of thermal field of concrete and its application based on niche genetic algorithms
Chen Shou-kai; Zhu Yue-ming; Shuai Wei
2010-01-01
An improved form of basic genetic algorithm: Niche genetic algorithm which is based on sharing function is designed. It is aiming at the premature problem of basic genetic algorithm that existed in the back analysis of parameters of concrete thermal field. According to the measured temperature of concrete of rock anchor beam during construction period, its program composition is used
Test Driving Three 1995 Genetic Algorithms: New Test Functions and Geometric Matching
Whitley, Darrell
. 1 Introduction The use of genetic algorithms as optimization tools is now familiar to a broad rangeTest Driving Three 1995 Genetic Algorithms: New Test Functions and Geometric Matching D. Whitley, R, Colorado 80523 USA (303) 4915373 whitley@cs.colostate.edu Abstract Genetic algorithms have attracted
Genetic Quantum Algorithm for Voltage and Pattern Design of Piezoelectric Actuator
A.-R. Khorsand; M.-R. Akbarzadeh-T; H. Moin
2006-01-01
This paper presents genetic quantum algorithm and its associated evolutionary tools for the voltage and pattern design of piezoelectric actuator. Genetic quantum algorithms (GQA) is similar to genetic algorithms (GA) maintain a population of individuals but each individual is composed of probabilistic quantum bits for preserving diversity. Also, instead of crossover or mutation, GQA use quantum gates (QG) to update
Solving Similar Problems Using Genetic Algorithms and Case-Based Memory
Sushil J. Louis; Judy Johnson
1997-01-01
This paper uses genetic algorithms augmentedwith a case-based memory of pastproblem solving attempts to obtain betterperformance over time on sets of similar problems.When confronted with a problem weseed a genetic algorithm's initial populationwith solutions to similar, previously solvedproblems and the genetic algorithm thenadapts its seeded population toward solvingthe current problem. We address the issueof selecting "appropriate" cases for injectionand introduce
Geometric Nelder-Mead Algorithm on the Space of Genetic Programs
Yao, Xin
Geometric Nelder-Mead Algorithm on the Space of Genetic Programs Alberto Moraglio School@{kdbio.inesc-id.pt,dei.uc.pt} ABSTRACT The Nelder-Mead Algorithm (NMA) is a close relative of Particle Swarm Optimization (PSO of genetic programs. The result is a Nelder-Mead Algorithm searching the space of genetic pro- grams
An Airborne Conflict Resolution Approach Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mondoloni, Stephane; Conway, Sheila
2001-01-01
An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.
Genomic multiple sequence alignments: refinement using a genetic algorithm
Wang, Chunlin; Lefkowitz, Elliot J
2005-01-01
Background Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics – the practice of comparing genomic sequences from different species – plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program. Results We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased – through the removal of gaps – by approximately 200 gapped regions representing roughly 1,300 gaps. Conclusion We have implemented a genetic algorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time. PMID:16086841
Application of coevolutionary genetic algorithms for multiobjective optimization
NASA Astrophysics Data System (ADS)
Liu, Jian-guo; Li, Zu-shu; Wu, Wei-ping
2007-12-01
Multiobjective optimization is clearly one of the most important classes of problems in science and engineering. The solution of real problem involved in multiobjective optimization must satisfy all optimization objectives simultaneously, and in general the solution is a set of indeterminacy points. The task of multiobjective optimization is to estimate the distribution of this solution set, then to find the satisfying solution in it. Many methods solving multiobjective optimization using genetic algorithm have been proposed in recent twenty years. But these approaches tend to work negatively, causing that the population converges to small number of solutions due to the random genetic drift. To avoid this phenomenon, a multiobjective coevolutionary genetic algorithm (MoCGA) for multiobjective optimization is proposed. The primary design goal of the proposed approach is to produce a reasonably good approximation of the true Pareto front of a problem. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set. The proposed approach is validated using Schaffer's test function f II and it is compared with the Niched Pareto GA (nPGA). Simulation experiments prove that the algorithm has a better performance in finding the Pareto solutions, and the MoCGA can have advantages over the other algorithms under consideration in convergence to the Pareto-optimal front.
Guili Yuan; Yan-guang Xue; Jizhen Liu
2010-01-01
Aiming at prematureness, slow convergence rate and reduction in diversity which exist in Genetic Algorithm (GA), this paper presents Adaptive Immune Genetic Algorithm (AIGA) based on GA and immune system mechanism. Adaptive Immune Genetic Algorithm introduces antigens recognition function, immune memory function and antibodies self-adjusting function to Genetic Algorithm, and replaces the fixed probability crossover and mutation operator of Genetic
Design of wavelength-selective waveplates using genetic algorithm
NASA Astrophysics Data System (ADS)
Katayama, Ryuichi
2013-03-01
Wavelength-selective waveplates, which act either identically or differently for plural wavelengths, are useful for optical systems that handle plural wavelengths. However, they cannot be analytically designed because of the complexity of their structure. Genetic algorithm is one of the methods for solving optimization problems and is used for several kinds of optical design (e.g., design of thin films, diffractive optical elements, and lenses). I considered that it is effective for designing wavelength-selective waveplates also and tried to design them using the genetic algorithm for the first time to the best of my knowledge. As a result, four types of wavelength-selective waveplate for three wavelengths (405, 650, and 780 nm) were successfully designed. These waveplates are useful for Blu-ray Disc/Digital Versatile Disc/Compact Disc compatible optical pickups.
Quantum control using genetic algorithms in quantum communication: superdense coding
NASA Astrophysics Data System (ADS)
Domínguez-Serna, Francisco; Rojas, Fernando
2015-06-01
We present a physical example model of how Quantum Control with genetic algorithms is applied to implement the quantum superdense code protocol. We studied a model consisting of two quantum dots with an electron with spin, including spin-orbit interaction. The electron and the spin get hybridized with the site acquiring two degrees of freedom, spin and charge. The system has tunneling and site energies as time dependent control parameters that are optimized by means of genetic algorithms to prepare a hybrid Bell-like state used as a transmission channel. This state is transformed to obtain any state of the four Bell basis as required by superdense protocol to transmit two bits of classical information. The control process protocol is equivalent to implement one of the quantum gates in the charge subsystem. Fidelities larger than 99.5% are achieved for the hybrid entangled state preparation and the superdense operations.
Genetic algorithm application in optimization of wireless sensor networks.
Norouzi, Ali; Zaim, A Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Genetic algorithms and the analysis of SnIa data
Savvas Nesseris
2010-11-08
The Genetic Algorithm is a heuristic that can be used to produce model independent solutions to an optimization problem, thus making it ideal for use in cosmology and more specifically in the analysis of type Ia supernovae data. In this work we use the Genetic Algorithms (GA) in order to derive a null test on the spatially flat cosmological constant model $\\Lambda$CDM. This is done in two steps: first, we apply the GA to the Constitution SNIa data in order to acquire a model independent reconstruction of the expansion history of the Universe $H(z)$ and second, we use the reconstructed $H(z)$ in conjunction with the Om statistic, which is constant only for the $\\Lambda$CDM model, to derive our constraints. We find that while $\\Lambda$CDM is consistent with the data at the $2\\sigma$ level, some deviations from $\\Lambda$CDM model at low redshifts can be accommodated.
A sustainable genetic algorithm for satellite resource allocation
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Campbell, M. L.; Krenz, W. C.
1995-01-01
A hybrid genetic algorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
GAz: a genetic algorithm for photometric redshift estimation
NASA Astrophysics Data System (ADS)
Hogan, Robert; Fairbairn, Malcolm; Seeburn, Navin
2015-05-01
We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The genetic algorithm explores the very large space of high order polynomials while only requiring optimization of a small number of terms. We find a ?rms = 0.0408 ± 0.0006 for redshifts in the range 0.4 < z < 0.7. These results are competitive with the current state-of-the-art but can be presented simply as a polynomial which does not require the user to run any code. We demonstrate that the method generalizes well to other data sets and redshift ranges by testing it on SDSS DR11 and on simulated data. For other data sets or applications the code has been made available at https://github.com/rbrthogan/GAz.
Users guide to the PGAPack parallel genetic algorithm library
Levine, D.
1996-01-01
PGAPack is a parallel genetic algorithm library that is intended to provide most capabilities desired in a genetic algorithm package, in an integrated, seamless, and portable manner. Key features of PGAPack are as follows: Ability to be called from Fortran or C. Executable on uniprocessors, multiprocessors, multicomputers, and workstation networks. Binary-, integer-, real-, and character-valued native data types. Object-oriented data structure neutral design. Parameterized population replacement. Multiple choices for selection, crossover, and mutation operators. Easy integration of hill-climbing heuristics. Easy-to-use interface for novice and application users. Multiple levels of access for expert users. Full extensibility to support custom operators and new data types. Extensive debugging facilities. Large set of example problems.
Forecasting Smoothed Non-Stationary Time Series Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Norouzzadeh, P.; Rahmani, B.; Norouzzadeh, M. S.
We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time variability of it. The final model is mainly dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small.
Using genetic algorithms for Spectrally Modulated Spectrally Encoded waveform design
T. W. Beard; M. A. Temple; J. O. Miller; R. F. Mills
2007-01-01
A genetic algorithm (GA) is used to design Spectrally Modulated, Spectrally Encoded (SMSE) waveforms while characterizing the impact of parametric variation on coexistence. As recently proposed, the SMSE framework supports cognition-based, software defined radio (SDR) applications and is well-suited for coexistence analysis. For initial proof-of-concept, two SMSE waveform parameters (number of carriers and carrier bandwidth) are optimized in a coexistent
Solving a multistage partial inspection problem using genetic algorithms
Heredia-Langner, Alejandro (BATTELLE (PACIFIC NW LAB)) [BATTELLE (PACIFIC NW LAB); Montgomery, D C.(Arizona State University) [Arizona State University; Carlyle, W M.(Naval Postgraduate School) [Naval Postgraduate School
2002-01-01
Traditionally, the multistage inspection problem has been formulated as consisting of a decision schedule where some manufacturing stages receive full inspection and the rest none. Dynamic programming and heuristic methods (like local search) are the most commonly used solution techniques. A highly constrained multistage inspection problem is presented where all stages must receive partial rectifying inspection and it is solved using a real-valued genetic algorithm. This solution technique can handle multiple objectives and quality constraints effectively.
Use of a genetic algorithm for compact stellarator coil design
NASA Astrophysics Data System (ADS)
Miner, W. H., Jr.; Valanju, P. M.; Hirshman, S. P.; Brooks, A.; Pomphrey, N.
2001-09-01
A new global optimization technique for designing stellarator coils has been developed and applied to the design of coils for the National Compact Stellarator Experiment. Using this technique coil sets were found with fewer coils and lower current densities than those obtained with traditional methods. A new coil design procedure which uses a genetic algorithm as the core optimization method is described and the resulting advanced coil designs presented.
Multi-objective design space exploration using genetic algorithms
Maurizio Palesi; Tony Givargis
2002-01-01
In this work, we provide a technique for efficiently exploring a parameterized system-on-a-chip (SoC) architecture to find all Pareto-optimal configurations in a multi-objective design space. Globally, our approach uses a parameter dependency model of our target parameterized SoC architecture to extensively prune non-optimal sub-spaces. Locally, our approach applies Genetic Algorithms (GAs) to discover Pareto-optimal configurations within the remaining design points.
Constraint-Based School Timetabling Using Hybrid Genetic Algorithms
Tuncay Yigit
2007-01-01
In this paper, a hybrid genetic algorithm (HGA) has been developed to solve the constraint-based school timetabling problem\\u000a (CB-STTP). HGA has a new operator called repair operator, in addition to standard crossover and mutation operators. A timetabling\\u000a tool has been developed for HGA to solve CB-STTP. The timetabling tool has been tested extensively using real-word data obtained\\u000a the Technical and
Method of mechanism synthesis by hybrid genetic algorithm
O'Neil, Robert Anthony
1999-01-01
subtypes 3 Two possible closure points. 4 Generation of cognate linkages. 5 Mechanisms that approximate straight-line motion. . 6 Chebyshev coupler curve. 10 7 Evans' coupler curve. . 10 8 Hoeken type 1 coupler curve. . 10 9 Hoeken type 2 coupler... curve. . 10 10 Roberts' coupler curve. . 10 11 Watt's coupler curve . 10 12 Demonstration of single point crossover 12 13 Genetic Algorithm. 14 Incremental method to find straight-line segment. . . . . . 14 . . . 1 7 15 Scaled Chebyshev output...
Using genetic algorithms to find technical trading rules1
Franklin Allen; Risto Karjalainen
1993-01-01
We use a genetic algorithm to learn technical trading rules for the S&P 500 index using daily prices from 1928 to 1995. After transaction costs, the rules do not earn consistent excess returns over a simple buy-and-hold strategy in the out-of-sample test periods. The rules are able to identify periods to be in the index when daily returns are positive
Investigation of range extension with a genetic algorithm
Austin, A. S., LLNL
1998-03-04
Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and the simulator`s output (terminal range) was the fitness measure. The parameters of interest were initial launch altitude, initial launch speed, wing angle-of-attack, and engine ignition time. The parameter values the GA produced were validated by Monte Carlo investigations employing a full-scale six-degree-of-freedom (6 DOF) simulation. The best results produced by Monte Carlo processes using values based on the GA derived parameters were within - 1% of the ranges generated by the simplified model using the evolved parameter values. This report has five sections. Section 2 discusses the motivation for the range extension investigation and reviews the surrogate flight model developed as a fitness function for the genetic algorithm tool. Section 3 details the representation and implementation of the task within the genetic algorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.
Use of a Genetic Algorithm for Neuron Model Specification
W. C. Gerken; L. K. Purvis; R. J. Butera
2005-01-01
We have used a genetic algorithm (GA) to develop simple firing neuron models consisting of a single compartment with one inward and one outward current. The GA not only chooses the model parameters, but also chooses the formulation of the ionic currents (i.e. single-variable, two-variable, instantaneous, or leak). The fitness function of the GA compares the output of the GA
Intelligent Programming of CNC Turning Operations using Genetic Algorithm
Joze Balic; Miha Kovacic; Bostjan Vaupotic
2006-01-01
CAD\\/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and\\u000a generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic\\u000a algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting\\u000a conditions. The first
Genetic Algorithms for Municipal Solid Waste Collection and Routing Optimization
Nikolaos V. Karadimas; Katerina Papatzelou; Vassili G. Loumos
2007-01-01
In the present paper, the Genetic Algorithm (GA) is used for the identification of optimal routes in the case of Municipal\\u000a Solid Waste (MSW) collection. The identification of a route for MSW collection trucks is critical since it has been estimated\\u000a that, of the total amount of money spent for the collection, transportation, and disposal of solid waste, approximately 60–80%
Design of a biplanar gradient coil using a genetic algorithm
B. J Fisher; N Dillon; T. A Carpenter; L. D Hall
1997-01-01
A biplanar z-gradient coil has been designed using a genetic algorithm, and its efficiency for producing a gradient along the axis of a solenoid magnet compared to that of a conventional Maxwell coil set. Coils of 21.8 cm by 20.9 cm area and 10 cm separation give 0.37 m Tm?1 A?1 with standard and maximum deviations of 2.6 and 13.1%
PAPR reduction in multicarrier modulations using Genetic Algorithms
Marco Lixia; Maurizio Murroni; Vlad Popescu
2010-01-01
This work presents a novel approach to the reduction of Peak-to-Average Power Ratio (PAPR) in multicarrier modulation: Partial Transmit Sequence (PTS) is optimized by opportune tailored Genetic Algorithms (GA) which allow achieving solution with pre-selected accuracy and reduced computational burden. A comparative study on both Orthogonal Frequency Division Multiplexing (OFDM) and Wavelet Packets Multi-Carrier Modulation (WP-MCM) has been conducted. Results
Optimization of heat pump using fuzzy logic and genetic algorithm
Arzu ?encan ?ahin; Bayram K?l?ç; Ula? K?l?ç
Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial\\u000a and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized\\u000a using genetic algorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated\\u000a by FL. Thermodynamic properties obtained with FL were compared with actual
A Genetic Algorithm Scheme for Pairing Meteorite Finds
NASA Astrophysics Data System (ADS)
Conway, A. J.; Bland, P. A.
1998-05-01
A genetic algorithm is employed to perform the pairing of meteorite fragments based on various characteristics measured from thin sections using an image analysis program, and analyses routinely carried out during classification. The genetic algorithm searches for best group pairings by: generating a population of trial pairs; linking them together to form groups; and evolving the population so that only pairs that are members of likely pairing groups survive to the next generation of the population. In this way meaningful pairing groups will emerge from the population, as long as characteristics from within real pairing groups have variance sufficiently small compared to the variance between groups. What constitutes `sufficiently small' is discussed and investigated by testing the genetic algorithm method on artificial data, which shows that, in principle, the method can achieve a 100 success rate. The method is then tested on real data whose pairing groups are definitely known. This is achieved by gathering data from the image processing of several scenes of the same meteorite thin section, treating each scene as a separate fragment. Using thin sections from the Reg el Acfer meteorite population, we find that the genetic algorithm identifies almost all of the main pairing groups, with about half the groups being found in their entirety; the pair-wise success rate being 76. Although this methodology requires some refinement before it could be applied to a population of meteorite fragments, these preliminary results are encouraging. The potential benefit of an automated approach lies in the tremendous savings in time and effort, allowing meaningful and reproducible pairings to be made from data sets which are prohibitively large for a human being.
Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction
K. Venugopal; K. Srinivasa; L. Patnaik
Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored\\u000a in ever growing databases. The vacillating nature of the stock market requires the use of data mining techniques like clustering\\u000a for stock market analysis and prediction. Genetic algorithms and neural networks have the ability to handle complex data.\\u000a In
OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM
Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam [Queensland University of Technology, Brisbane, Queensland (Australia); Ivanovich, Grujica [Ergon Energy, Toowoomba, Queensland (Australia)
2010-06-15
This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.
First flights of genetic-algorithm Kitty Hawk
Goldberg, D.E. [Univ. of Illinois, Urbana, IL (United States)
1994-12-31
The design of complex systems requires an effective methodology of invention. This paper considers the methodology of the Wright brothers in inventing the powered airplane and suggests how successes in the design of genetic algorithms have come at the hands of a Wright-brothers-like approach. Recent reliable subquadratic results in solving hard problems with nontraditional GAs and predictions of the limits of simple GAs are presented as two accomplishments achieved in this manner.
Decision support for irrigation project planning using a genetic algorithm
Sheng-Feng Kuo; Gary P. Merkley; Chen-Wuing Liu
2000-01-01
This work presents a model based on on-farm irrigation scheduling and the simple genetic algorithm optimization (GA) method for decision support in irrigation project planning. The proposed model is applied to an irrigation project located in Delta, Utah of 394.6ha in area, for optimizing economic profits, simulating the water demand, crop yields, and estimating the related crop area percentages with
GENNET-Toolbox: An Evolving Genetic Algorithm for Neural Network Training
Vicente Gómez-Garay; Eloy Irigoyen; Fernando Artaza
2010-01-01
\\u000a Genetic Algorithms have been used from 1989 for both Neural Network training and design. Nevertheless, the use of a Genetic\\u000a Algorithm for adjusting the Neural Network parameters can still be engaging. This work presents the study and validation of\\u000a a different approach to this matter by introducing a Genetic Algorithm designed for Neural Network training. This algorithm\\u000a features a mutation
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
NASA Astrophysics Data System (ADS)
Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.
MAC protocol for ad hoc networks using a genetic algorithm.
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L; Reyna, Alberto
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339
Genetic Algorithm Optimizes Q-LAW Control Parameters
NASA Technical Reports Server (NTRS)
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
A genetic algorithm to reduce stream channel cross section data
Berenbrock, C.
2006-01-01
A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.
Automatic Data Filter Customization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mandrake, Lukas
2013-01-01
This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.
Application of genetic algorithm for modeling of dense packing of concrete aggregates
Konstantin Sobolev; Adil Amirjanov
2010-01-01
Sequential Packing Algorithm (SPA) was developed to model the dense packing of large assemblies of particulate materials (in the order of millions). These assemblies represent the real aggregate systems of portland cement or asphalt concrete. To improve the SPA performance, the program engine was updated with a genetic algorithm (GA) search module. Multi-cell packing procedures, fine adjustment of the algorithm’s
Andreas Pitsillides; George Stylianou; Constantinos S. Pattichis; Y. Ahmet Sekercioglu; Athanasios V. Vasilakos
2002-01-01
We consider a high speed integrated services network, and investigate compare and contrast the performance of two algorithms for solving the Aggregated Bandwidth Allocation (BA) problem. The algorithms we focus our attention are: (1) a classical constrained optimisation (CCO) algorithm and (2) a constrained optimisation Genetic Algorithm (GA). We adopt the Virtual Path (VP) concept for ATM (Asynchronous Transfer Mode)
Leopoldo Carro-Calvo; Sancho Salcedo-Sanz; José Antonio Portilla-Figueras; Emilio G. Ortíz-García
2010-01-01
This paper presents a novel genetic algorithm to solve the industrial Ethernet network partition problem (IENPP). A new switch-device encoding is presented for the problem, and incorporated into the genetic algorithm. This encoding has several advantages against the traditional representation used in previous approaches, which will be detailed in the paper. Also, several new genetic operators included in the genetic
An obstacle-avoidance path-planning in robot soccer based on Refined Genetic Algorithms
Song Da-lei; Li Yan-li
2010-01-01
An intelligent obstacle-avoidance algorithms in robot soccer based on Refined Genetic Algorithms is introduced in the paper and this method is used to plan the path of the robot in robot soccer based on Microsoft Robotics Simulation Platform. The genetic algorithms display a better obstacle-avoidance effect from the data based on Microsoft Robotics Simulation Platform 11 VS 11. Because of
SUBMITTED FOR PUBLICATION TO: KES'99, MAY 13, 1999 1 Dynamic Demes Parallel Genetic Algorithm
Poli, Riccardo
SUBMITTED FOR PUBLICATION TO: KES'99, MAY 13, 1999 1 Dynamic Demes Parallel Genetic Algorithm Edgbaston, Birmingham B15 2TT, UK R.Poli@cs.bham.ac.uk Abstract--Dynamic Demes is a new method experimental results where we compared Dynamic Demes with other algorithms. I. PARALLEL GENETIC ALGORITHMS
Using genetic algorithms to solve the South African school timetabling problem
Rushil Raghavjee; Nelishia Pillay
2010-01-01
The study presented in this paper applies a genetic algorithm to solve the school timetabling problem for a South African primary school and high school. The overall algorithm makes use of domain knowledge in the form of low-level heuristics to guide the search. The genetic algorithm employed to solve the problems uses tournament selection and mutation operators for the purposes
Peter D. Turney
1995-01-01
This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algo- rithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors.
An Introduction to Genetic Algorithms and to Their Use in Information Retrieval.
ERIC Educational Resources Information Center
Jones, Gareth; And Others
1994-01-01
Genetic algorithms, a class of nondeterministic algorithms in which the role of chance makes the precise nature of a solution impossible to guarantee, seem to be well suited to combinatorial-optimization problems in information retrieval. Provides an introduction to techniques and characteristics of genetic algorithms and illustrates their…
Short-term load forecasting using optimized neural network with genetic algorithm
Liang Tian; Afzel Noore
2004-01-01
An optimized neural network modeling approach with genetic algorithm for short-term load forecasting based on only multiple delayed historical power load data is proposed. Genetic algorithm is used to globally optimize the number of delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
Gang Leng; Thomas Martin McGinnity; Girijesh Prasad
2006-01-01
A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the -completeness of fuzzy rules is first used to generate the
Na Li; Peng Du; Huijie Zhao
2005-01-01
To avoid the restriction of neuron activation functions of neural learning algorithm and the disadvantage of getting into local optimum solution with general numerical computation method, a novel independent component analysis (ICA) based on improved quantum genetic algorithm (IQGA) is proposed in our paper. Moreover, Han's quantum genetic algorithm (QGA) is improved by adopting the quantum crossover and quantum mutation
New evolutionary genetic algorithms for NP-complete combinatorial optimization problems
Fam Quang Bac; V. L. Perov
1993-01-01
Evolutionary genetic algorithms have been proposed to solve NP-complete combinatorial optimization problems. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithms were described as stochastic processes, using population dynamics and interactive markovian chains. The proposed algorithms were used in solving flowshop problems and an asymmetric traveling salesman problem. The experimental
Fernandez, Thomas
Comparative application of artificial neural networks and genetic algorithms for multivariate time of artificial neural networks and genetic algorithms in terms of forecasting and understanding of algal blooms algorithms not only perform better in seven-days-ahead predictions of algal blooms than artificial neural
VRP Problem with Time Windows in the Logistics and Distribution Solved by Immune Genetic Algorithm
Li-Min Zhi; Jun Zhi; De Zhi; Yu Jin; Li-Ping Song
2009-01-01
It has set up a mathematics model of VRP problem with time windows and has proposed a kind of improved immune genetic algorithm. On the basis of the standard genetic algorithm, it joins in the self-adaptation cross operator and heuristic mutation operator based on triangle show, and uses niche technique to improve the immune algorithm. The experimental result indicates that
Optimization Design of Following Penetrating Bomb Based on Improved Genetic Algorithms
Cheng Yanrong; Xue Zhengyu; Lu Tingjin
2010-01-01
Improved genetic algorithm is adopted to model the following penetrating bomb. Comparing the optimization algorithm with the original one, the numerical simulation shows that the projectile structure has better shape to reduce the resistance with shorter flying time. The improved genetic algorithm is more effective in realizing the global optimization and promoting evolution efficiency, and has stronger adaptability in solving
Kansas, University of
Combining Genetic Algorithms and Case-Based Reasoning for Genetic Learning of a Casebase: A Conceptual@ittc.ukans.edu Abstract In this paper, we present a conceptual frame- work that combines genetic algorithms and case as the system runs. We propose to use genetic algorithms to gen- erate useful cases since there is not any
EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME
J. THEILER; ET AL
1999-06-01
The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.
A genetic algorithm for assembling chromosome physical maps
Fickett, J.W.; Cinkosky, M.J. [Los Alamos National Lab., NM (United States)
1993-12-31
Physical map assembly typically begins with a number of pairwise relationships between clones, and from these produces an overall arrangement of the clones. When there are only a few clones, an investigator can keep in mind all of the relevant data, and can weigh the evidence to produce a map that fits all the experimental results reasonably well. Today, however, it is common to build maps with thousands of clones and millions of pairwise relationships. Computer aided map assembly is thus required. Current computer algorithms typically use only a small fraction of available experimental results, and sometimes fail to deal adequately with inconsistency in the data. The assembly problem is here framed as optimizing a map to fit all the experimental data, and a genetic algorithm to search for optimal maps is described (in genetic algorithms, possible solutions to a problem are treated as individuals in an evolving population). The method has been used to construct or improve ordered clone maps for large parts of human chromosome 16.
Application of genetic algorithms to tuning fuzzy control systems
NASA Technical Reports Server (NTRS)
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
A step forward in studying the compact genetic algorithm.
Rastegar, Reza; Hariri, Arash
2006-01-01
The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized. PMID:16903794
Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm
O. T. Kosmas; D. S. Vlachos
2009-05-04
Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.
A genetic algorithm based method for docking flexible molecules
Judson, R.S. [Sandia National Labs., Livermore, CA (United States); Jaeger, E.P.; Treasurywala, A.M. [Sterling-Winthrop Inc., Collegeville, PA (United States)
1993-11-01
The authors describe a computational method for docking flexible molecules into protein binding sites. The method uses a genetic algorithm (GA) to search the combined conformation/orientation space of the molecule to find low energy conformation. Several techniques are described that increase the efficiency of the basic search method. These include the use of several interacting GA subpopulations or niches; the use of a growing algorithm that initially docks only a small part of the molecule; and the use of gradient minimization during the search. To illustrate the method, they dock Cbz-GlyP-Leu-Leu (ZGLL) into thermolysin. This system was chosen because a well refined crystal structure is available and because another docking method had previously been tested on this system. Their method is able to find conformations that lie physically close to and in some cases lower in energy than the crystal conformation in reasonable periods of time on readily available hardware.
Genetic Algorithms: A New Method for Neutron Beam Spectral Characterization
David W. Freeman
2000-06-04
A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron beams associated with boron neutron capture therapy (BNCT). The GA unfolding technique has been successfully applied to problems with moderate energy resolution (up to 47 energy groups). Initial research indicates that the GA unfolding technique may well be superior to popular unfolding methods in common use. Research now under way at Kansas State University is focused on optimizing the unfolding algorithm and expanding its energy resolution to unfold detailed beam spectra based on multiple foil measurements. Indications are that the final code will significantly outperform current, state-of-the-art codes in use by the scientific community.
A Genetic Algorithm for Solving the Generalized Vehicle Routing Problem
NASA Astrophysics Data System (ADS)
Pop, P. C.; Matei, O.; Sitar, C. Pop; Chira, C.
The generalized vehicle routing problem is a variant of the well-known vehicle routing problem in which the nodes of a graph are partitioned into a given number of node sets (clusters) and the objective is to find the minimum-cost delivery or collection of routes, subject to capacity restrictions, from a given depot to the number of predefined clusters passing through one node from each clusters. We present an effective metaheuristic algorithm for the problem based on genetic algorithms. The proposed metaheuristic is competitive with other heuristics published to date in both solution quality and computation time. Computational results for benchmarks problems are reported and the results point out that GA is an appropriate method to explore the search space of this complex problem and leads to good solutions in a short amount of time.
An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems
Salcedo-Sanz, S.; Del Ser, J.; Geem, Z. W.
2014-01-01
This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases. PMID:24977235
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2013-03-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2012-04-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
S. P. Brumby; N. R. Harvey; S. J. Perkins; R. B. Porter; John J. Szymanski; James Theiler; Jeffrey J. Bloch
2000-01-01
We describe the implementation and performance of a genetic algorithm (GA) which evolves and combines image processing tools for multispectral imagery (MSI) datasets. Existing algorithms for particular features can also be \\