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...
Genetic Algorithms and Quantum Computation
Gilson A. Giraldi; Renato Portugal; Ricardo N. Thess
2004-01-01
Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations
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.
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.
Genetic Algorithms and Quantum Computation
Gilson A. Giraldi; Renato Portugal; Ricardo N. Thess
2004-01-01
Recently, researchers have applied genetic algorithms (GAs) to address some\\u000aproblems in quantum computation. Also, there has been some works in the\\u000adesigning of genetic algorithms based on quantum theoretical concepts and\\u000atechniques. The so called Quantum Evolutionary Programming has two major\\u000asub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic\\u000aAlgorithms (QGAs). The former adopts qubit chromosomes as representations
A new immune genetic algorithm
Yan Lu; Ran Dai; Xiangting Wu; Guanglei Xia
2010-01-01
The application of genetic algorithm is widely, but it is easy to premature convergence and is inadequate about the local searching optimization ability. In this paper, a new immune genetic algorithm (IGA) is proposed. Experiments are done to compare the proposed algorithm with the standard GA, and the results indicate that the proposed IGA's optimization results and converging speed are
GENETIC ALGORITHMS CONTROL SYSTEMS ENGINEERING
Coello, Carlos A. Coello
GENETIC ALGORITHMS IN CONTROL SYSTEMS ENGINEERING P. J. FLEMING R. C. PURSHOUSE Department. 789 May 2001 #12;Genetic algorithms in control systems engineering P. J. Fleming and R. C. Purshouse of Automatic Control and Systems Engineering University of Sheffield Sheffield, S1 3JD UK Research Report No
The Distributed Genetic Algorithm Revisited
Theodore C. Belding
1995-01-01
This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of difficult, randomly-generated Walsh polynomials. This left open the question of whether the DGA would have similar success on functions that were more amenable to optimization by the CGA. In this
Messy genetic algorithms: Recent developments
Kargupta, H. [Los Alamos National Lab., NM (United States). Computational Science Methods Group
1996-09-01
Messy genetic algorithms define a rare class of algorithms that realize the need for detecting appropriate relations among members of the search domain in optimization. This paper reviews earlier works in messy genetic algorithms and describes some recent developments. It also describes the gene expression messy GA (GEMGA)--an {Omicron}({Lambda}{sup {kappa}}({ell}{sup 2} + {kappa})) sample complexity algorithm for the class of order-{kappa} delineable problems (problems that can be solved by considering no higher than order-{kappa} relations) of size {ell} and alphabet size {Lambda}. Experimental results are presented to demonstrate the scalability of the GEMGA.
Genetic algorithm for mobiles equilibrium
Mohamed Moustafa; M. Naghshineh
2000-01-01
An adaptive algorithm is proposed for controlling mobile users transmitter power and information bit rate cooperatively in CDMA networks. The active component of this scheme is called genetic algorithm for mobiles equilibrium (GAME). Based on an evolutionary computational model, the base station tries to achieve an adequate equilibrium between its users. Thereof, each mobile call send its traffic with a
Genetic algorithms and their applications
K. S. Tang; K. F. Man; S. Kwong; Q. He
1996-01-01
This article introduces the genetic algorithm (GA) as an emerging optimization algorithm for signal processing. After a discussion of traditional optimization techniques, it reviews the fundamental operations of a simple GA and discusses procedures to improve its functionality. The properties of the GA that relate to signal processing are summarized, and a number of applications, such as IIR adaptive filtering,
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.
Genetic algorithms in engineering electromagnetics
J. Michael Johnson; V. Rahmat-Samii
1997-01-01
This paper presents a tutorial and overview of genetic algorithms for electromagnetic optimization. Genetic-algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and the GA is discussed. Step-by-step implementation aspects of the GA are detailed, through an example with the objective of providing useful guidelines for
Distributed Probabilistic Model-Building Genetic Algorithm
Dongarra, Jack
Distributed Probabilistic Model-Building Genetic Algorithm Tomoyuki Hiroyasu1 , Mitsunori Miki1 Abstract. In this paper, a new model of Probabilistic Model-Building Genetic Algorithms (PMBGAs discussed. 1 Introduction Genetic Algorithms (GAs) are stochastic search algorithms based on the me- chanics
Genetic algorithms in computer aided design
Gábor Renner; Anikó Ekárt
2003-01-01
Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems in engineering. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way. Genetic algorithms (GA) transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they
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 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
Genetic algorithms and simulated annealing
Lawrence Davis
1987-01-01
This RESEARCH NOTE is a collection of papers on two types of stochastic search techniques-genetic algorithms and simulated annealing. These two techniques have been applied to problems that are both difficult and important, such as designing semiconductor layouts, controlling factories, and making communication networks cheaper, to name a few. Both techniques are modeled on processes found in nature-natural evolution and
Simultaneous stabilization using genetic algorithms
Benson, R.W.; Schmitendorf, W.E. (California Univ., Irvine, CA (USA). Dept. of Mechanical Engineering)
1991-01-01
This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.
Genetic Algorithms: Basic principles and applications
C. A. Murthy
2012-01-01
Genetic Algorithms are a part of Soft Computing Techniques that deal with function optimization. The basic principles of Genetic Algorithms are stated. Its stochastic nature and various genetic operators are discussed. Some basic issues (e.g., convergence) related to these algorithms are also discussed.
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
An Introduction to Genetic Algorithms Kalyanmoy Deb
Srivastava, Kumar Vaibhav
An Introduction to Genetic Algorithms Kalyanmoy Deb Kanpur Genetic Algorithms Laboratory (KanGAL) Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur, PIN 208 016, India EÂmail: deb@iitk.ernet.in Abstract Genetic algorithms (GAs) are search and optimization tools, which work
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
Learning Intelligent Genetic Algorithms Using Japanese Nonograms
ERIC Educational Resources Information Center
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen
2012-01-01
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
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.
A Genetic Algorithm Tutorial Darrell Whitley
Evett, Matthew
. These algorithms encode a potential solution to a speci c problem on a simple chromosome-like data structure algorithms are often viewed as function optimizers, although the range of problems to which geneticA Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University
A Genetic Algorithm Tutorial Darrell Whitley
Whitley, Darrell
. These algorithms encode a potential solution to a specific problem on a simple chromosomeÂlike data structure algorithms are often viewed as function optimizers, although the range of problems to which geneticA Genetic Algorithm Tutorial Darrell Whitley Computer Science Department, Colorado State University
Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma
Krings, Axel W.
Dynamic Populations in Genetic Algorithms Zhanshan (Sam) Ma University of Idaho Computer Science of genetic algorithms (GA). In particular, we test five dynamic population-sizing patterns: random. General Terms Algorithms. Keywords: Dynamic Population, Fluctuating Population, Genetic Algorithms
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,
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.
Genetic Algorithms in Engineering and Computer Science
Edited J. P'eriaux; G. Winter; John Wiley Sons; Thomas Back
1995-01-01
Contents 13 Parallel Genetic Algorithms for Optimisation in CFD : : : : : : : : 1 13.1 INTRODUCTION : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 13.2 CFD ANALYSIS FOR AEROSPACE DESIGN : : : :
Genetic Algorithms and Evolutionary Darrell Whitley
Whitley, Darrell
and evolutionary algorithms encode a potential solution to a speÂ cific problem on a chromosomeÂlike data structure operators are also used to alter potential solutions. An implementation of a genetic algorithm begins algorithms do not use gradient information. Thus, solutions may be evaluated using a mathematical function
Genetic Algorithms using Populations based on
António Manso; Luís Correia
Abstract. The traditional representation of the populations used in evolutionary algorithms raises two types of problems: the loss of genetic diversity during the evolutionary process and evaluation of redundant individuals. In [11, 12] the authors propose a new formal model (PLATO) for multiset representation of individuals and their populations which applied to heuristic algorithms, minimizes the problems identified above. This paper presents a computational representation of populations based in multisets, and the adaptation of the genetic algorithm to deal with this type of representation, the Multiset Genetic Algorithm (MGA). A new operator called rescaling is developed as well as a metric to measure genetic diversity. The standard genetic algorithm is applied to some types of problems using the standard and the new type of populations and empirical results shows the genetic diversity is increased and the number of individuals evaluated is decreased as expected.
Biomimetic use of genetic algorithms
Dessalles, Jean-Louis
2011-01-01
Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature. But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards. We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection. One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role. Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations.
Combining Genetic Algorithms and Neural Networks
Koehn, Philipp
and genetic algorithms demonstrate powerful problem solving ability. They are based on quite simple principlesÂ 1 Â Combining Genetic Algorithms and Neural Networks: The Encoding Problem A Thesis Presented learning showed results by searching for various kinds of functions. However, the choice of the basic
A Versatile Genetic Algorithm for Network Planning
Riedl, Anton
first generation with all kinds of possible gene structures, natural selection suggests that over is applied to two very different fields in network planning. First, it is used to minimize the costs of fiber;2. How do Genetic Algorithms Work? Basic Idea Genetic algorithms were first developed by John Holland [1
A genetic algorithm framework for test generation
Elizabeth M. Rudnick; Janak H. Patel; Gary S. Greenstein; Thomas M. Niermann
1997-01-01
Test generation using deterministic fault-oriented algorithms is highly complex and time consuming. New approaches are needed to augment the existing techniques, both to reduce execution time and to improve fault coverage. Genetic algorithms (GA's) have been effective in solving many search and optimization problems. Since test generation is a search process over a large vector space, it is an ideal
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 the immune system
Forrest, S. (New Mexico Univ., Albuquerque, NM (USA). Dept. of Computer Science); Perelson, A.S. (Los Alamos National Lab., NM (USA))
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. 8 refs.
Hyperplane Ranking in Simple Genetic Algorithms
Pyeatt, Larry
Hyperplane Ranking in Simple Genetic Algorithms D. Whitley, K. Mathias, and L. Pyeatt Department,mathiask,pyeatt@cs.colostate.edu Abstract We examine the role of hyperplane ranking during genetic search by developing a metÂ ric the function, as well as the dynamic ranking of hyperplanes during genetic search. The metric applied to static
Genetic algorithms at UC Davis/LLNL
Vemuri, V.R. [comp.
1993-12-31
A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.
Rank-density based multiobjective genetic algorithm
Haiming Lu; Gary G. Yen
2002-01-01
In this paper, a new evolutionary approach, the rank-density based genetic algorithm (RDGA), to multiobjective optimization problems is proposed. In RDGA, a new ranking method, called an automatic accumulated ranking strategy and a \\
Genetic algorithms and supernovae type Ia analysis
NASA Astrophysics Data System (ADS)
Bogdanos, Charalampos; Nesseris, Savvas
2009-05-01
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) ? PDE/?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.
Genetic algorithm based tomographic flow visualization
Lyons, Donald Paul
1997-01-01
the field of evolutionary computing. For verification of the technique, both axisymmetric and asymmetric phantom density fields are tested for a limited number of projections under interferometric visualization. For the hybrid genetic algorithm...
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.
Liyi Zhang; Ting Liu; Yunshan Sun; Lei Chen
2010-01-01
Aimed at the shortcoming of neural network blind equalization algorithm, namely, the structure of neural network is difficult to determine, two basic principles of neural network blind equalization algorithm optimized by genetic algorithm were analyzed in the paper, by combining genetic algorithm and neural network blind equalization algorithm. At first, the structure and weight of neural network were optimized together
A Distributed Pool Architecture for Genetic Algorithms
Roy, Gautam
2011-02-22
A DISTRIBUTED POOL ARCHITECTURE FOR GENETIC ALGORITHMS A Thesis by GAUTAM SAMARENDRA N ROY Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December... 2009 Major Subject: Computer Engineering A DISTRIBUTED POOL ARCHITECTURE FOR GENETIC ALGORITHMS A Thesis by GAUTAM SAMARENDRA N ROY Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements...
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.
Genetic algorithm for disassembly strategy definition
NASA Astrophysics Data System (ADS)
Caccia, Claudio; Pozzetti, Alessandro
2001-02-01
The paper presents the application of a genetic algorithm to determine strategies for disassembly of products that have reached the end of their life. First, a general outline of the proposed methodology is provided and the features and specific properties of the genetic algorithm are described. Then an analysis of the algorithm's behaviour is carried out based on different problems. Once product structure is acquired, feasible disassembly alternatives may be determined; the domain of solutions may then be analysed through the genetic algorithm. First of all, a 'population' of acceptable solutions is randomly generated; then these solutions are estimated based on the criteria of the highest recovery value and the minimisation of discharged parts: genetic mutation and crossover operators are applied to the current population in order to generate a new population as a substitute to the previous one. Some cycles are made estimating, each time, the goodness of each individual solution and its probability to 'reproduce' itself. At the end, the best-rated alternative becomes the solution of the algorithm. The solution of the algorithm is compared to the one provided by a 'best-first' algorithm (providing the optimal solution), for different types of products. In the paper, the efficacy of the proposed methodology is analysed, in terms of type of solution and computation time.
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.
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
A hybrid of the genetic algorithm and concurrent simplex
Randolph, David Ethan
1995-01-01
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... will introduce an example problem; later that problem will be used to illustrate how a genetic algorithm arrives at its solutions. 1. A Toy Problem Consider the eight queens puzzle, a well ? known problem in chess. In the game of chess, the most powerful...
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
A. E. Eiben; Emile H. L. Aarts; Kees M. Van Hee
1990-01-01
In this paper we are trying to make a step towards a concise theory of genetic algorithms (GAs) and simulated annealing (SA). First, we set up an abstract stochastic algorithm for treating combinatorial optimization problems. This algorithm generalizes and unifies genetic algorithms and simulated annealing, such that any GA or SA algorithm at hand is an instance of our abstract
Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
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.
Forecasting chaotic time series with genetic algorithms
NASA Astrophysics Data System (ADS)
Szpiro, George G.
1997-03-01
This paper proposes the use of genetic algorithms-search procedures, modeled on the Darwinian theories of natural selection and survival of the fittest-to find equations that describe the behavior of a time series. The method permits global forecasts of such series. Very little data are sufficient to utilize the method and, as a byproduct, these algorithms sometimes indicate the functional form of the dynamic that underlies the data. The algorithms are tested with clean as well as with noisy chaotic data, and with the sunspot series.
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
MULTIPLE CRITERIA GENETIC ALGORITHMS IN ENGINEERING DESIGN AND OPERATION
Coello, Carlos A. Coello
MULTIPLE CRITERIA GENETIC ALGORITHMS IN ENGINEERING DESIGN AND OPERATION A Thesis Submitted. Pratyush Sen Department of Marine Technology UNIVERSITY OF NEWCASTLE #12; Multiple Criteria Genetic of Genetic Algorithms (GAs) to multiple criteria problems in engineering design and operation. The GA
Searching for Diverse, Cooperative Populations with Genetic Algorithms
Forrest, Stephanie
Searching for Diverse, Cooperative Populations with Genetic Algorithms Robert E. Smith Dept Abstract In typical applications, genetic algorithms (GAs) process populations of potential problem a population is necessary for the long term success of any evolutionary system. Genetic diversity helps
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.
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.
A distributed pool architecture for genetic algorithms
Gautam Roy; Hyunyoung Lee; Jennifer L. Welch; Yuan Zhao; Vijitashwa Pandey; Deborah L. Thurston
2009-01-01
The genetic algorithm (GA) paradigm is a well-known heuristic for solving many problems in science and engineering. As problem sizes increase, a natural question is how to exploit advances in distributed and parallel computing to speed up the execution of GAs. This paper proposes a new distributed architecture for GAs, based on distributed storage of the individuals in a persistent
Dynamic Parameter Encoding for Genetic Algorithms \\Lambda
Schraudolph, Nicol N.
Dynamic Parameter Encoding for Genetic Algorithms \\Lambda Nicol N. Schraudolph Richard K. Belew the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding program and can be obained via Internet file transfer or electronic mail from the first author
Dynamic Parameter Encoding for Genetic Algorithms
Belew, Richard K.
Dynamic Parameter Encoding for Genetic Algorithms Nicol N. Schraudolph Richard K. Belew nici the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding program and can be obained via Internet file transfer or electronic mail from the first author. 2 We use
Dynamic Parameter Encoding for Genetic Algorithms
Schraudolph, Nicol N.
Dynamic Parameter Encoding for Genetic Algorithms Nicol N. Schraudolph Richard K. Belew nici of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism enhancements -- user- transparent DPE. It is a public domain program and can be obained via Internet file
Evolving Quantum Circuits Using Genetic Algorithm
Martin Lukac; Marek A. Perkowski
2002-01-01
In this paper we focus on a general approach of using genetic algorithm (GA) to evolve Quantum circuits (QC). We propose a generic GA to evolve arbitrary quantum circuit specified by a (target) unitary matrix as well as a specific encoding that reduces the time of calculating the resultant unitary matrices of chromosomes. We demonstrate that, in contrast to previous
Designing fuzzy net controllers using genetic algorithms
Jinwoo Kim; Yoonkeon Moon; Bernard P. Zeigler
1995-01-01
As control system tasks become more demanding, more robust controller design methodologies are needed. A genetic algorithm (GA) optimizer, which utilizes natural evolution strategies, offers a promising technology that supports optimization of the parameters of fuzzy logic and other parameterized nonlinear controllers. This article shows how GAs can effectively and efficiently optimize the performance of fuzzy net controllers employing high
Passive filter design using genetic algorithms
Yaow-Ming Chen
2003-01-01
The objective of this paper is to propose a new approach for designing a passive LC filter of the full-bridge rectifier by using genetic algorithms (GAs). The performance of the cost-effective passive LC filter for a constant load depends on the appropriate inductor and capacitor selection. Several design methods are reviewed and a novel design methodology using GAs is proposed
Scheduling Multiprocessor Tasks with Genetic Algorithms
Ricardo C. Corrêa; Afonso Ferreira; Pascal Rebreyend
1999-01-01
In the multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. We propose a new combined approach, where a genetic algorithm is improved with the introduction of some knowledge
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.
Multicriteria inventory classification using a genetic algorithm
H. Altay Guvenir; Erdal Erel
1998-01-01
One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed,
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
Turbo codes optimization using genetic algorithms
Nicolas Durand; Jean-Marc Alliot; B. Bartolome
1999-01-01
Turbo codes have been an important revolution in the digital communications world. Since their discovery, the coding community has been trying to understand, explain and improve turbo codes. The floor phenomenon is the parallel concatenated convolutional turbo codes main problem. In this paper, genetic algorithms are used to lower the free distance of such a code. Results in terms of
Hybrid Genetic Algorithms for Feature Selection
Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon
2004-01-01
This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and
Binary wavefront optimization using a genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Xiaolong; Kner, Peter
2014-12-01
We demonstrate the use of a genetic algorithm with binary amplitude modulation of light through turbid media. We apply this method to binary amplitude modulation with a digital micromirror device. We achieve the theoretical maximum enhancement of 64 with 384 segments, and an enhancement of 320 with 6144 segments.
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
Medical Image Segmentation Using Genetic Algorithms
Ujjwal Maulik
2009-01-01
Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ\\/tissue boundaries. The resulting search space is
K-Means Clustering Analysis Based on Genetic Algorithm
LAI Yu-xia; LIU Jian-ping; YANG Guo-xing
2008-01-01
(Abstract)Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value. To solve such problems, this paper presents an improved K-Means algorithm based on genetic algorithm. It combines the locally searching capability of the K-Means with the global optimization capability of genetic algorithm, and introduces the K-Means operation into the genetic algorithm of
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.
Production scheduling and rescheduling with genetic algorithms.
Bierwirth, C; Mattfeld, D C
1999-01-01
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs. PMID:10199993
Multiple protein sequence comparison by genetic algorithms
NASA Astrophysics Data System (ADS)
Gonzalez, Raquel R.; Izquierdo, Carmen M.; Seijas, Juan
1998-03-01
In the analysis of molecular evolution, it is very frequent to consider M sequences at a time, where M greater than 2. The simultaneous study of the relationships among M sequences is a large and difficult problem. This paper presents a new approach to multiple protein sequence comparison based on Genetic Algorithms, (G.A.). In particular, it is described an algorithm for finding the alignment of three protein sequences; besides, it can be easily changed for finding the alignment of more than three sequences or for other types of sequences. The G.A. was originally developed for only two sequences comparison [Morato96].
An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions
Whitley, Darrell
An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions Keith Mathias particular potential as a tool for optimization when the evaluation function is noisy. Several types Gaussian noise has been injected into the evaluation function. The genetic algorithms used
Clonal Selection based Genetic Algorithm for Workflow Service Selection
Ludwig, Simone
Clonal Selection based Genetic Algorithm for Workflow Service Selection Simone A. Ludwig North for requested workflows. Genetic algorithm is one such method that can find approximate solutions in the form of services selected. In this paper, we propose an improved version of the standard genetic algorithm approach
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
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
Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function
Coello, Carlos A. Coello
Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization Kwong with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has;Adaptive Elitist-Population Based Genetic Algorithm 1161 some fundamental dilemmas in EAs implementation
Performance Evaluation of Genetic Algorithms for Flowshop Scheduling Problems
Tadahiko Murata; Hisao Ishibuchi
1994-01-01
The aim of this paper is to evaluate the performance of genetic algorithms for the flowshop scheduling problem with an objective of minimizing the makespan. First we examine various genetic operators for the scheduling problem. Next we compare genetic algorithms with other search algorithms such as local search, taboo search and simulated annealing. By computer simulations, it is shown that
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 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 multi-sexual genetic algorithm for multiobjective optimization
Joanna Lis; A. E. Eiben
1997-01-01
In this paper a new method for solving multicriteria optimization problems by Genetic Algorithms is proposed. Standard Genetic Algorithms use a population, where each individual has the same sex (or has no sex) and any two individuals can be crossed over. In the proposed Multisexual Genetic Algorithm (MSGA), individuals have an additional feature, their sex or gender and one individual
Multiobjective simulation optimization using an enhanced genetic algorithm
Hamidreza Eskandari; Luis Rabelo; Mansooreh Mollaghasemi
2005-01-01
This paper presents an improved genetic algorithm ap- proach, based on new ranking strategy, to conduct multi- objective optimization of simulation modeling problems. This approach integrates a simulation model with stochas- tic nondomination-based multiobjective optimization tech- nique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of find- ing Pareto optimal solutions and its efficiency in
Holographic diffuser design using a modified genetic algorithm
Yao, Jianping
Holographic diffuser design using a modified genetic algorithm Mengtao Wen Jianping Yao, MEMBER Singapore 639798 Abstract. A modified genetic algorithm is proposed for the optimization of holographic diffusers for diffuse IR wireless home networking. The novel algorithm combines the conventional genetic
Birefringent filter design by use of a modified genetic algorithm
Yao, Jianping
Birefringent filter design by use of a modified genetic algorithm Mengtao Wen and Jianping Yao A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation angles and the element lengths are determined by the genetic algorithm to minimize the sidelobe levels
Genetic algorithm dynamics on a rugged landscape Stefan Bornholdt*
Bornholdt, Stefan
far from equilibrium, as are genetic algorithms, one can sometimes identify distribu- tions that tendGenetic algorithm dynamics on a rugged landscape Stefan Bornholdt* Institut fu¨r Theoretische manuscript received 5 December 1997 The genetic algorithm is an optimization procedure motivated
Towards a Genetic Programming Algorithm for Automatically Evolving Rule Induction Algorithms
Fernandez, Thomas
. In general, the program evolved by GP can produce the same solution humans use to solve the target problemTowards a Genetic Programming Algorithm for Automatically Evolving Rule Induction Algorithms Gisele for auto- matically evolving computer programs. This work proposes a genetic pro- gramming algorithm
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.
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.
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.
Genetic Algorithms Connecting evolution and learning
Indiana University
based on current generation Â a(o,t) = Pcross(L(o)/K)/P(o,t)) Â· Pcross = probability that a selected and a difficult search) Â· Crossover Â· Mutation #12;The Essential Genetic Algorithm #12;A Simple GA example #12 of the population occupied by instances of schema o at time t+1 Â [U(o,t)/U(t)] = ratio of average fitness value
Self-Organizing Genetic Algorithm: A Survey
Self-organization systems are an increasingly attractive dynamic processes without a central control, emerge global order from local interactions in a bottom up approach. The advantage of blending the concept of self-organization enhances the working efficiency of other techniques to find a solution of huge search problem. Genetic Algorithms (GA) is such a technique, inspired by the natural evolution process, used to solve difficult optimization problem of large space solution, for an example, multiple sequence alignment (MSA) problem in a bioinformatics research. Self-organization technique automates the selection of appropriate parameter values of GA during execution without the user’s intervention. An attempt towards applying Self-organizing Genetic Algorithm (SOGA) on MSA requires a complete knowledge of the various parameters of SO and its relationships. This lead us to make a complete survey on inherent properties of SO and the method of blending GA in order to develop a self-organizing genetic algorithm (SOGA) for MSA. The aim of the research is to make use of the efficiency of GA without getting any input from the nontrained users to tune the parameters in order to achieve the expected result.
Evolutionary Computation: from Genetic Algorithms to Genetic Programming
Fernandez, Thomas
1 Evolutionary Computation: from Genetic Algorithms to Genetic Programming Ajith Abraham1 , Nadia Nedjah2 , and Luiza de Macedo Mourelle3 1 School of Computer Science and Engineering Chung-Ang University 410, 2nd Engineering Building 221, Heukseok-dong, Dongjak-gu Seoul 156-756, Korea ajith
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
NASA Technical Reports Server (NTRS)
Burchett, Bradley T.
2003-01-01
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
V.: A genetic engineering approach to genetic algorithms
John S. Gero; Vladimir Kazakov
We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.
Path planning for autonomous UAV via vibrational genetic algorithm
Y. Volkan Pehlivanoglu; Oktay Baysal; Abdurrahman Hacioglu
2007-01-01
Purpose – It is aimed to provide an efficient algorithm for path planning in guidance of autonomous unmanned aerial vehicle (UAV) through 3D terrain environments. Design\\/methodology\\/approach – As a stochastic search method, vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for path planning. Findings – Using VGA, an efficient path planning algorithm for autonomous UAV was
Selection of relevant features in a fuzzy genetic learning algorithm
Antonio González; Raúl Pérez
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
Particle swarm optimization versus genetic algorithms for phased array synthesis
Daniel W. Boeringer; Douglas H. Werner
2004-01-01
Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array
Design of active suspension based on genetic algorithm
Chongzhi Song; Youqun Zhao; Lu Wang
2008-01-01
Control algorithms are developed for force control in an active vehicle suspension design using genetic algorithms with quarter-car model. Force cancellation, virtual damper, skyhook damper, and road-following concepts are proposed to design the force controller for achieving better ride and handling quality. Genetic algorithms are employed to obtain a more effective search for optimum control parameters. Computer simulations are performed
Genetic Algorithms To provide a background and understanding of basic genetic
Qu, Rong
and have been successfully applied to complex engineering optimisation problems. Genetic Algorithms1 Genetic Algorithms Objectives To provide a background and understanding of basic genetic algorithms and some of their applications. Â·a basic genetic algorithm Â·the basic discussion Â·the applications
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.
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.
Adaptive Control of Third Harmonic Generation via Genetic Algorithm
Hua, Xia
2010-10-12
Genetic algorithm is often used to find the global optimum in a multi-dimensional search problem. Inspired by the natural evolution process, this algorithm employs three reproduction strategies -- cloning, crossover and mutation -- combined...
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 ...
Saving Resources with Plagues in Genetic Algorithms
de Vega, F F; Cantu-Paz, E; Lopez, J I; Manzano, T
2004-06-15
The population size of genetic algorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to genetic algorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.
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.
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.
Parallel Genetic Algorithm for Alpha Spectra Fitting
NASA Astrophysics Data System (ADS)
García-Orellana, Carlos J.; Rubio-Montero, Pilar; González-Velasco, Horacio
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 cluster for parallel simulation. The relationship between simulation time (and parallel efficiency) and processors number is studied using several alpha spectra, with the aim of obtaining a method to estimate the optimal processors number that must be used in a simulation.
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
Michael Affenzeller
2001-01-01
Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, GeneticAlgorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially for
Michael Afienzeller
2001-01-01
Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved e-ciently only by heuristics. Both, Genetic Algorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially
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.
A Genetic Algorithm for Designing Constellations with Low Error Floors
Valenti, Matthew C.
and Electrical Engineering West Virginia UniveGenetic Algorithm March 21, 2008 1 / 21 #12;Outline 1 Introduction Department of Computer Science and Electrical Engineering West Virginia UniveGenetic Algorithm March 21, 2008 Valenti et al. ( Lane Department of Computer Science and Electrical Engineering West Virginia UniveGenetic
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
Instrument design and optimization using genetic algorithms
Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter [Institut Laue-Langevin, BP 156, F-38042 Grenoble Cedex 9 (France); Hahn-Meitner Institut, Glienicker Strasse 100, D-14109 Berlin (Germany); Institut Laue-Langevin, BP 156, F-38042, Grenoble Cedex 9 (France)
2006-10-15
This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.
Rational function optimization using genetic algorithms
NASA Astrophysics Data System (ADS)
Valadan Zoej, M. J.; Mokhtarzade, M.; Mansourian, A.; Ebadi, H.; Sadeghian, S.
2007-12-01
In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates.
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
Model Generation for an Intrusion Detection System Using Genetic Algorithms
Adhitya Chittur
Abstract This experiment analyzed theeffectiveness of a genetic algorithm applied to the detection of computer intrusions and malicious computer behavior. The use of genetic algorithms to detect malicious computer behavior is a novel approach,to the computer,network,intrusion detection problem presented in designing an Intrusion Detection System.A genetic algorithm is a method of artificial intelligence problem-solving based on the theory of Darwinian
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\\
THE CHOPPER GENETIC ALGORITHM A VARIABLE POPULATION GA
Nicholas E. Chop; Dr. David Calvert
Genetic algorithms typically use fixed population sizes. Simple genetic algorithms replace their entire populations at each time step, while partial replacement algorithms replace only a portion of their population. This paper proposes the Chopper variable population genetic algorithm. The Chopper algorithm draws inspiration from the idea that a biological population will expand to the capacity of the food supply, as well as the idea that a large population inside a genetic population does little good if it has a low diversity. At each generation, the population is bred amongst itself to create a larger pool. Then, based on the diversity of the population, a number of culls are performed, shrinking the population size. If the population is very diverse, then fewer culls are performed. If, however, the population has a low diversity, more culls are performed in the hope that redundant genetic information is eliminated. The Chopper algorithm shows promise in cases where only a few comparisons can be performed.
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
A simple elitist genetic algorithm for constrained optimization
Sangameswar Venkatraman; Gary G. Yen
2004-01-01
In this paper we propose a novel approach for solving constrained optimization problems using genetic algorithms. The main emphasis of this algorithm is to be problem independent and to produce consistent results in terms of the quality of feasible solutions. The basic characteristic of this algorithm is the complete ignorance of the objective function till at least one feasible solution
INVERSE DESIGN OF 2-D AIRFOIL VIA VIBRATIONAL GENETIC ALGORITHM
Y. Volkan PEHLIVANOGLU; Abdurrahman HACIOGLU
Within this study, it is aimed to provide an efficient algorithm for inverse design of 2-D airfoil in different flow conditions. For this purpose, as a stochastic search method, current vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for inverse design. From the results obtained, it is concluded that VGA decreased the required time for optimal
Antenna Design With a Mixed Integer Genetic Algorithm
Randy L. Haupt
2007-01-01
Antenna design variables, such as size, have continuous values while others, such as permittivity, have a finite number of values. Having both variable types in one problem requires a mixed integer optimization algorithm. This paper describes a genetic algorithm (GA) that works with real and\\/or binary values in the same chromosome. The algorithm is demonstrated on designing low side-lobe phase
SOLVING THE SIMPLE PLANT LOCATION PROBLEM BY GENETIC ALGORITHM
Jozef Kratica; Vladimir Filipovi; Ivana Ljubi; P. Tolla
2001-01-01
The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorithms.
Solving The Simple Plant Location Problem By Genetic Algorithm
Jozef Kratica; Dusan Tosic; Vladimir Filipovic; Ivana Ljubic
2001-01-01
The simple plant location problem (SPLP) is consideredand a genetic algorithm is proposed to solve this problem. By usingthe developed algorithm it is possible to solve SPLP with more than1000 facility sites and customers. Computational results are presentedand compared to dual based algorithms.
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.
A genetic algorithm for solving supply chain network design model
NASA Astrophysics Data System (ADS)
Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.
2013-09-01
Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.
Spacecraft Attitude Maneuver Planning Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Kornfeld, Richard P.
2004-01-01
A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.
Offline Handwriting Recognition using Genetic Algorithm
Kala, Rahul; Shukla, Anupam; Tiwari, Ritu
2010-01-01
Handwriting Recognition enables a person to scribble something on a piece of paper and then convert it into text. If we look into the practical reality there are enumerable styles in which a character may be written. These styles can be self combined to generate more styles. Even if a small child knows the basic styles a character can be written, he would be able to recognize characters written in styles intermediate between them or formed by their mixture. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing. Using this method we received an accuracy of 98.44%.
PDE Nozzle Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Billings, Dana; Turner, James E. (Technical Monitor)
2000-01-01
Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.
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.
Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
A Hybrid Genetic Algorithm for Routing Optimization in IP Networks
Riedl, Anton
engineering are demonstrated. Keywords Â IP Traffic engineering, genetic algorithm, bandwidth-delay sensitiveA Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay traffic engineering, which relies on conventional, destination-based routing protocols. We introduce
How Genetic Algorithms Can Improve a Pacemaker Effciency
Dumas, Laurent
How Genetic Algorithms Can Improve a Pacemaker Effciency Laurent Dumas Laboratoire Jacques In this paper, we propose the use of Genetic Algorithms as a tool for improving a pacemaker efficiency induced in the sinus node, the natural pacemaker, then propagates through the atria and reaches
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Enrique Alba; José M. Troya
2002-01-01
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo- tivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular genetic algorithms. Afterwards, this study is extended to consider a
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
Fuzzy Neural Networks and Genetic Algorithms for Medical Images Interpretation
Nacér Benamrane; A. Aribi; L. Kraoula
2006-01-01
In this paper, we propose an approach for detection and specification of anomalies present in medical images. The idea is to combine three metaphors: neural networks, fuzzy logic and genetic algorithms in a hybrid system. The neural networks and fuzzy logic metaphors are coupled in one system called fuzzy neural networks. The genetic algorithm adds to this hybridizing the property
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, and a corresponding hardware platform are described. The basic operations of conventional GA (CGA) are made pipelined
A new hybrid genetic algorithm based on chaos and PSO
Yiwen Wang; Min Yao
2009-01-01
In practice, two key problems have been found in genetic algorithm (GA), one is premature convergence and the other is weak local search ability. In this paper, a new hybrid genetic algorithm based on chaos and particle swarm optimization (PSO) is proposed to solve the two problems above. The basic principle is that chaotic search mechanism and PSO mutation are
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.
Watermarking in Contourlet Transform Domain Using Genetic Algorithm
T. Kumaran; P. Thangavel
2008-01-01
Contourlet transform is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks. In this work, we focus on optimizing the image watermarking using the genetic algorithm applied to the contourlet transform which improves the quality of the watermarked image and the robustness of watermark. We employ, genetic algorithm based embedding schemes namely surrounding mean and
Aerosol Layer Discrimination using Laser Radar and Genetic Algorithms
Jo Ann Parikh; Nimmi C. Parikh Sharma
2006-01-01
A technique has been developed to retrieve the height of the top of the aerosol layer from Micro Pulse Lidar (MPL) datasets. The technique combines first derivative estimates of normalized relative backscatter profiles with genetic algorithm refinements. The genetic algorithm is used to explore the gradient profiles to produce temporally coherent results. I. INTRODUCTION The distribution of aerosols over altitude
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
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
Application of genetic algorithm in broadband microstrip antenna design
Si-Yang Sun; Ying-Hua Lu; Dong-Sheng La
2009-01-01
Genetic algorithm is utilized in the optimization of microstrip antenna with complicated structure. A broadband single-patch microstrip patch antenna is proposed. Capacitance compensated technique and E-shaped patch are combined together to expand the bandwidth of the antenna. Genetic algorithm combined with finite element software is applied to optimize the structure of the antenna. The optimization procedure is also discussed. The
A Test of Genetic Algorithms in Relevance Feedback.
ERIC Educational Resources Information Center
Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de
2002-01-01
Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…
A Lamarckian Evolution Strategy for Genetic Algorithms Brian J. Ross
Ross, Brian J.
. 1. Introduction Prior to Charles Darwin's theory of evolution by natural selection, Jean BaptisteA Lamarckian Evolution Strategy for Genetic Algorithms Brian J. Ross Brock University Department implementation of a simple Lamarckian evolution module for genetic algorithms is discussed. Lamarckian evolution
Plasma Xray Spectra Analysis Using Genetic Algorithms Igor E. Golovkin
Louis, Sushil J.
for plasma diagnostics. We use genetic algorithms to automatically analyze experiÂ mental XÂray line spectraPlasma XÂray Spectra Analysis Using Genetic Algorithms Igor E. Golovkin Department of Physics Reno Reno, NV 89557 sushil@cs.unr.edu Abstract XÂray spectroscopic analysis is a powerful tool
Modified Genetic Algorithm for Parameter Selection of Compartmental Models
Neil A. Shah; Richard A. Moffitt; May D. Wang
2007-01-01
A modified genetic algorithm has been developed for the task of optimal parameter selection for compartmental models. As a case study, a predictive model of the emerging health threat of obesity in America was developed which incorporated varying levels of three treatment strategies in an attempt to decrease the amount of overweight Americans over a ten-year period. The genetic algorithm
Application of improved genetic algorithm in camera calibration
NASA Astrophysics Data System (ADS)
Li, Weimin; Liu, Hui; Zhu, Lichun; Zhao, Yu
2014-09-01
With the camera internal parameters known, to calculate the external parameters is to solve a set of highly nonlinear over-determined equations. In this paper, an improved hybrid genetic algorithm is adopted to obtain external parameters. It combines the advantages of genetic algorithm and Newton method, making it possible to obtain results with high accuracy and a faster convergence.
Synthesis of high-impedance FSSs using genetic algorithms
Luigi Lanuzza; Agostino Monorchio; Giuliano Manara
2002-01-01
In this paper, a genetic algorithm-based technique for synthesizing high impedance surfaces is presented. These surfaces behave like a perfect magnetic conductor (PMC) in a certain frequency range; to achieve this result, a multilayered dielectric structure in conjunction with an FSS screen and a perfectly electric conductor (PEC) ground plane have been used. The genetic algorithm (GA) uses an electromagnetic
Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for
Zhu, Mu
Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for Variable Selection Mu outcome of interest commonly arises in various industrial engineering applications. The genetic algorithm modification. Our idea is to run a number of GAs in parallel without allowing each GA to fully converge
Automatic Tuning of Agent-Based Models Using Genetic Algorithms
Paris-Sud XI, Université de
Automatic Tuning of Agent-Based Models Using Genetic Algorithms Beno^it Calvez and Guillaume lead to a radical modification of the dynamics of the whole system. The development and the parameter on suggesting the use of genetic algorithms. The idea is to capture in the fitness func- tion the goal
Evolving dynamic Bayesian networks with Multi-objective genetic algorithms
Brian J. Ross; Eduardo Zuviria
2007-01-01
A dynamic Bayesian network (DBN) is a probabilistic network that models inter- dependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evalua- tion strategy with a genetic algorithm. The multi-objective criteria are a network's
Transport Demand Management in Turkey: A Genetic Algorithm Approach
Soner Haldenbilen; Halim Ceylan
2005-01-01
This article proposes new models for estimating transport demand using a genetic algorithm (GA) approach. Based on population, gross national product and number of vehicles, four forms of the genetic algorithm transport planning (GATP) model are developed – one exponential and the others taking quadratic forms – and applied to Turkey. The best fit models in terms of minimum total
THE NATURE OF NICHING: GENETIC ALGORITHMS AND THE EVOLUTION OF
Coello, Carlos A. Coello
THE NATURE OF NICHING: GENETIC ALGORITHMS AND THE EVOLUTION OF OPTIMAL, COOPERATIVE POPULATIONS #12; c flCopyright by Jeffrey Horn 1997 #12; Abstract Genetic algorithms (GAs) with fitness sharing #12; niche overlap, a stable equilibrium population is quickly found and indefinitely maintained
Nash Genetic Algorithms : examples and applications M. Sefrioui
Coello, Carlos A. Coello
Nash Genetic Algorithms : examples and applications M. Sefrioui LIP6, University Paris 6 4, Place aspects and experimental results on Nash Genetic Algorithms. Nash GAs are an alternative for multiple. They are explained in details, along with the advantages conferred by their equilibrium state. This approach
Concurrent genetic algorithms for optimization of large structures
Hojjat Adeli; Nai-Tsang Cheng
1994-01-01
In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high-rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the
Generating test patterns for VLSI circuits using a genetic algorithm
M. J. O'Dare; T. Arslan
1994-01-01
The authors present the development of a technique that uses genetic algorithms for the generation of test patterns that detect single stuck-at faults in combinational VLSI circuits. As the genetic algorithm evolves, an efficient set of test patterns are produced, by searching the solution space for patterns that detect the highest number of remaining faults in the fault list.
Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong
George Mason University
Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong Computer Science Department George Mason University Fairfax, VA 22030, USA kdejong@aic.gmu.edu Abstract Genetic Algorithms (GAs) have received a great deal of attention regarding their potential as optimization techniques for complex
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.
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.
Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms
Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms Sergio of jazz standards, and we collected commercial audio recordings extracted from jazz guitar CDs. Based on the MIDI record- ings as ground truth, two different instrument settings are compared (Jazz trio
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
Andrei Petrovski; Siddhartha Shakya; John Mccall
2006-01-01
This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Learn- ing (PBIL), which is an Estimation of Distribution Algo- rithm (EDA), and Genetic Algorithms (GAs) have been ap- plied to the problem of finding effective chemotherapeutic treatments. To our knowledge, EDAs have been applied to fewer real
Video scene retrieval with interactive genetic algorithm
Hun-woo Yoo; Sung-bae Cho
2007-01-01
This paper proposes a video scene retrieval algorithm based on emotion. First, abrupt\\/gradual shot boundaries are detected in the video clip of representing a specific story. Then, five video features such as \\
Grooming of arbitrary traffic using improved genetic algorithms
NASA Astrophysics Data System (ADS)
Jiao, Yueguang; Xu, Zhengchun; Zhang, Hanyi
2004-04-01
A genetic algorithm is proposed with permutation based chromosome presentation and roulette wheel selection to solve traffic grooming problems in WDM ring network. The parameters of the algorithm are evaluated by calculating of large amount of traffic patterns at different conditions. Four methods were developed to improve the algorithm, which can be used combining with each other. Effects of them on the algorithm are studied via computer simulations. The results show that they can all make the algorithm more powerful to reduce the number of add-drop multiplexers or wavelengths required in a network.
Coello, Carlos A. Coello
Sub-population policies for a parallel multiobjective genetic algorithm with applications to wing. 2. GENETIC ALGORITHM WITH SUB-POPULATIONS The algorithm here described, that will be called Virtual Sub- population Genetic Algorithm (VSGA), is an extension of the multi-objective genetic algorithm
Genetic algorithm-based form error evaluation
NASA Astrophysics Data System (ADS)
Cui, Changcai; Li, Bing; Huang, Fugui; Zhang, Rencheng
2007-07-01
Form error evaluation of geometrical products is a nonlinear optimization problem, for which a solution has been attempted by different methods with some complexity. A genetic algorithm (GA) was developed to deal with the problem, which was proved simple to understand and realize, and its key techniques have been investigated in detail. Firstly, the fitness function of GA was discussed emphatically as a bridge between GA and the concrete problems to be solved. Secondly, the real numbers-based representation of the desired solutions in the continual space optimization problem was discussed. Thirdly, many improved evolutionary strategies of GA were described on emphasis. These evolutionary strategies were the selection operation of 'odd number selection plus roulette wheel selection', the crossover operation of 'arithmetic crossover between near relatives and far relatives' and the mutation operation of 'adaptive Gaussian' mutation. After evolutions from generation to generation with the evolutionary strategies, the initial population produced stochastically around the least-squared solutions of the problem would be updated and improved iteratively till the best chromosome or individual of GA appeared. Finally, some examples were given to verify the evolutionary method. Experimental results show that the GA-based method can find desired solutions that are superior to the least-squared solutions except for a few examples in which the GA-based method can obtain similar results to those by the least-squared method. Compared with other optimization techniques, the GA-based method can obtain almost equal results but with less complicated models and computation time.
Multiobjective Genetic Algorithm applied to dengue control.
Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F
2014-12-01
Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. PMID:25230238
Lunar Habitat Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
Closed Loop System Identification with Genetic Algorithms
NASA Technical Reports Server (NTRS)
Whorton, Mark S.
2004-01-01
High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.
Genetic Algorithms: survival of the fittest
NASA Astrophysics Data System (ADS)
Regensburger, Joseph; Xi, Hao-Wen
1997-05-01
"Genetic algorithms(GAs)" inspired by the evolution in natural system in biology have been receiving a great deal of attention and emerged as a practical, optimization method. GAs can handle highly complex optimization problems with surprising speed and efficiency, and yet is easy to implement in a few simple steps. GAs are based on evolution. The potential optimization solutions are coded as fixed length strings(called chromosomes), usually in binary code. Each "individuals" in a pool of solutions is then assigned "a fitness" that measure how good it is. For example, if the problem is to find the maximum of a function, the fitness may be defined as the corresponding value of the height of the function. These "individuals" must then compete for the right to reproduce themselves. They reproduce according to their fitness. In a survival-of-the-fittest situation, exact or near-exact optimum solutions to the given problem are literally "bred" from an initial pool of solutions. The purpose of this research is to provide a introduction how GAs work, and to demonstrate their usefulness in function optimization and in finding the ground state energies of Ising spin model in statistical physics.
Improved classification accuracy by feature extraction using genetic algorithms
NASA Astrophysics Data System (ADS)
Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.
2003-05-01
A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.
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 Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2004-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Parallel processing of cooperative genetic algorithm for nurse scheduling
Makoto Ohki; Shin-ya Uneme; Hikaru Kawano
2008-01-01
This paper proposes an effective parallel algorithm for the cooperative genetic algorithm (CGA) to solve a nurse scheduling problem. The nurse scheduling is very complex task for a clinical director in a general hospital. Even veteran director needs one or two weeks to create the schedule. Besides, we extend the nurse schedule to permit the change of the schedule. This
Parallel micro genetic algorithm for constrained economic dispatch
Jarurote Tippayachai; Weerakorn Ongsakul; Issarachai Ngamroo
2002-01-01
This paper proposes a parallel micro genetic algorithm (PMGA) for solving ramp rate constrained economic dispatch (ED) problems for generating units with nonmonotonically and monotonically increasing incremental cost (IC) functions. The developed PMGA algorithm is implemented on the thirty-two-processor Beowulf cluster with ethernet switches network on the systems with the number of generating units ranging from 10 to 80 over
Optimized Monte Carlo Path Generation using Genetic Algorithms
F. Suykens; Y. D. Willems
In this technical report we present a new method for optimizing the generation of paths in Monte Carlo global illumination rendering algorithms. Ray tracing, particle tracing, and bidirectional ray tracing all use random walks to estimate various fluxes in the scene. The probability density functions neces- sary to generate these random walks are optimized using a genetic algorithm, such that
High-performance of geometric primitives detection usinig genetic algorithm
Yao Dong Wang; Noboru FUNAKUBO
1999-01-01
In this paper, we present some new methods for high performance of geometric primitives detection using a genetic algorithm (GA). At first, we describe the detection algorithm based on minimal subset and improvement of fitness function of geometric primitives. Secondly, we analyze the structure of minimal subsets and its probability properties in a digital image, and we improved the probability
A Genetic Algorithm for Flexible Job-Shop Scheduling
Haoxun Chen; Jiirgen Ihlow; Carsten Lehmann
1999-01-01
Genetic algorithms have been applied to the scheduling of job shops-a class of very complicated combinatorial optimization problems. Among these algorithms for job shops, a common assumption is that the routes that jobs visit machines are fixed, this is not true for flexible job shops such as flexible manufacturing systems, where jobs have machine route flexibility. The paper presents a
A Genetic Algorithm for the Generation of Jazz Melodies
George Papadopoulos; Geraint Wiggins
1998-01-01
This paper describes a system for the generation of jazz melodies over an input chord progression. A genetic algorithm was used to search through the space of possible solutions. A symbolic, as opposed to binary, approach with domain-specificreproduction operators was chosen because it allowed knowledge based constraints to be imposed on the search space. The objective, algorithmic fitnessfunction as well
Footballs Video Scene Retrieval with Interactive Genetic Algorithm
Qing-kai Bu; Ai-qun Hu
2008-01-01
This paper proposed an interactive genetic algorithm (IGA) for football video scenes retrieval with multimodal features. Four audio-visual features (average shot duration, average motion activity average sound energy, and average speech rate) were extracted from each of the videos. Then they were encoded as chromosomes and indexed into search table. First, the proposed algorithm randomly selected the videos from the
Genetic algorithms applications in the analysis of insolvency risk
Franco Varetto
1998-01-01
This study analyses the comparison between a traditional statistical methodology for bankruptcy classification and prediction, i.e. linear discriminant analysis (LDA), and an artificial intelligence algorithm known as Genetic Algorithm (GA). The study was carried out at Centrale dei Bilanci, in Turin, Italy, analysing 1920 unsound and 1920 sound industrial Italian companies from 1982–1995. This paper follows our earlier examination of
A genetic algorithm for packing in three dimensions
Arthur L. Corcoran III; Roger L. Wainwright
1992-01-01
Recent research in Bin Packing haa almost exclusively been in two dimensions. In this paper we extend the classic Bin Packing problem to three dimensions. We investigate the solutions for the three dmenaicmal packing problem using fust fit ond next fit packing strategies with and without genetic algorithms. Five data sets were used to test our algorithms, both random and
A nearest-neighboring-end algorithm for genetic mapping
Charles F. Crane; Yan M. Crane
2005-01-01
Motivation: High-throughput methods are beginning to make pos- sible the genotyping of thousands of loci in thousands of individuals, which could be useful for tightly associating phenotypes to candidate loci. Current mapping algorithms cannot handle so many data without building hierarchies of framework maps. Results: A version of Kruskal's minimum spanning tree algorithm can solve any genetic mapping problem that
Wireless Resource Management Using Genetic Algorithm for Mobiles Equilibrium
Mohamed Moustafa; Ibrahim W. Habib; Mahmoud Naghshineh
2001-01-01
Resource scheduling is essential in order to provide suitable signal quality and to achieve good channel efficiency in wireless mobile networks. A novel algorithm is proposed for controlling the transmitter power and transmission rate of mobile calls cooperatively. Previous work has focused on handling them separately. The proposed scheme is called the genetic algorithm for mobiles equilibrium. Based on an
Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem
Ferris, Michael C.
have looked at the application of genetic algorithms to optimization of nonlinear functions; our algorithm works. The method operates with a set of potential solutions. This is referred to as a population individuals. Based on this fitness function a number of individuals are selected as potential parents
A novel survival of the fittest genetic algorithm
Fengping Pan; Xiaoyan Sun; Shifan Xu; Xijin Guo; Dunwei Gong
2002-01-01
Considering the relationship between the variety of evolution population and evolution times, a novel closed crossing avoidance strategy is put forth in this paper. Based on it, a novel survival of the fittest genetic algorithm is present. The algorithm can avoid close breeding effectively and the thought of survival of the fittest is externalized. It has been proved that the
Neocognitron's parameter tuning by genetic algorithms.
Shi, D; Dong, C; Yeung, D S
1999-12-01
The further study on the sensitivity analysis of Neocognitron is discussed in this paper. Fukushima's Neocognitron is capable of recognizing distorted patterns as well as tolerating positional shift. Supervised learning of the Neocognitron is fulfilled by training patterns layer by layer. However, many parameters, such as selectivity and receptive fields are set manually. Furthermore, in Fukushima's original Neocognitron, all the training patterns are designed empirically. In this paper, we use Genetic Algorithms (GAs) to tune the parameters of Neocognitron and search its reasonable training pattern sets. Four contributions are claimed: first, by analyzing the learning mechanism of Fukushima's original Neocognitron, the correlations amongst the training patterns are claimed to affect the performance of Neocognitron, tuning the Neocognitron's number of planes is equivalent to searching reasonable training patterns for its supervised learning; second, a GA-based supervised learning of the Neocognitron is carried out in this way, searching the parameters and training patterns by GAs but specifying the connection weights by training the Neocognitron; third, other than traditional GAs which are unsuitable for the large searching space of training patterns set, the cooperative coevolution is incorporated to play this role; fourth, an effective fitness function is given out when applying the above methodology into numeral recognition. The evolutionary computation in our initial experiments is implemented based on the original training pattern set, e.g. the individuals of the population are generated from Fukushima's original training patterns during initialization of GAs. The results prove that our correlation analysis is reasonable, and show that the performance of a Neocognitron is sensitive to its training patterns, selectivity and receptive fields, especially, the performance is not monotonically increasing with respect to the number of training patterns, and this GA-based supervised learning is able to improve Neocognitron's performance. PMID:10651333
Earthquake location — genetic algorithms for teleseisms
NASA Astrophysics Data System (ADS)
Kennett, B. L. N.; Sambridge, M. S.
1992-12-01
The location of earthquakes requires the estimation of the spatial and temporal components of the hypocentre. This can be achieved by a direct minimisation of a measure of the misfit between observed and calculated travel times, and also slownesses and azimuths if array data are available. An efficient means of carrying out this optimisation procedure is to make use of genetic algorithms. This technique is based on the use of many estimates of the hypocentre location at once and the properties of the cluster of estimated locations in four dimensions are exploited in the course of the optimisation process. Each estimate of the hypocentral location is represented on a local discrete grid by a bit-string and successive iterations generate new bit-strings (and hence location estimates) by operations based on biological analogues. These operations are the replication of the best-fitting bit-strings, the cross-over of information between pairs of bit-strings and the mutation of individual bits in a string. The non-local character of the information on the misfit function carried in the cloud of hypocentral estimates is usually sufficient to prevent the location being trapped in local minima of the misfit surface. Convergence to the global misfit minimum can be achieved with a very limited sampling of the original spatial and temporal grid. No derivatives of the seismic phase information are required and so the technique is easily generalised to three-dimensional velocity models, and can be used with any suitable measure of the quality of an earthquake location by the choice of the misfit criterion between observed and calculated quantities.
An Overview of Genetic Algorithms : Part 1, Fundamentals
Martin, Ralph R.
stated by Charles Darwin in The Origin of Species. By mimicking this process, genetic algorithms are able to evolution. Exactly which biological processes are essential for evolution, and which processes have little
EVOLUTION OF EVOLUTION An Exploratory Work on Genetic Algorithms
it in advance, provided only that we have a way to recognize when the problem is solved." Marvin Minsky to avoid this." Marvin Minsky #12; 5 Origin of Species In this chapter, genetic algorithms
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.
Study on living object identification based on genetic algorithms
NASA Astrophysics Data System (ADS)
Wang, Yao; Xiong, Mu-di; Jia, Si-nan
2007-12-01
Fast and effectual salvage can reduce accident loss, ensure people's lives and belongings safely when shipwreck occurs. It is very important that discovering objects should be timely and exactly to insure the salvage going on wheels. This text puts forward an object identification arithmetic based on Genetic Algorithms, which makes use of Genetic Algorithms to search living objects in the sea based on different infrared radiation characteristics between living objects and background, uses single point crossover method and simple mutation method with adaptive probability, ensures the global and local searching ability of Genetic Algorithms. Thus GA can accomplish searching course of optimization quickly and exactly with favorable searching ability. From identification test aiming at standard infrared image, it is seen that the image is strengthened by Genetic Algorithms, and the living objects can be identified exactly.
A Random Key Based Genetic Algorithm for the Resource ...
x
2005-07-01
Jun 30, 2005 ... priorities of the activities are defined by the genetic algorithm. The heuristic generates .... that computes a Modified makespan value which is used as the fitness measure (quality measure) to ... biological organisms. Over many ...
Using Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White
White, Tony
-TSP algorithm as a Genetic Algorithm modification to ACS-TSP. The algorithm uses a GA to evolve a populationUsing Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White School of Computer,arpwhite}@scs.carleton.ca Abstract. We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031
Superscattering of light optimized by a genetic algorithm
Mirzaei, Ali, E-mail: ali.mirzaei@anu.edu.au; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S. [Nonlinear Physics Center, Research School of Physics and Engineering, Australian National University, Canberra ACT 0200 (Australia)
2014-07-07
We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.
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.
Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms
Gibbs, Trevor Howard
2011-08-08
HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE May 2010 Major Subject: Petroleum Engineering HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate...
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
An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Weir, John M.; Wells, B. Earl
2003-01-01
Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.
Bayesian network structure learning using chaos hybrid genetic algorithm
NASA Astrophysics Data System (ADS)
Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.
Genetic Algorithms to Learn Feature Weights for the Nearest Neighbor Algorithm
Gülsen Demiroz; H. Altay Güvenir
1996-01-01
In this paper we use genetic algorithms to learn feature weights for the NearestNeighbor classification algorithm. We represent feature weights as real values in [0..1]and their sum is 1. A new crossover operation, called continuous uniform crossover,is introduced where the legality of chromosomes is preserved after the crossoveroperation. This new crossover operation is compared with three other crossoveroperations---one-point crossover, two-point
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
NASA Astrophysics Data System (ADS)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
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.
Contents correlation and genetic algorithm based remote sensing images fusion
NASA Astrophysics Data System (ADS)
Na, Yan; Ehlers, Manfred; Ji, Hongbin; Shi, Lin
2007-10-01
A contents correlation and Genetic Algorithm based remote sensing images fusion method is presented. Based on the imaging properties of Panchromatic images and multi-spectral images, contents correlation analysis concept is introduced. The fusion procedure is that Contourlet transform decomposition of Panchromatic and multi-spectral images, Analysis of redundancy and supplement relations of images contents, the construction of fusion method to redundancy components and supplement components, fusion algorithms optimization by using Genetic Algorithm. Finally, a fused image can be obtained with inverse Contourlet transform. Preliminary experiment results show that this method is better than ordinary wavelet transform based fusion method, IHS transform based fusion method and PCA transform based fusion method.
A simple genetic algorithm for multiple sequence alignment.
Gondro, C; Kinghorn, B P
2007-01-01
Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of 'residues' (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task. PMID:18058716
Integrated genetic algorithm for optimization of space structures
NASA Astrophysics Data System (ADS)
Adeli, Hojjat; Cheng, Nai-Tsang
1993-10-01
Gradient-based mathematical-optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty-function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global-searches. The technique employs the Darwinian survival-of-the-fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to the optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.
Application of regression analysis based on genetic particle swarm algorithm in financial analysis
Xiaorong Cheng; Lin Sun; Ping Liu
2010-01-01
Slow convergence speed and premature are two key problems existing in the regression analysis techniques based on genetic algorithm. To overcome the shortcomings,this paper proposes an improved regression analysis based on the genetic particle swarm algorithm. The basic principle is that a new operator is constructed to use PSO. This algorithm has the choice of genetic algorithms and genetic features,
Improvement on the genetic algorithm and its application in employee performance evaluation
Minying Huang; Rui Mou
2010-01-01
Aiming at the deficiencies of standard genetic algorithm, an improved algorithm is presented. Through improvement and expansion of genetic operators of standard genetic algorithm, the study enhances the operating efficiency and accuracy of fuzzy clustering analytical method which based on the improved genetic algorithm, applies it in the human resource management system, and conducts scientific and reasonable evaluation of employee
A genetic algorithm for unmanned aerial vehicle routing
Matthew A. Russell; Gary B. Lamont
2005-01-01
Genetic Algorithms (GAs) can efficiently produce high quality results for hard combinatorial real world problems such as the Vehicle Routing Problem (VRP). Genetic Vehicle Representation (GVR), a recent approach to solving instances of the VRP with a GA, produces competitive or superior results to the standard benchmark problems. This work extends GVR research by presenting a more precise mathematical model
Genetic programming can be used to automatically discover algorithms for
Spector, Lee
of larger devices is in progress. Current experimental quantum computing hardware is based on the use of ionABSTRACT Genetic programming can be used to automatically discover algorithms for quantum computers of genetic programming to quantum computation and vice versa. 1. Quantum Computing Quantum computers
Abductive reasoning in Bayesian belief networks using a genetic algorithm
Edzard S. Gelsema
1995-01-01
A set of computational experiments is described in which genetic algorithms are used for abductive reasoning in Bayesian belief networks. It is shown that good solutions and explanations are consistently found with high probabilities. The efficiency of genetic sampling w.r.t. random sampling is shown to increase with increasing complexity of the search space and with increasing complexity of the search
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…
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 equilibrium with an aspect ratio of 3.5 that is stable to kink and ballooning modes at = 4% has been obtained
Local search genetic algorithm for optimal design of reliable networks
Berna Dengiz; Fulya Altiparmak; Alice E. Smith
1997-01-01
This paper presents a genetic algorithm (GA) with specialized encoding, initialization and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the fitness function involves calculating the all-terminal reliability of the network, a calculation that
Haplotyping a single triploid individual based on genetic algorithm.
Wu, Jingli; Chen, Xixi; Li, Xianchen
2014-01-01
The minimum error correction model is an important combinatorial model for haplotyping a single individual. In this article, triploid individual haplotype reconstruction problem is studied by using the model. A genetic algorithm based method GTIHR is presented for reconstructing the triploid individual haplotype. A novel coding method and an effectual hill-climbing operator are introduced for the GTIHR algorithm. This relatively short chromosome code can lead to a smaller solution space, which plays a positive role in speeding up the convergence process. The hill-climbing operator ensures algorithm GTIHR converge at a good solution quickly, and prevents premature convergence simultaneously. The experimental results prove that algorithm GTIHR can be implemented efficiently, and can get higher reconstruction rate than previous algorithms. PMID:25227091
Two-dimensional phase unwrapping using a hybrid genetic algorithm.
Karout, Salah A; Gdeisat, Munther A; Burton, David R; Lalor, Michael J
2007-02-10
A novel hybrid genetic algorithm (HGA) is proposed to solve the branch-cut phase unwrapping problem. It employs both local and global search methods. The local search is implemented by using the nearest-neighbor method, whereas the global search is performed by using the genetic algorithm. The branch-cut phase unwrapping problem [a nondeterministic polynomial (NP-hard) problem] is implemented in a similar way to the traveling-salesman problem, a very-well-known combinational optimization problem with profound research and applications. The performance of the proposed algorithm was tested on both simulated and real wrapped phase maps. The HGA is found to be robust and fast compared with three well-known branch-cut phase unwrapping algorithms. PMID:17279161
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.
Use of a genetic algorithm to analyze robust stability problems
Murdock, T.M.; Schmitendorf, W.E.; Forrest, S.
1990-01-01
This note resents a genetic algorithm technique for testing the stability of a characteristic polynomial whose coefficients are functions of unknown but bounded parameters. This technique is fast and can handle a large number of parametric uncertainties. We also use this method to determine robust stability margins for uncertain polynomials. Several benchmark examples are included to illustrate the two uses of the algorithm. 27 refs., 4 figs.
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.
An Improved Routing Protocol in WSN with Hybrid Genetic Algorithm
Lejiang Guo; Qiang Tang
2010-01-01
Wireless Sensor Networks (WSN) represent a new dimension in the field of networking. In this paper, an improved Genetic Algorithm is applied to the design of high performance multi-path routing protocol of WSN at the first time. The Algorithm consists of two stages: single-parent evolution and population evolution. The initial population is formed in the stage of single-parent evolution by
Modified ant-colony-optimization algorithm as an alternative to genetic algorithms
NASA Astrophysics Data System (ADS)
Gollub, C.; de Vivie-Riedle, R.
2009-02-01
An alternative approach for the optimization strategy in quantum control experiments is proposed. Genetic algorithms are used frequently to improve the laser fields driving quantum processes. We present a flexible scheme based on ant-colony-optimization, which introduces a correlation of the mask function pixels and allows a decrease in the shaped pulse complexity and duration without loss of efficiency.
IEEE COMMUNICATIONS LETTERS, VOL. 4, NO. 8, AUGUST 2000 267 Comparison of a Genetic Algorithm with
Thompson, Dale R.
--Asynchronous transfer mode, genetic algorithm, networks, simulated annealing, topology. I. INTRODUCTION1 THE topologicalIEEE COMMUNICATIONS LETTERS, VOL. 4, NO. 8, AUGUST 2000 267 Comparison of a Genetic Algorithm. Bilbro, Senior Member, IEEE Abstract--The genetic algorithm (GA) and simulated annealing algorithm (SA
INVARIANT SUBSETS OF THE SEARCH SPACE AND THE UNIVERSALITY OF A GENERALIZED GENETIC ALGORITHM
BORIS MITAVSKIY
In this paper we shall give a mathematical description of a gen- eral evolutionary heuristic search algorithm which allows to see a very special property which slightly generalized binary genetic algorithms have compar- ing to other evolutionary computation techniques. It turns out that such a generalized genetic algorithm, which we call a binary semi-genetic algorithm, is capable of encoding virtually
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…
Coello, Carlos A. Coello
MULTIOBJECTIVE OPTIMIZATION OF HEAT TRANSFER PLANT USING DECISION TABLE CONTROLER AND GENETIC 74 029 Abstract - A genetic algorithm based procedure for di- rect decision table adjusting by means of genetic algorithm. The proposed evolutional optimizing procedure of the multiobjective
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.
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.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Canto, J; Martinez-Gomez, E; 10.1051/0004-6361/200911740
2009-01-01
Context. Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims. We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (Asexual Genetic Algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two e...
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 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.
Genetic Algorithms and the Search for Viable String Vacua
Steven Abel; John Rizos
2014-06-16
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 10^{10} models, and yet a Genetic Algorithm can find them after constructing only 10^5 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.
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.
Low-energy routing based on ant colony algorithm genetic algorithm in wireless sensor networks
NASA Astrophysics Data System (ADS)
Zhang, Shi; Lu, Qiannan; Zhang, Zhe; Chen, Jian
2006-11-01
While designing the routing protocol in wireless sensor networks (WSN), one of the key problems is to keep the energy-load balance over the network for prolonging its lifetime. In this paper, we propose a low-energy clustering WSN routing protocol based on Ant Colony Algorithm Genetic Algorithm (ACAGA). The protocol divides the network into several clusters and selects cluster heads according to the relative locations and the residual energy status of nodes. While keeping the energy-load balance over the whole network, it accomplishes a low-energy routing and prolongs the whole network's lifetime by using the distributed computation and global route optimization capabilities of ACAGA.
Application of Genetic Algorithm to Hexagon-Based Motion Estimation
Cheng, Wan-Shu
2014-01-01
With the improvement of science and technology, the development of the network, and the exploitation of the HDTV, the demands of audio and video become more and more important. Depending on the video coding technology would be the solution for achieving these requirements. Motion estimation, which removes the redundancy in video frames, plays an important role in the video coding. Therefore, many experts devote themselves to the issues. The existing fast algorithms rely on the assumption that the matching error decreases monotonically as the searched point moves closer to the global optimum. However, genetic algorithm is not fundamentally limited to this restriction. The character would help the proposed scheme to search the mean square error closer to the algorithm of full search than those fast algorithms. The aim of this paper is to propose a new technique which focuses on combing the hexagon-based search algorithm, which is faster than diamond search, and genetic algorithm. Experiments are performed to demonstrate the encoding speed and accuracy of hexagon-based search pattern method and proposed method. PMID:24592178
The Influence of Population Genetics for the Redesign of Genetic Algorithms
Michael Affenzeller; Stefan Wagner
This contribution considers recent results of population genetics in order to present generic extensions to the general concept of a Genetic Algorithm (GA). Consequently a new model for self-adaptive selection pressure steering...
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.
Ebtehaj, Isa; Bonakdari, Hossein
2014-01-01
The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations. PMID:25429460
Virus evolutionary genetic algorithm for task collaboration of logistics distribution
NASA Astrophysics Data System (ADS)
Ning, Fanghua; Chen, Zichen; Xiong, Li
2005-12-01
In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.
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.
Genetic algorithms for multicriteria shape optimization of induction furnace
NASA Astrophysics Data System (ADS)
K?s, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo
2012-09-01
In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization 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.
HARDWARE IMPLEMENTATION OF A PARALLELIZED GENETIC ALGORITHM FOR
Ricks, Kenneth G.
HARDWARE IMPLEMENTATION OF A PARALLELIZED GENETIC ALGORITHM FOR TASK SCHEDULING by VIJAY TIRUMALAI in Electrical Engineering in the Department of Electrical and Computer Engineering in the Graduate School of the requirements for the degree of Master of Science in Electrical Engineering. Accepted on behalf of the Faculty
Circuit synthesis evolution using a hardware-based genetic algorithm
Rami Abielmona; Voicu Groza
2001-01-01
We propose a scheme based on a hardware implementation of a genetic algorithm, to evolve the minimized logic solution of a defined input function. The minimization will be one of resource usage, more precisely of look-up tables (LUTs). The design aids in the difficult issue of technology mapping, as well as multi-level logic synthesis. The approach undertaken in this research
Reduced scale PWR passive safety system designing by genetic algorithms
João J. da Cunha; Antonio Carlos M. Alvim; Celso Marcelo Franklin Lapa
2007-01-01
This paper presents the concept of “Design by Genetic Algorithms (DbyGA)”, applied to a new reduced scale system problem. The design problem of a passive thermal-hydraulic safety system, considering dimensional and operational constraints, has been solved. Taking into account the passive safety characteristics of the last nuclear reactor generation, a PWR core under natural circulation is used in order to
Genetic Algorithm for Grid Scheduling using Best Rank Power
Wael Abdulal; Omar Al Jadaan; Ahmad Jabas; S. Ramachandram
2009-01-01
The large computing capacity provided by grid systems is beneficial for solving complex problems by using many nodes of the grid at the same time. The usefulness of a grid system largely depends, among other factors, on the efficiency of the system regarding the allocation of jobs to grid resources. This paper proposes an Roulette Wheel Selection Genetic Algorithm using
Topology Design of Feedforward Neural Networks by Genetic Algorithms
Slawomir W. Stepniewski; Andy J. Keane
1996-01-01
For many applications feedforward neural networks have proved to be a valuable tool. A lthough the basic principles of employing such networks are quite straightforward, the problem o f tuning their architectures to achieve near optimal performance still remains a very challenging task. Genetic algorithms may be used to solve this problem, since they have a number of distinct f
Optimization of activated sludge designs using genetic algorithms
T. A. Doby; D. H. Loughlin; J. J. Ducoste
2002-01-01
We describe a framework in which a genetic algorithm (GA) and a static activated sludge (AS) treatment plant design model (WRC AS model) are used to identify low cost activated sludge designs that meet specified effluent limits (e.g. for BOD, N, and P). Once the user has chosen a particular process (Bardenpho, Biodenipho, UCT or SBR), this approach allows the
Vibrational genetic algorithm as a new concept in airfoil design
Abdurrahman Hacio?lu; ?brahim Özkol
2002-01-01
We introduce the Vibration concept for real coded Genetic Algorithm and its implementation to inverse airfoil design, which decreases the number of CFD calculations. This concept assures efficient diversity in the population and consequently gives faster solution. We used the Vibration concept as vibrational mutation and vibrational crossover. For the mutational manner, a sinusoidal wave with random amplitude is introduced
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
Optimal design of the magnetic microactuator using the genetic algorithm
C. H. Ko; J. C. Chiou
2003-01-01
This paper presents the optimal design of the magnetic microactuator using the genetic algorithm. The magnetic microactuator is composed of an enclosed core and a permalloy plate to form a closed magnetic circuit. The present design allows the area of the magnetic poles to be optimally enlarged and achieve a maximum force generation. To obtain the optimal geometry and maximum
Random-key genetic algorithms Jose Fernando Goncalves
Resende, Mauricio G. C.
Random-key genetic algorithms JosÂ´e Fernando GonÂ¸calves LIAAD, INESC TEC, Faculdade de Economia do interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization prob- lem at random in the continuous interval [0, 1). A decoder is a procedure that maps a vector of random keys
Simulating Gender Separation and Mating Constraints for Genetic Algorithms
Vrajitoru, Dana
of the sexual reproduction and of the mating schemes has been an interest of research in GAs and evolutionary and evolved into more intelligent ones. In this context, sexual reproduction is the most important mechanism various reproduction modes and types restrictions from nature with the genetic algorithms. We con- sider
Improvement of Selection and Crossover Strategy in Genetic Algorithm
FENG Dong-qing; WANG Fei; MA Yan
2008-01-01
(Abstract)This paper proposes an improved Genetic Algorithm(GA). The ranking selection intensity adopts an adaptive adjusting mechanism, which can adjust the selection intensity dynamically according to the change of the population state. A new crossover strategy which chooses the outstanding individuals according to competition is used to increase the individual average performance of the population. The simulation with the typical test
Experiences with the PGAPack Parallel Genetic Algorithm library
Levine, D.; Hallstrom, P.; Noelle, D.; Walenz, B.
1997-07-01
PGAPack is the first widely distributed parallel genetic algorithm library. Since its release, several thousand copies have been distributed worldwide to interested users. In this paper we discuss the key components of the PGAPack design philosophy and present a number of application examples that use PGAPack.
Technical Report No. 494 Using Cyclic Genetic Algorithms
Portland State University
for a small hexapod robot are generated by a cyclic genetic algorithm. From these automata a Xilinx net list the communication network of an experimental robot colony. This recon guration of the hexapod's nervous system the control modules| realized as Xilinx xc030pc44 devices|for multiple robot agents are synthe- sized and down
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.
A Hybrid Engine Control System based on Genetic Algorithms
D. PORTO; A. MARTINEZ; S. SCIMONE
In this paper an optimal management of the energetic flows in a hybrid vehicle based on a Genetic Algorithm is introduced. The aim is maximize the use of the electric engine, minimizing the use of the internal combustion one, increasing the driving pleasure and reducing consumptions, emissions and noise. From the available literature, a typical configuration series-parallel hybrid engine as
A Genetic Algorithm to Improve an Othello Program
Jean-marc Alliot; Nicolas Durand
1995-01-01
this article, we show how genetic algorithms can be used to evolve the parameters of theevaluation function of an Othello program. We must stress that our method can be used on anyalgorithm using an evaluation function, for any two players game. In the first part of the article,we explain the structure of the Othello program. In the second part, we
SEXUAL SELECTION WITH COMPETITIVE/COOPERATIVE OPERATORS FOR GENETIC ALGORITHMS
Bullinaria, John
sex. Similarly, approaches including competitive/coÂ operative operators and natural selection derivedâ??anchezÂVelazco and John A. Bullinaria School of Computer Science The University of Birmingham Birmingham B15 2TT, UK fjgs,jxbg@cs.bham.ac.uk ABSTRACT In a standard genetic algorithm (GA), individuals reproÂ duce asexually: any two organisms may
Event-Based Soccer Video Retrieval with Interactive Genetic Algorithm
Guangsheng Zhao
2008-01-01
This paper proposes an interactive genetic algorithm (IGA) for soccer video events retrieval with multimodal features. Eight audio-visual features (average shot duration, standard deviation of shot duration, average motion activity, standard deviation of motion activity, average sound energy, standard deviation of sound energy, average speech rate and standard deviation of speech rate) were extracted from each video in database. Then
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
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
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
Comparison of probabilistic and deterministic optimizations using genetic algorithms
E. Ponslet; G. Maglaras; R. T. Haftka; E. Nikolaidis; H. H. Cudney
1995-01-01
This paper describes an application of genetic algorithms to deterministic and probabilistic (reliability-based) optimization of damping augmentation for a truss structure. The probabilistic formulation minimizes the probability of exceeding upper limits on the magnitude of the dynamic response of the structure due to uncertainties in the properties of the damping devices. The corresponding deterministic formulation maximizes a safety margin with
A Genetic Algorithm for Railway Scheduling , F. Barber2
Barber, Federico
10 A Genetic Algorithm for Railway Scheduling Problems P. Tormos1 , A. Lova1 , F. Barber2 , L in a reasonable computational time and raises the need for good heuristic scheduling techniques. The railway scheduling problem considered in this work implies the optimization of trains on a railway line
A genetic algorithm approach to periodic railway synchronization
Karl Nachtigall; Stefan Voget
1996-01-01
We consider the compilation of timetables for periodic served railway networks. The calculation of timetables with minimal waiting time for passengers changing trains is modeled by a periodic network optimization problem. We present a genetic algorithm which is combined with a greedy heuristic and a local improvement procedure.
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
Propeller performance analysis and multidisciplinary optimization using a genetic algorithm
Christoph Burger
2007-01-01
A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and\\/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility
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 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
NITRIDING PARAMETERS ANALIZED BY NEURAL NETWORK AND GENETIC ALGORITHM
Tomislav Filetin; Davor Novak; I. Lu
The surface hardness and hardness profile of a nitrided workpiece depe nd on the chemical composition of the steel, nitriding temperature and time, and on type of the nitriding process (i.e. atmo sphere). An issue in this approach was to test how the statistical analysis, arti ficial neural network, ge netic algorithm and genetic programming may be used for determination
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
Using Genetic Algorithms to Converge on Molecules with Specific Properties
Stephen Foster; Nathan Lindzey; Jon Rogers; Carl West; Walt Potter; Sean Smith; Steven Alexander
2007-01-01
Although it can be a straightforward matter to determine the properties of a molecule from its structure, the inverse problem is much more difficult. We have chosen to generate molecules by using a genetic algorithm, a computer simulation that models biological evolution and natural selection. By creating a population of randomly generated molecules, we can apply a process of selection,
An Adaptive Penalty Approach for Constrained GeneticAlgorithm Optimization
Rasheed, Khaled
). These include: 1. Rejection of infeasible solutions (the death penalty). 2. Using a mapping function so that allAn Adaptive Penalty Approach for Constrained GeneticÂAlgorithm Optimization Khaled Rasheed shehata@cs.rutgers.edu ABSTRACT In this paper we describe a new adaptive penalty approach for handling
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.
ORIGINAL PAPER A self-organizing random immigrants genetic algorithm
Yang, Shengxiang
the problem changes due to the dynamics. Keywords Genetic algorithms Á Self-organized criticality Á Dynamic with environmental changes, caused, for example, by natural R. Tino´s Departamento de Fi´sica e Matema´tica, FFCLRP, Universidade de Sa~o Paulo (USP), Ribeira~o Preto, 14040-901, Brazil e-mail: rtinos@ffclrp.usp.br S. Yang
Optimum design of composite laminates using genetic algorithms
Kelvin J. Callahan
1992-01-01
The use of genetic algorithms (GAs) for the design of composite laminates is presented. Unlike the traditional hill-climbing techniques, GAs are global search procedures based on the mechanics of natural selection with the result that they are robust over a wide range of environments, particularly the multimodal search spaces encountered in composite design. The GA requires coding of the design
Synthesis of linear array using Schelkunoff's method and genetic algorithms
D. Marcano; M. Jiminez; O. Chang
1996-01-01
The synthesis of radiation patterns of linear arrays using the Schelkunoff method and genetic algorithms (GAs) is presented. GAs permit the location of the roots on the complex w plane until the desired pattern is obtained. This technique is a powerful tool in the synthesis of complex radiation patterns
Genetic algorithms for nondestructive testing in crack identification
A. A. Arkadan; T. Sareen; S. Subramaniam
1994-01-01
A method to identify the nature of a crack on the surface of a region using nondestructive testing (NDT) and inverse problem methodology is presented. A genetic algorithm (GA) based approach, which involves a global search to avoid local minima, is presented and applied to solve the inverse problem of identifying the position, shape and the orientation of a surface
Genetic algorithm for extracting rules in discrete domain
Neruda, R.
1995-09-20
We propose a genetic algorithm that evolves families of rules from a set of examples. Inputs and outputs of the problem are discrete and nominal values which makes it difficult to use alternative learning methods that implicitly regard a metric space. A way how to encode sets of rules is presented together with special variants of genetic operators suitable for this encoding. The solution found by means of this process can be used as a core of a rule-based expert system.
Ancestral DNA Sequence Reconstruction Using Recursive Genetic Algorithms
Mauricio Martínez; Edgar E. Vallejo; Enrique Morett
2007-01-01
This paper explores the capabilities of genetic algorithms for reconstructing ancestral DNA sequences. We conducted a series\\u000a of experiments on reconstructing ancestral states from a given collection of taxa and their phylogenetic relationships. We\\u000a tested the proposed model using simulated phylogenies obtained from actual DNA sequences by applying realistic mutation rates.\\u000a Experimental results demonstrated that the recursive application of genetic
Genetic Algorithms as a Reactive Power Source Dispatching Aid for Voltage Security Enhancement
CHIH-WEN LIU; CHEN-SUNG CHANG; JOE-AIR JIANG
2001-01-01
This paper presents an efficient computing algorithm for enhancing voltage security. The algorithm uses the genetic algorithm (GA) to dispatch reactive power sources under various system conditions. GA aids the dispatching of reactive power sources so as to maintain the specified security level. The reactive power sources used in the proposed genetic algorithm are transformer tap changers, static capacitors, static
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
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.
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.
Sampling protein conformations using segment libraries and a genetic algorithm
NASA Astrophysics Data System (ADS)
Gunn, John R.
1997-03-01
We present a new simulation algorithm for minimizing empirical contact potentials for a simplified model of protein structure. The model consists of backbone atoms only (including C?) with the ? and ? dihedral angles as the only degrees of freedom. In addition, ? and ? are restricted to a finite set of 532 discrete pairs of values, and the secondary structural elements are held fixed in ideal geometries. The potential function consists of a look-up table based on discretized inter-residue atomic distances. The minimization consists of two principal elements: the use of preselected lists of trial moves and the use of a genetic algorithm. The trial moves consist of substitutions of one or two complete loop regions, and the lists are in turn built up using preselected lists of randomly-generated three-residue segments. The genetic algorithm consists of mutation steps (namely, the loop replacements), as well as a hybridization step in which new structures are created by combining parts of two "parents'' and a selection step in which hybrid structures are introduced into the population. These methods are combined into a Monte Carlo simulated annealing algorithm which has the overall structure of a random walk on a restricted set of preselected conformations. The algorithm is tested using two types of simple model potential. The first uses global information derived from the radius of gyration and the rms deviation to drive the folding, whereas the second is based exclusively on distance-geometry constraints. The hierarchical algorithm significantly outperforms conventional Monte Carlo simulation for a set of test proteins in both cases, with the greatest advantage being for the largest molecule having 193 residues. When tested on a realistic potential function, the method consistently generates structures ranked lower than the crystal structure. The results also show that the improved efficiency of the hierarchical algorithm exceeds that which would be anticipated from tests on either of the two main elements used independently.
Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2012-01-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2011-12-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
Andrej Lipej; Carlo Poloni
2000-01-01
Numerical flow analysis in turbomachinery is an indispensable tool for water turbines design. Following the design of axial runner, the energetic and cavitation characteristics can be predicted using the numerical method. In this paper it is described how the multiobjective genetic algorithm aids the human decision of the best design solution, based on objective functions obtained by numerical flow analysis.
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
A novel pipeline based FPGA implementation of a genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2014-05-01
To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.
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.
Protein Structure Alignment Using a Genetic Algorithm Joseph D. Szustakowski and Zhiping Weng*
Weng, Zhiping
Protein Structure Alignment Using a Genetic Algorithm Joseph D. Szustakowski and Zhiping Weng* Boston University, Department of Biomedical Engineering, Boston, Massachusetts ABSTRACT We have developed alignment is determined by a genetic algorithm. After refinement of the second- ary structure element
Genetic algorithms and their applications to the design of neural networks. Antonia J. Jones
Jones, Antonia J.
Genetic algorithms and their applications to the design of neural networks. Antonia J. Jones 2002 1 Genetic algorithms and their applications to the design of neural networks. CONTENTS Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Figure 4 Stochastic equilibrium with stable niches using Goldberg sharing
Genetic algorithms and their use in geophysical problems
NASA Astrophysics Data System (ADS)
Parker, Paul Bradley
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. Also, 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.
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.
GENETIC ALGORITHM TOOLS FOR CONTROL SYSTEMS ENGINEERING
A. J. Chipperfield; P. J. Fleming; C. M. Fonseca
1994-01-01
GAs have been shown to be an effective strategy in the off- line design of control systems by a number of practitioners. For example, Krishnakumar and Goldberg (1) and Bramlette and Cusin (2) have demonstrated how genetic optimization methods can be used to derive superior controller structures in aerospace applications in less time (in terms of function evaluations) than that
Multiple-Objectives Genetic Algorithm Brahim Rekiek
Coello, Carlos A. Coello
into a scalar function. Population Initialization Decision-Aid Method Promethee Genetic Operators Population Promethee II [2]. It computes a `net flow' () associated with each solution. This flow gives us a ranking, called the Promethee II complete ranking, between the different solutions in the population. The weights
Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)
2002-01-01
We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.
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.
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.
Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model
Liang, Shunlin
Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model) and the inversion of a canopy radiative transfer (RT) model. In recent years, applications of the genetic algorithms area index; Genetic algorithms; Radiative transfer; Inversion; Landsat-7; ETM+ 1. Introduction Land
A genetic algorithm for the Flexible Job-shop Scheduling Problem
F. Pezzella; G. Morganti; G. Ciaschetti
2008-01-01
In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are
A Genetic Algorithm-Based Approach to Flexible Job-Shop Scheduling Problem
Hongze Qiu; Wanli Zhou; Hailong Wang
2009-01-01
Flexible Job-shop Scheduling Problem (FJSP) is one of extremely hard problems because it requires very large combinatorial search space. Genetic algorithm is wildly used to solve Flexible Job-shop Scheduling Problem. This paper presents an improved genetic algorithm. The improved genetic algorithm we proposed uses many different strategies to get a better result. During the phase of create initial population, the
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Francisco Herrera; Manuel Lozano; José L. Verdegay
1998-01-01
Genetic algorithms play a significant role, as search techniques for handling com- plex 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 natural evolution principles of populations. These algorithms process a population of chromo- somes, which represent search space solutions, with three operations:
An application of genetic algorithm to DNA sequencing by oligonucleotide hybridization
H. Douzono; S. Hara; Y. Noguchi
1998-01-01
The authors propose a sequencing algorithm for oligonucleotide hybridization using the genetic algorithm. The target DNA sequence reconstructed by the hybridization method is relatively long for genetic algorithm (GA), so special setups of the genetic operation are necessary. The authors introduce the grouping GA and a special crossover method for this problem. They carried out some experiments of sequence reconstruction,
Inferencing Over Incomplete Solution Spaces with Genetic Algorithms for Probabilistic Reasoning 1
Indiana University
Inferencing Over Incomplete Solution Spaces with Genetic Algorithms for Probabilistic Reasoning 1 Engineering Air Force Institute of Technology WrightPatterson AFB, OH 454337765 fbborghet Bayesian Knowledge Bases(BKB) using genetic algorithms(GA) . The fitness function for a genetic algorithm
Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization
NASA Technical Reports Server (NTRS)
Shaykhian, Gholam Ali; Sen, S. K.
2007-01-01
Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *
The Genetic Algorithm: Searching for Planets around Pulsars
NASA Astrophysics Data System (ADS)
Lazio, T. Joseph W.; Cordes, James M.; Novak, Jurica
1993-12-01
We discuss the general problem of finding multiple planets around pulsars through analysis of pulsar timing data. Fitting a full Keplerian orbit requires a search through four non-linear parameters for each planet. This problem is especially difficult when there is a large range of planetary masses and orbital periods. As one means for attacking the search problem, we have considered genetic algorithms, which are a general method for optimization that make use of biological-like genetic concepts like ``survival of the fittest,'' mutation, and chromosome exchange. Through these means, the algorithm searches parameter space in the same way that life finds optimal niches in the biological environment: through incremental rewarding of successful genetic variations. We show examples of the genetic algorithm as applied to simulated pulsar data and we compare its performance with alternative methods such as grid searches, nonlinear least squares fitting, the simplex method, hill-climbing, and simulated annealing. We also show preliminary application to real pulsar data.
Locomotive assignment problem with train precedence using genetic algorithm
NASA Astrophysics Data System (ADS)
Noori, Siamak; Ghannadpour, Seyed Farid
2012-07-01
This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations. These trains have different degrees of priority for servicing, and the high class of trains should be serviced earlier than others. This problem is modeled using vehicle routing and scheduling problem where trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows. A two-phase approach is used which, in the first phase, the multi-depot locomotive assignment is converted to a set of single depot problems, and after that, each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm, various heuristics and efficient operators are used in the evolutionary search. The suggested algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover, some of the results are compared with those solutions produced by branch-and-bound technique to determine validity and quality of the model. Results show that suggested approach is rather effective in respect of quality and time.
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.
Multiple Criteria Genetic Algorithms In Engineering Design And Operation
David Todd; Supervisor Prof; Pratyush Sen
1997-01-01
This thesis investigates the application of Genetic Algorithms (GAs) to multiple criteria problems in engineering design and operation. The GA is an evolutionary computing technique which applies Darwinian principles such as survival of the fittest, mating and mutation to a population of individuals to evolve good solutions to a broad range of problems. GAs are normally used as single criterion optimisers. However, a Multiple Criteria Genetic Algorithm (MCGA) has been developed in this thesis which allows simultaneous maximisation and minimisation across several criteria. The MCGA is explained, enhanced and modified throughout the thesis as new requirements are introduced. At each stage the enhancements are tested on basic test functions to assess their performance. New concepts such as a Pareto Population, Adaptive Niche Sizing and Neural Network Preferencing are introduced. A broad range of applications have been tackled using the MCGA, all of which are combinatorial in nature. Com...
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
Strain gage selection in loads using a genetic algorithm
Nelson, S. II
1994-12-31
Traditionally, structural loads are measured using strain gages. Many research structures such as airplane wings are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. A technique using a genetic algorithm to choose the strain gages for in-flight loads is presented. This technique is compared with the current T-value technique and a variant known as the Best Step-down technique. Additionally, 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 current methods could not.
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.
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.
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.
Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control
NASA Technical Reports Server (NTRS)
Rogers, James L.
2004-01-01
The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.
A parallel genetic algorithm for generation expansion planning
Fukuyama, Yoshikazu [Fuji Electric Corporate R and D, Ltd., Tokyo (Japan)] [Fuji Electric Corporate R and D, Ltd., Tokyo (Japan); Chiang, H.D. [Cornell Univ., Ithaca, NY (United States). School of Electrical Engineering] [Cornell Univ., Ithaca, NY (United States). School of Electrical Engineering
1996-05-01
This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm, The results reveal the speed and effectiveness of the proposed method for solving this problem.
A genetic algorithm solution to the unit commitment problem
Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V. [Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering] [Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering
1996-02-01
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 in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 units and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.
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.
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.
Michael Mattes; Juan R. Mosig
2004-01-01
A new adaptive sampling is proposed to accelerate frequency-domain calculations. The algorithm is based on the survival-of-the-fittest principle of genetic algorithms and uses rational functions to approximate the frequency response. The sampling algorithm is derivative free and well-adapted to devices with rapidly varying frequency responses like microwave filters. The criteria for convergence checking and to determine the location of additional
Genetic algorithms for fast search in fractal image coding
NASA Astrophysics Data System (ADS)
Redmill, David W.; Bull, David R.; Martin, Ralph R.
1996-02-01
This paper demonstrates the application of genetic algorithms (GAs) to the real-time search problem of fractal image compression. An approach using GAs has been simulated and compared with both an exhaustive search method and a heuristic multi-grid method. Results, for various block sizes, show that the GA based approach offers a computationally more efficient search than either of the other methods.
Dynamic Optimal Design of Groundwater Remediation Using Genetic Algorithms
Amy Chan Hilton; Aysegul Aksoy; Teresa B. Culver
The use of genetic algorithms for the dynamic optimal design of pump-and-treat groundwater remediation systems is demonstrated\\u000a through two new dynamic formulations. In the first formulation in which the contaminant sorption was assumed to be in equilibrium,\\u000a the lengths of management periods were decision variables. The second formulation assumed a pulsed pumping approach to remove\\u000a a contaminant with mass-transfer-limited sorption.
Water Distribution System Optimization Using Genetic Simulated Annealing Algorithm
Shihu. Shu
2011-01-01
\\u000a Water supply system optimization makes use of the latest advances in hybrid genetic algorithm to automatically determine the\\u000a least-cost pump operation for each pump station in large-scale water distribution system while satisfying simplified hydraulic\\u000a performance requirements. Calibration results of the original model were pretty good. The comparison results show that the\\u000a difference between the simplified and the original mode simulation
GenJam: A Genetic Algorithm for Generating Jazz Solos
John A. Biles
1994-01-01
This paper describes GenJam, a genetic algorithm-based model of a novice jazz musician learning to improvise. GenJam maintains hierarchically related populations of melodic ideas that are mapped to specific notes through scales suggested by the chord progression being played. As GenJam plays its solos over the accompaniment of a standard rhythm section, a human mentor gives real-time feedback, which is
River water quality management model using genetic algorithm
Egemen Aras; Vedat To?an; Mehmet Berkun
2007-01-01
Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and\\u000a integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this\\u000a study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model\\u000a was applied to a river system contaminated by three
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Colin R. Reeves; Christine C. Wright
1995-01-01
In an earlier paper we examined the relationshipbetween genetic algorithms (GAs)and traditional methods of experimental design.This was motivated by an investigationinto the problems caused by epistasis inthe implementation and application of GAsto optimization problems. We showed howthis viewpoint enables us to gain further insightsinto the determination of epistatic effects,and into the value of different forms ofencoding a problem for a
Genetic Algorithms applications to optimization and system identification
Lin, Yun-Jeng
1998-01-01
. Optimal Results of the Second Model; Abs. Viscosity vs. Rot. Speed. . . . . . . . 20 5. The Frequency Spectrum of the Real System. . . . 25 6 The Frequency Spectrum of the Estimated System by GA. . . . . . . 25 vlu LIST OF TABLES TABLE Page 1... the strings of first generation. Start Produce 10 strings Fitness- evaluation Ranking Reproduction Selecting Reproducing Crossover Mutation Fig. 1 Flow Chart of Genetic Algorithms Program The second step is for the 10 binary strings to go through...
A quantum genetic algorithm with quantum crossover and mutation operations
NASA Astrophysics Data System (ADS)
SaiToh, Akira; Rahimi, Robabeh; Nakahara, Mikio
2013-11-01
In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm that has a quantum crossover procedure performing crossovers among all chromosomes in parallel for each generation. A complexity analysis shows that a quadratic speedup is achieved over its classical counterpart in the dominant factor of the run time to handle each generation.
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
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.
Congressional Redistricting Using A TSP-based Genetic Algorithm
Sean L. Forman; Yading Yue
2002-01-01
The drawing of congressional districts by legislative bodies in the United States creates a great deal of controversy each\\u000a decade as political parties and special interest groups attempt to divide states into districts beneficial to their candidates.\\u000a The genetic algorithm presented in this paper attempts to find a set of compact and contiguous congressional districts of\\u000a approximately equal population. This
MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.
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
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.
A meta-learning system based on genetic algorithms
NASA Astrophysics Data System (ADS)
Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain
2004-04-01
The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.
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 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.
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.
NASA Astrophysics Data System (ADS)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models
NASA Astrophysics Data System (ADS)
Wang, Q. J.
1991-09-01
The genetic algorithm is a search procedure based on the mechanics of natural selection and natural genetics, which combines an artificial survival of the fittest with genetic operators abstracted from nature. In this paper, a genetic algorithm for function optimization is introduced and applied to calibration of a conceptual rainfall-runoff model for data from a particular catchment. All seven parameters of the model are optimized. The results show that the genetic algorithm can be efficient and robust.
Eliciting spatial statistics from geological experts using genetic algorithms
NASA Astrophysics Data System (ADS)
Walker, Matthew; Curtis, Andrew
2014-07-01
A new method to obtain the statistics of a geostatistical model is introduced. The method elicits the statistical information from a geological expert directly, by iteratively updating a population of vectors of statistics, based on the expert's subjective opinion of the corresponding geological simulations. Thus, it does not require the expert to have knowledge of the mathematical and statistical details of the model. The process uses a genetic algorithm to generate new vectors. We demonstrate the methodology for a particular geostatistical model used to model rock pore-space, which simulates the spatial distribution of matrix and pores over a 2-D grid, using multipoint statistics specified by conditional probabilities. Experts were asked to use the algorithm to estimate the statistics of a given target pore-space image with known statistics; thus, their numerical rates of convergence could be calculated. Convergence was measured for all experts, showing that the algorithm can be used to find appropriate probabilities given the expert's subjective input. However, considerable and apparently irreducible residual misfit was found between the true statistics and the estimates of statistics obtained by the experts, with the root-mean-square error on the conditional probabilities typically >0.1. This is interpreted as the limit of the experts' abilities to distinguish between realizations of different spatial statistics using the algorithm. More accurate discrimination is therefore likely to require complementary elicitation techniques or sources of information independent of expert opinion.
A NEW MULTI-FREQUENCY VIBRATIONAL GENETIC ALGORITHM IN RADAR CROSS SECTION MINIMIZATION PROBLEMS
Y. Volkan PEHLIVANOGLU
Within this study, it is aimed to provide an efficient stochastic algorithm for different optimization problems. For this purpose, as a search method, multi frequency vibrational genetic algorithm (m-VGA) is improved and used to accelerate the genetic algorithm for radar cross section minimization problem. From the results obtained, it is concluded that m-VGA decreased the required time for the minimized
Lewis, Kemper E.
GENETIC ALGORITHMS Kurt A. Hacker Postdoctoral Research Associate AIAA Student Member Dept. of Mechanical algorithms such as Simulated Annealing or Genetic Algorithms often can locate near optimal solutions but can (CFD), heat transfer and vehicle dynamics simulations. The execution time for these types of analyses
OPTIMISATION OF TIME DOMAIN CONTROLLERS FOR SUPPLY SHIPS USING GENETIC ALGORITHMS
Fernandez, Thomas
focus is not on parameters but on transfer functions. Structured Genetic Algorithm incorporatesOPTIMISATION OF TIME DOMAIN CONTROLLERS FOR SUPPLY SHIPS USING GENETIC ALGORITHMS AND GENETIC Cid, 2003 #12;A Jose y mi familia #12;i ABSTRACT The use of genetic methods for the optimisation
Application of genetic algorithm in atmospheric carbon dioxide concentration retrieval
NASA Astrophysics Data System (ADS)
Li, Jingyao; Shi, Runhe; Gao, Wei
2013-09-01
This paper introduces the basic theory and method of carbon dioxide (CO2) retrieval. The key step is to search for the optimal solution and the random search algorithm Genetic Algorithm (GA) which can effectively avoid the local optimization. We first investigate the basic principles of GA in CO2 retrieval and then design the corresponding encoding and decoding methods as well as the fitness function. This newly-developed GA is further applied to retrieve the atmospheric CO2 concentration using Atmospheric Infrared Sounder (AIRS) observations from January 2006 to December 2008 centered at 20°N, 144°E. Compared to the aircraft measurements, the GA retrieval yields the small root mean square error of 1.13 ppmv and reproduces good results with the observed seasonal cycle.
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.
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.
Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial
Yang, Zhen; Wang, Wei; Chen, Shuyan; Ding, Haoyang; Li, Xiaowei
2015-01-01
Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority. PMID:25663837
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 Parallel Genetic Algorithm for Automated Electronic Circuit Design
NASA Technical Reports Server (NTRS)
Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris
2000-01-01
Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.
Bounds for probability of success of classical genetic algorithm based on hamming distance
Shiu Yin Yuen; Bernard K. S. Cheung
2006-01-01
Genetic algorithms have proven to be reasonably good optimization algorithms. Despite many successful applications, there is a lack of theoretical insight into why they work so well. In this paper, Vose-Liepins' so called \\
Evolution of a human-competitive quantum fourier transform algorithm using genetic programming
Paul Massey; John A. Clark; Susan Stepney
2005-01-01
In this paper, we show how genetic programming (GP) can be used to evolve system-size-independent quantum algorithms, and present a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP.
Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)
1993-07-01
A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.
Image Retouching Mothod using Interactive Genetic Algorithm in Mobile Blog Environment
Cho, Sung-Bae
Image Retouching Mothod using Interactive Genetic Algorithm in Mobile Blog Environment with online blog service and photo. However it is difficult to retouch image in mobile environment Algorithm, Mobile Blog, Mobile Interface, Photo Retouching, Photo Refinement 2009 ( ) (No. R01
Design Space Exploration of incompletely specified Embedded Systems by Genetic Algorithms
Huss, Sorin A.
Design Space Exploration of incompletely specified Embedded Systems by Genetic Algorithms Stephan design space exploration algorithm, which jointly determines a complete set of Pareto optimal for new modules in a single optimization run. This design space exploration method is based
Dynamic and fault tolerant three-dimensional cellular genetic algorithms
Al Naqi, Asmaa
2012-11-29
In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has been very active, especially in the last decade. These algorithms started to evolve when scientists from various regions of the ...
Yannis Marinakis; Magdalene Marinaki; Nikolaos F. Matsatsinis; Constantin Zopounidis
2009-01-01
This paper presents a new memetic algorithm, which is based on the concepts of genetic algorithms (GAs), particle swarm optimization\\u000a (PSO) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed\\u000a algorithm is a two phase algorithm which combines a memetic algorithm for the solution of the feature selection problem and\\u000a a GRASP
Empirical study of self-configuring genetic programming algorithm performance and behaviour
NASA Astrophysics Data System (ADS)
Semenkin, E.; Semenkina, M.
2015-01-01
The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems.
Xiuli Wu; Shudong Sun; Ganggang Niu; Yinni Zhai
2006-01-01
First, a multi-objective immune genetic algorithm integrating immune algorithm and genetic algorithm for flexible job shop\\u000a scheduling is designed. Second, Markov chain is used to analyze quantitatively its convergence. Third, a simulation experiment\\u000a of the flexible job shop scheduling is carried out. Running results show that the proposed algorithm can converge to the Pareto\\u000a frontier quickly and distribute evenly along
An Improved Genetic Algorithm for Dual-Resource Constrained Flexible Job Shop Scheduling
Cao Xianzhou; Yang Zhenhe
2011-01-01
a xianzhoucao@163.com , b yang_zhenhe@126.com Abstract—In this paper, a dual-resource constrained job shop scheduling problem was studied. According to the information processing mechanism of an immune system in biotic science, a new immune Genetic Algorithm for flexible job shop scheduling through combining immune algorithm with genetic algorithm was proposed. The algorithm can effectively avoid the premature convergence problem caused by
Genetic algorithm in DNA computing: A solution to the maximal clique problem
Yuan Li; Chen Fang; Qi Ouyang
2004-01-01
Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA computing. Using the idea of\\u000a Darwinian evolution, we introduce a genetic DNA computing algorithm to solve the maximal clique problem. All the operations\\u000a in the algorithm are accessible with today’s molecular biotechnology. Our computer simulations show that with this new computing\\u000a algorithm, it
Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.
1997-01-01
The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.
Flexible Job-Shop Scheduling Problem by Genetic Algorithm
NASA Astrophysics Data System (ADS)
Ida, Kenichi; Oka, Kensaku
Flexible Job-shop Scheduling Problem is expansion of the traditional Job-shop Scheduling Problem that an operation can be processed one or more machines. The purpose of this problem is to look for the smallest makespan. For that purpose, it is necessary to decide optimal assignment of machines to operations and order of operations on machines. In this paper, we focus on maximum of workloads for all machines and propose new suvival selection, creation method of initial solution, mutation, and escape method to Genetic Algorithm for this problem. The efficacy of our method is demonstrated by comparing its numerical experiment results with another methods.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Optimal brushless DC motor design using genetic algorithms
NASA Astrophysics Data System (ADS)
Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.
2010-11-01
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
An Evolved Wavelet Library Based on Genetic Algorithm
Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.
2014-01-01
As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31?dB improvement in the average PSNR and a 0.39?dB improvement in the maximum PSNR. PMID:25405225
Integrating GIS and genetic algorithms for automating land partitioning
NASA Astrophysics Data System (ADS)
Demetriou, Demetris; See, Linda; Stillwell, John
2014-08-01
Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.
Spang, Rainer
Development Thymus Â T cell DN2 DN3 DN4 DPL DPS SP8 SP4 DN1 DN Â double negatives, DP Â double positives, SP: Algorithmics Group http://algorithmics.molgen.mpg.de Data Thymus DN2 DN3 DN4 DPL DPS SP8 SP4 Â· T Cell [Hoffman
Improving the performance of genetic algorithms for terrain categorization of multispectral images
NASA Astrophysics Data System (ADS)
Larch, David E.
1996-03-01
A method that uses a genetic algorithm (GA) to optimize rules for categorizing terrain as depicted in multispectral data has been developed by us. A variety of multispectral data have been used in the work. Linear techniques have not separated terrain categories with sufficient accuracy so that genetic algorithms have been applied to the problem. Genetic algorithms, in general, are a nonlinear optimization technique based on the biological ideas of natural selection and survival of the fittest. For the work presented here, the genetic algorithm optimizes rules for the categorization of terrain. The genetic algorithm produced promising results for terrain categorization; however, work continues with efforts to improve classification accuracy. As part of this effort, new rule types have been added to the genetic algorithm's repertoire. These new rule types include the clustering of data, the ratio of bands, the linear combination of bands, and the second order combination of two and three bands. Improved performance of the rules is demonstrated.
Improving the performance of genetic algorithms for terrain categorization of multispectral images
NASA Astrophysics Data System (ADS)
Larch, David E.
1996-06-01
A method that uses a genetic algorithm (GA) to optimize rules for categorizing terrain as depicted in multispectral data has been developed by us. A variety of multispectral data have been used in the work. Linear techniques have not separated terrain categories with sufficient accuracy so that genetic algorithms have been applied to the problem. Genetic algorithms, in general, are a nonlinear optimization technique based on the biological ideas of natural selection and survival of the fittest. For the work presented here, the genetic algorithm optimizes rules for the categorization of terrain. The genetic algorithm produced promising results for terrain categorization; however, work continues with efforts to improve classification accuracy. As part of this effort, new rule types have been added to the genetic algorithm's repertoire. These new rule types include the clustering of data, the ratio of bands, the linear combination of bands, boxes in spectral space, and the second order combination of three bands. Improved performance of the rules is demonstrated.
Genetic Algorithms And Its Application To Economic Load Dispatch
NASA Astrophysics Data System (ADS)
Borana, Kavita
2010-11-01
Genetic Algorithm (GA) is a search method that simulates the process of natural selection and it attempts to find a good solution to some problem by randomly generating a collection of potential solutions to the problem and then manipulating those solutions using genetic operators. Through selection, mutation and re-combination (crossover) operations, better solutions are hopefully generated out of the current set of potential solutions. This process continues until an acceptable solution is found. GA is becoming popular to solve the optimization problems mainly because of its robustness in finding optimal solution and ability to provide near optimal solution close to global optimum. The ELD approach is tested on sample 3-generator system with the load of 24 hours.
Nonlinear predictive control of a drying process using genetic algorithms.
Yuzgec, Ugur; Becerikli, Yasar; Turker, Mustafa
2006-10-01
A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control. PMID:17063940
Fuel management optimization using genetic algorithms and code independence
DeChaine, M.D.; Feltus, M.A.
1994-12-31
Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs) address these problems and, therefore, appear ideal for fuel management optimization. They do not require derivative information and work well with combinatorial. functions. The GAs are a stochastic method based on concepts from biological genetics. They take a group of candidate solutions, called the population, and use selection, crossover, and mutation operators to create the next generation of better solutions. The selection operator is a {open_quotes}survival-of-the-fittest{close_quotes} operation and chooses the solutions for the next generation. The crossover operator is analogous to biological mating, where children inherit a mixture of traits from their parents, and the mutation operator makes small random changes to the solutions.
A Genetic Algorithm based Approach for Multi-Objective Data-Flow Graph Optimization
Coello, Carlos A. Coello
A Genetic Algorithm based Approach for Multi-Objective Data-Flow Graph Optimization Birger Landwehr.uni-dortmund.de Abstract: This paper presents a genetic algorithm based approach for algebraic optimization of behavioral that the correct- ness of algebraic transformations realized by the underlying genetic operators selection
ExGA II: An Improved Exonic Genetic Algorithm for the Multiple Knapsack Problem
Bullinaria, John
closely the principles of molecular genetics. In [10], we presented an adaptive GA, labelled ExGA IExGA II: An Improved Exonic Genetic Algorithm for the Multiple Knapsack Problem Philipp Rohlfshagen Kingdom J.A.Bullinaria@cs.bham.ac.uk ABSTRACT ExGA I, a previously presented genetic algorithm, success
A Representation Scheme to Perform Program Induction in a Canonical Genetic Algorithm
Wineberg, Mark
A Representation Scheme to Perform Program Induction in a Canonical Genetic Algorithm Mark Wineberg, K1S 5B6 wineberg@scs.carleton.ca, oppacher@scs.carleton.ca Abstract. This paper studies Genetic Programming (GP) and its relation to the Genetic Algorithm (GA). GP uses a GA approach to breed successive
A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows
Jean Berger; Martin Salois; Regent Begin
1998-01-01
A variety of hybrid genetic algorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of the intermediate solutions computed during the evolutionary process to explore the solution space. This paper presents a new hybrid genetic algorithm
Nelson, Brent E.
Genetic Algorithms In Software and In Hardware --- A Performance Analysis Of Workstation and Custom This paper analyzes the performance differences found between the hardware and software versions of a genetic implementation we found that a simple fourFPGA genetic algorithm design outperforms a state
A DNA genetic algorithm with reconstruct operator for chemical process modeling
Chen Xiao; Zhang Ridong
2011-01-01
A DNA genetic algorithm with reconstruct operator (rDNA-GA) is presented in this paper. In the proposed algorithm, potential solutions of the problems are represented as individuals with nucleotide bases, and genetic operators inspired by DNA operations are adopted over the individuals. Among the genetic operators, reconstruct operator is a new operator which is used to improve the diversity of the
Genetic algorithm applied to hierarchically coupled associative memories.
Gomes, Rogério Martins; Braga, Antônio Pádua; Borges, Henrique E
2010-01-01
Inspired by the theory of neuronal group selection (TNGS), we have carried out an analysis of the capacity of convergence of a multi-level associative memory based on coupled generalized-brain-state-in-a-box (GBSB) networks through evolutionary computation. The TNGS establishes that a memory process can be described as being organized functionally in hierarchical levels where higher levels coordinate sets of functions of lower levels. According to this theory, the most basic units in the cortical area of the brain are called neuronal groups or first-level blocks of memories and the higher-level memories are formed through selective strengthening or weakening of the synapses amongst the neuronal groups. In order to analyse this effect, we propose that the higher levels should emerge through a learning mechanism as correlations of lower level memories. According to this proposal, this paper describes a method of acquiring the inter-group synapses based on a genetic algorithm. Thus the results show that genetic algorithms are feasible as they allow the emergence of complex behaviours which could be potentially excluded in other learning process. PMID:20020348
Feature Subset Selection, Class Separability, and Genetic Algorithms
Cantu-Paz, E
2004-01-21
The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be unpractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. The experiments suggest that the hybrid usually finds compact feature subsets that give the most accurate results, while beating the execution time of the other wrappers.
Internal Lattice Reconfiguration for Diversity Tuning in Cellular Genetic Algorithms
Morales-Reyes, Alicia; Erdogan, Ahmet T.
2012-01-01
Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover, internal reconfiguration allows the adjacency of individuals to be maintained. Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy. PMID:22859973
Genetic Algorithm for Structural Optimization of Tubular Nanostructures
NASA Astrophysics Data System (ADS)
Davies, Teresa E. B.; Popa, Mihail M.; Ciobanu, Cristian V.
2007-11-01
Why can metals or oxides form nanotubes, sometimes even chiral ones? How could silicon, which has little or no propensity for sp2 hybridization, form nanotubes akin with the well understood carbon nanotubes in which the atoms are unequivocally sp2 hybridized? It would be perhaps beneficial, if not expected, for the theory to step up to the plate and engage in the discovery of credible growth mechanisms and atomic structures for the tubular and multi-shell structures that can determine future directions in nanoscience and nanotechnology. In an effort to contribute to answering these questions, we present here a global optimization method designed specifically for tubular structures. Due to the recent success of the genetic algorithms in elucidating structures of 1- and 2-dimensional nanoscale materials, we base our optimization procedure on the same evolutionary principles. We have found that the cross-over operations based on planar cuts (which were so successful previously) are not sufficient to ensure convergence to lowest energy structures, and design new ones. The application of the new and more diverse cross-over operations has resulted in converged structures for different materials, which provides confidence in pursuing the application of genetic algorithm for finding the structures of new tubular materials.
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Sve?ko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
Segmentation of thermographic images of hands using a genetic algorithm
NASA Astrophysics Data System (ADS)
Ghosh, Payel; Mitchell, Melanie; Gold, Judith
2010-01-01
This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.
Actuator Placement Via Genetic Algorithm for Aircraft Morphing
NASA Technical Reports Server (NTRS)
Crossley, William A.; Cook, Andrea M.
2001-01-01
This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of Genetic Algorithm (GA) approaches that could be used to solve an actuator placement problem by treating this as a discrete optimization problem. In these efforts, the actuators are assumed to be "smart" devices that change the aerodynamic shape of an aircraft wing to alter the flow past the wing, and, as a result, provide aerodynamic moments that could provide flight control. The earlier work investigated issued for the problem statement, developed the appropriate actuator modeling, recognized the importance of symmetry for this problem, modified the aerodynamic analysis routine for more efficient use with the genetic algorithm, and began a problem size study to measure the impact of increasing problem complexity. The research discussed in this final summary further investigated the problem statement to provide a "combined moment" problem statement to simultaneously address roll, pitch and yaw. Investigations of problem size using this new problem statement provided insight into performance of the GA as the number of possible actuator locations increased. Where previous investigations utilized a simple wing model to develop the GA approach for actuator placement, this research culminated with application of the GA approach to a high-altitude unmanned aerial vehicle concept to demonstrate that the approach is valid for an aircraft configuration.
Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-Law Low-Thrust Orbit Transfers
Arizona, University of
Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-Law Low-Thrust Orbit Transfers algorithms, Low-thrust orbit transfer, Q-law, Non-dominated sorting genetic algorithm 1. INTRODUCTION Many.Lee@jpl.nasa.gov ABSTRACT Multi-objective genetic algorithms (MOGA) are used to optimize a low-thrust spacecraft control law
Genetic Algorithms, Pulsar Planets, and Ionized Interstellar Microturbulence
NASA Astrophysics Data System (ADS)
Lazio, T. J.
1997-09-01
We probe the intense microturbulence in the Galactic center and the radio-wave scattering it generates by analyzing observations of extragalactic sources, OH and H$_2$O masers, and free-free emission. The region responsible for the enhanced, anisotropic angular broadening of Sgr~A$^*$ and nearby OH masers is within 150~pc of the Galactic center and has an angular radius $\\approx 1\\arcdeg$. The enhanced scattering probably occurs in the interface regions between $10^7$~K gas and molecular clouds and is a manifestation of the energetic processes occurring in the Galactic center. Radio scattering measurements are also used to probe turbulent gas toward the Galactic anticenter. Ionized gas at Galactocentric distances $\\sim 50$~kpc is suggested by absorption lines in quasar spectra, the appearance of the H 1 disks of nearby galaxies, and models for low-redshift quasar absorption systems and Galactic ``fountains.'' We conducted multifrequency, Very Long Baseline Array (VLBA) observations on twelve extragalactic sources in order to measure their scattering diameters. Seven sources are at $|b| < 1\\arcdeg$ and their lines of sight potentially probe path lengths $\\gtrsim 50$~kpc through the disk. We find that the ionized disk is unwarped, has an extent of $\\approx 20$~kpc, and traces the extent of massive star formation in the outer Galaxy. Planetary companions to neutron stars are challenging to recognize amid the several processes that contribute to pulsar arrival time data. We use a genetic algorithm to search for planetary companions to pulsars. Genetic algorithms are an optimization method that uses biological-like concepts such as survival of the fittest, mutation, and chromosome exchange. The algorithm searches parameter space in the same way that life finds optimal niches in the biological environment---incremental rewarding of successful variations. Fitting for Keplerian orbits requires a search through four non-linear parameters per planet and is especially difficult if there is a large range of planetary masses and orbital periods. We find that the GA is more efficient and more accurate than the downhill simplex and simulated annealing algorithms. We confirm the presence of a second planetary companion to PSR~B0329$+$54 and identify possible companions to B1911$-$04 and B1929$+$10. (SECTION: Dissertation Summaries)
Y. Volkan Pehlivanoglu
A new optimization algorithm called multi-frequency vibrational genetic algorithm (mVGA) that can be used to solve the path planning problems of autonomous unmanned aerial vehicles (UAVs) is significantly improved. The algorithm emphasizes a new mutation application strategy and diversity variety such as the global random and the local random diversity. Clustering method and Voronoi diagram concepts are used within the
Flexible Job Shop scheduling problem solving based on genetic algorithm with model constraints
Xuan Du; Zongbin Li; Wei Xiong
2008-01-01
An improved genetic algorithm (GA) is presented to solve Flexible Job Shop scheduling (FJSS) problem. This algorithm combines GA with constraint model based on polychromatic sets theory (PST). According to the characteristic of FJSS problem, this algorithm uses contour matrix to formalize the restrictions between workpiece and operations, and between operations and machines. During the process of encoding, decoding, crossover
A Genetic Algorithm for Finite State Automata Induction with an Application to Phonotactics
Anja Belz; Berkan Eskikaya
1998-01-01
This paper presents a genetic algorithm for the automatic construction of finite-state automata from positive data. The algorithm is suitable for constructing automata from complete and incomplete presentation of data. In the case of incomplete presentation of data, different degrees of generalisation can be achieved with a set of search parameters. This paper describes the algorithm, and presents results for
Resolution of simple plant location problems using an adapted genetic algorithm
Jorng-Tzong Horng; Li-Yi Lin; Baw-Jhiune Liu; Cheng-Yan Kao
1999-01-01
This investigation presents an adapted genetic algorithm to resolve simple plant location problems. The proposed algorithm applies a clustering technique as mutation guidance and a novel local search method to enhance the solution quality. The proposed algorithm is then applied to the fifteen test problems taken from Beasley's OR-Library (J.E. Beasley, 1990). Empirical results indicate that the error rate of
A Genetic Algorithm for Evolving Stochastic Context-Free James Anderson, Joe Staines & Paula Tataru
Goldschmidt, Christina
A Genetic Algorithm for Evolving Stochastic Context-Free Grammars James Anderson, Joe Staines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.1 Modifications to the Grammar Structure . . . . . . . . . . . . . . . . . . . . . 15 3.5 Modification to CYK and Inside/Outside Algorithms . . . . . . . . . . . . . . . . . . 23 3.5.1 CYK Algorithm
Evolution of a Human-Competitive Quantum Fourier Transform Algorithm Using Genetic Programming
Fernandez, Thomas
programming (GP) can be used to evolve system-size-independent quantum algorithms, and present a human-competitive performance for quantum algorithm design? In this paper we show how GP has been used to evolve a humanEvolution of a Human-Competitive Quantum Fourier Transform Algorithm Using Genetic Programming Paul
Non-Uniform search domain based Genetic algorithm for the optimization of real time FFT Processor
Arslan, Tughrul
Non-Uniform search domain based Genetic algorithm for the optimization of real time FFT Processor for optimization of word length coefficients in a pipelined FFT processor. The algorithm optimizes memory and buses data and coefficients in real time pipelined FFT processor architectures. The algorithm is specially
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
NASA Astrophysics Data System (ADS)
Abrahart, R. J.
2004-05-01
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 estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and operational specialization. The standard approach to neural-evolution is at the network level such that a population of working solutions is manipulated until the fittest member is found. SANE [Symbiotic Adaptive Neuro-Evolution]1 source code offers an alternative method based on co-operative co-evolution in which a population of hidden neurons is evolved. The task of each hidden neuron is to establish appropriate connections that will provide: [i] a functional solution and [ii] performance improvements. Each member of the population attempts to optimize one particular aspect of the overall modelling process and evolution can lead to several different forms of specialization. This method of adaptive evolution also facilitates the creation of symbiotic relationships in which individual members must co-operate with others - who must be present - to permit survival. 1http://www.cs.utexas.edu/users/nn/pages/software/abstracts.html#sane-c
Genetic algorithm parameter optimization: applied to sensor coverage
NASA Astrophysics Data System (ADS)
Sahin, Ferat; Abbate, Giuseppe
2004-08-01
Genetic Algorithms are powerful tools, which when set upon a solution space will search for the optimal answer. These algorithms though have some associated problems, which are inherent to the method such as pre-mature convergence and lack of population diversity. These problems can be controlled with changes to certain parameters such as crossover, selection, and mutation. This paper attempts to tackle these problems in GA by having another GA controlling these parameters. The values for crossover parameter are: one point, two point, and uniform. The values for selection parameters are: best, worst, roulette wheel, inside 50%, outside 50%. The values for the mutation parameter are: random and swap. The system will include a control GA whose population will consist of different parameters settings. While this GA is attempting to find the best parameters it will be advancing into the search space of the problem and refining the population. As the population changes due to the search so will the optimal parameters. For every control GA generation each of the individuals in the population will be tested for fitness by being run through the problem GA with the assigned parameters. During these runs the population used in the next control generation is compiled. Thus, both the issue of finding the best parameters and the solution to the problem are attacked at the same time. The goal is to optimize the sensor coverage in a square field. The test case used was a 30 by 30 unit field with 100 sensor nodes. Each sensor node had a coverage area of 3 by 3 units. The algorithm attempts to optimize the sensor coverage in the field by moving the nodes. The results show that the control GA will provide better results when compared to a system with no parameter changes.
Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.
2008-01-01
This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558
Fuzzy control with genetic algorithm in a batch bioreactor.
Ahio?lu, Suna; Altinten, Ayla; Ertunç, Suna; Erdo?an, Sebahat; Hapo?lu, Hale
2013-12-01
In this study, the growth medium temperature in a batch bioreactor was controlled at the set point by using fuzzy model-based control method. Fuzzy control parameters which are membership functions and relation matrix were found using genetic algorithm. Heat input given from the immersed heater and the cooling water flow rate were selected as the manipulated variables in order to control the growth medium temperature in the bioreactor. Controller performance was tested in the face of different types of input variables. To eliminate the noise on the temperature measurements, first-order filter was used in the control algorithm. The achievement of the temperature control was analyzed in terms of both microorganism concentration which was reached at the end of the stationary phase and the performance criteria of Integral of the Absolute Error. It was concluded that the cooling flow rate was suitable as manipulated variable with regard to microorganism concentration. On the other hand, performance of the controller was satisfactory when the heat input given from the immersed heater was manipulated variable. PMID:24037514
Human emotion detector based on genetic algorithm using lip features
NASA Astrophysics Data System (ADS)
Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga
2010-04-01
We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.
An Implementation of Intrusion Detection System Using Genetic Algorithm
Hoque, Mohammad Sazzadul; Bikas, Md Abu Naser; 10.5121/ijnsa.2012.4208
2012-01-01
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark datase...
Fuzzy logic versus niched Pareto multiobjective genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Reardon, Brian J.
1998-11-01
A new multiobjective selection procedure for a genetic algorithm (GA) based on the paradigms of fuzzy logic is introduced, discussed and compared to the niched Pareto selection procedure. In the two example problems presented here (Schaffer's F2 problem and a simplified Born-Mayer potential) the fuzzy logic procedure optimized the parameters of functions in a manner of comparable efficiency to that of the niched Pareto approach. The two main advantages of the fuzzy logic approach over the niched Pareto approach are that the experimental error or `uncertainty' in the objective values can be accounted for and, unlike the niched Pareto approach, the efficiency of the fuzzy logic GA is shown to be independent of the number of objectives.
Improving ecological forecasts of copepod community dynamics using genetic algorithms
NASA Astrophysics Data System (ADS)
Record, N. R.; Pershing, A. J.; Runge, J. A.; Mayo, C. A.; Monger, B. C.; Chen, C.
2010-08-01
The validity of computational models is always in doubt. Skill assessment and validation are typically done by demonstrating that output is in agreement with empirical data. We test this approach by using a genetic algorithm to parameterize a biological-physical coupled copepod population dynamics computation. The model is applied to Cape Cod Bay, Massachusetts, and is designed for operational forecasting. By running twin experiments on terms in this dynamical system, we demonstrate that a good fit to data does not necessarily imply a valid parameterization. An ensemble of good fits, however, provides information on the accuracy of parameter values, on the functional importance of parameters, and on the ability to forecast accurately with an incorrect set of parameters. Additionally, we demonstrate that the technique is a useful tool for operational forecasting.
An adaptive genetic algorithm for crystal structure prediction
Wu, Shunqing [Ames Laboratory; Ji, Min [Ames Laboratory; Wang, Cai-Zhuang [Ames Laboratory; Nguyen, Manh Cuong [Ames Laboratory; Zhao, Xin [Ames Laboratory; Umemoto, K. [Ames Laboratory; Wentzcovitch, R. M. [University of Minnesota; Ho, Kai-Ming [Ames Laboratory
2013-10-31
We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.
An adaptive genetic algorithm for crystal structure prediction.
Wu, S Q; Ji, M; Wang, C Z; Nguyen, M C; Zhao, X; Umemoto, K; Wentzcovitch, R M; Ho, K M
2014-01-22
We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles. PMID:24351274
Population Induced Instabilities in Genetic Algorithms for Constrained Optimization
NASA Astrophysics Data System (ADS)
Vlachos, D. S.; Parousis-Orthodoxou, K. J.
2013-02-01
Evolutionary computation techniques, like genetic algorithms, have received a lot of attention as optimization techniques but, although they exhibit a very promising potential in curing the problem, they have not produced a significant breakthrough in the area of systematic treatment of constraints. There are two mainly ways of handling the constraints: the first is to produce an infeasibility measure and add it to the general cost function (the well known penalty methods) and the other is to modify the mutation and crossover operation in a way that they only produce feasible members. Both methods have their drawbacks and are strongly correlated to the problem that they are applied. In this work, we propose a different treatment of the constraints: we induce instabilities in the evolving population, in a way that infeasible solution cannot survive as they are. Preliminary results are presented in a set of well known from the literature constrained optimization problems.
A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb; Amrit Pratap; Sameer Agarwal; T. Meyarivan
2002-01-01
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated\\u000d\\u000a\\u0009sorting and sharing have been criticized mainly for: (1) their O(MNsup3\\/sup)\\u000d\\u000a\\u0009computational complexity (where M is the number of objectives and\\u000d\\u000a\\u0009N is the population size); (2) their non-elitism approach; and (3)\\u000d\\u000a\\u0009the need to specify a sharing parameter. In this paper, we suggest\\u000d\\u000a\\u0009a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated\\u000d\\u000a\\u0009Sorting Genetic
Darwin Meets Einstein: LISA Data Analysis Using Genetic Algorithms
Jeff Crowder; Neil J. Cornish; Lucas Reddinger
2006-01-17
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 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.
Alien Genetic Algorithm for Exploration of Search Space
NASA Astrophysics Data System (ADS)
Patel, Narendra; Padhiyar, Nitin
2010-10-01
Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.
Calculation of the partition function using quantum genetic algorithms
NASA Astrophysics Data System (ADS)
Grigorenko, I.; Garcia, M. E.
2002-10-01
We present a new method based on genetic algorithms which permits to determine efficiently the partition function and the excitation spectrum of few-body quantum systems. In our approach, we use a variational formulation for the partition function Z of the system as a functional of its eigenfunctions. Z is obtained by applying the procedure of survival of the fittest, starting from initial random population. During the evolution the best representative converges to a set of eigenfunctions for a given Hamiltonian, while the partition function attains its global extremum (maximum) for a given temperature. We calculate the spectrum and the partition function in the case of few interacting particles in one-dimensional infinite potential well. We investigate formation of the Wigner crystal and study its melting induced by termal and quantum fluctuations.
A new perspective on dark energy modeling via genetic algorithms
Nesseris, Savvas; García-Bellido, Juan, E-mail: savvas.nesseris@uam.es, E-mail: juan.garciabellido@uam.es [Instituto de Física Teórica UAM-CSIC, Universidad Autonóma de Madrid, Cantoblanco, 28049 Madrid (Spain)
2012-11-01
We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity d{sub L}(z) and the angular diameter distance d{sub A}(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density ?{sub m}(a) in the growth rate data, f?{sub 8}(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function ?{sub M}(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, ??d{sub L}(z)/(1+z){sup 2}d{sub A}(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-? deviation from one at redshifts z ? 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.
Cloud identification using genetic algorithms and massively parallel computation
NASA Technical Reports Server (NTRS)
Buckles, Bill P.; Petry, Frederick E.
1996-01-01
As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced.
Novel genetic algorithm search procedure for LEED surface structure determination.
Viana, M L; dos Reis, D D; Soares, E A; Van Hove, M A; Moritz, W; de Carvalho, V E
2014-06-01
Low Energy Electron Diffraction (LEED) is one of the most powerful experimental techniques for surface structure analysis but until now only a trial-and-error approach has been successful. So far, fitting procedures developed to optimize structural and nonstructural parameters-by minimization of the R-factor-have had a fairly small convergence radius, suitable only for local optimization. However, the identification of the global minimum among the several local minima is essential for complex surface structures. Global optimization methods have been applied to LEED structure determination, but they still require starting from structures that are relatively close to the correct one, in order to find the final structure. For complex systems, the number of trial structures and the resulting computation time increase so rapidly that the task of finding the correct model becomes impractical using the present methodologies. In this work we propose a new search method, based on Genetic Algorithms, which is able to determine the correct structural model starting from completely random structures. This method-called here NGA-LEED for Novel Genetic Algorithm for LEED-utilizes bond lengths and symmetry criteria to select reasonable trial structures before performing LEED calculations. This allows a reduction of the parameter space and, consequently of the calculation time, by several orders of magnitude. A refinement of the parameters by least squares fit of simulated annealing is performed only at some intermediate stages and in the final step. The method was successfully tested for two systems, Ag(1?1?1)(4 × 4)-O and Au(1?1?0)-(1 × 2), both in theory versus theory and in theory versus experiment comparisons. Details of the implementation as well as the results for these two systems are presented. PMID:24824047
Applications of Improved Multi-Agent Genetic Algorithm to Water Pollution Control System Planning
NASA Astrophysics Data System (ADS)
Dong, Qianjin; Lu, Fan; Gao, Shichun
2010-05-01
Combining the ability of apperception and counteractive to environment of agent with search method of genetic algorithm, an improved multi-agent genetic algorithm (MAGA) is advanced. It ensures diversity of population and improves local search ability of genetic algorithm by simulating competition, cooperate and self-study of different agents using neighboring cross operator, aberrance operator and self-learning operator of agent. The algorithm is applied to the optimal planning for the waste treatment system of Urumqi, Xinjiang. Results demonstrate an improved performance in finding the global minimum when water quality requirements have been fulfilled. The result demonstrates nicer performance and factual value of MAGA.
A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm
Jaehun Lee; Wooyong Chung; Euntai Kim; Soohan Kim
2010-01-01
In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called\\u000a the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome\\u000a and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote\\u000a the
Genetic Algorithms, Pulsar Planets, and Ionized Interstellar Microturbulence
NASA Astrophysics Data System (ADS)
Lazio, T. Joseph W.
1997-10-01
We probe the intense microturbulence in the Galactic center and the radio-wave scattering it generates by analyzing observations of extragalactic sources, OH and H2O masers, and free-free emission. The region responsible for the enhanced, anisotropic angular broadening of Sgr A* and nearby OH masers is within 150 pc of the Galactic center and has an angular radius ? 1o. The enhanced scattering probably occurs in the interface regions between 107 K gas and molecular clouds and is a manifestation of the energetic processes occurring in the Galactic center. Radio scattering measurements are also used to probe turbulent gas toward the Galactic anticenter. Ionized gas at Galactocentric distances ~50 kpc is suggested by absorption lines in quasar spectra, the appearance of the H I disks of nearby galaxies, and models for low-redshift quasar absorption systems and Galactic 'fountains.' We conducted multifrequency, Very Long Baseline Array (VLBA) observations on twelve extragalactic sources in order to measure their scattering sizes. Seven sources are at | b| < 1o and their lines of sight potentially probe path lengths ~>50 kpc through the disk. We find that the ionized disk is unwarped, has an extent of ?20 kpc, and traces the extent of massive star formation in the outer Galaxy. Planetary companions to neutron stars are challenging to recognize amid the several processes that contribute to pulsar arrival time data. We use a genetic algorithm to search for planetary companions to pulsars. Genetic algorithms are an optimization method that uses biological-like concepts such as survival of the fittest, mutation, and chromosome exchange. The algorithm searches parameter space in the same way that life finds optimal niches in the biological environment-incremental rewarding of successful variations. Fitting for Keplerian orbits requires a search through four non-linear parameters per planet and is especially difficult if there is a large range of planetary masses and orbital periods. We find that the GA is more efficient and more accurate than the downhill simplex and simulated annealing. We confirm the presence of a second planetary companion to PSR B0329+54 and identify possible companions to B1911-04 and B1929+10.
Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2001-01-01
A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.
A genetic algorithm based approach to thermal unit commitment of electric power systems
X. Ma; A. A. El-Keib; R. E. Smith; H. Ma
1995-01-01
This paper presents a new approach based on genetic algorithms to solve the thermal unit commitment problem of electric power systems. Genetic algorithms (GAs) are general search techniques based on the biological metaphor and are very suitable for solving combinatorial optimization problems. Because of its nonconvex and combinatorial nature, the unit commitment problem is difficult to solve by conventional programming
New knowledge-based genetic algorithm for excavator boom structural optimization
NASA Astrophysics Data System (ADS)
Hua, Haiyan; Lin, Shuwen
2014-03-01
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
A hybrid genetic algorithm\\/fuzzy logic approach to manufacturing process control
B. Freisleben; S. Strelen
1995-01-01
Genetic algorithms and fuzzy logic have been proven to be effective in a variety of control applications. We present an approach for automatically determining the control parameters of a machine used in a manufacturing environment for producing cookies and other pastry. The proposed solution is based on augmenting a genetic algorithm with elements of fuzzy logic, since either of them
Genetic Algorithm for the Topological Design of Survivable Optical Transport Networks
Rui Manuel Morais; Claunir Pavan; Armando Nolasco Pinto; Cristina Requejo
2011-01-01
We develop a genetic algorithm for the topo- logical design of survivable optical transport networks with minimum capital expenditure. Using the developed genetic algorithm we can obtain near-optimal topologies in a short time. The quality of the obtained solutions is assessed using an integer linear programming model. Two initial popula- tion generators, two selection methods, two crossover op- erators, and
Plasma X-ray Spectra Analysis Using Genetic Algorithms Igor E. Golovkin
Louis, Sushil J.
Plasma X-ray Spectra Analysis Using Genetic Algorithms Igor E. Golovkin Department of Physics for plasma diagnostics. We use genetic algorithms to automatically analyze experi- mental X-ray line spectra-ray line spectra. 1 INTRODUCTION X-ray spectroscopic analysis is a widely used method for hot dense plasma
Hierarchical rank density genetic algorithm for radial-basis function neural network design
Gary G. Yen; Haiming Lu
2002-01-01
In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A hierarchical rank density genetic algorithm (HRDGA) is used to evolve both the neural network's topology and parameters. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the
Fang Li; Fengxuan Jing; Xiaoyao Xie; Zhijie Liu; Anyu Zhang; Yan Chen; Xuhong Yu
2010-01-01
According to research the concept of the genetic algorithm and multidimensional item response theory, using the method o genetic algorithm, this paper propose basic principle of estimation of multidimensional item response, theory model in person ability, changes and take the place of the calculation procedure, and gives the program design method.
Research on Fault Diagnosis of Mixed-Signal Circuits Based on Genetic Algorithms
Shangcong Feng; Xiaofeng Wang
2012-01-01
As the fault modes of mixed-signal circuits growing, aiming at the features for its signal are both analog and digital amount, the paper analyzed that the fault diagnosis program of mixed-signal circuits with genetic algorithms by using SABER simulation method to inject faults and data collection based on a brief discussion of basic principles and operation of genetic algorithms, focused
A fixed functional set genetic algorithm (FFSGA) approach for function approximation
Fernandez, Thomas
an optimal balance between model complexity and model applicability by applying basic principles of modelA fixed functional set genetic algorithm (FFSGA) approach for function approximation Mohammad@engr.uky.edu This paper describes a simple mathematical technique that uses a genetic algorithm and least squares
MECHANISTIC-BASED GENETIC ALGORITHM SEARCH ON A BEOWULF CLUSTER OF LINUX PCS
Hoffman, Forrest M.
effective. INTRODUCTION The advent of Beowulf-style computers has brought cluster computing within the reachMECHANISTIC-BASED GENETIC ALGORITHM SEARCH ON A BEOWULF CLUSTER OF LINUX PCS Jin-Ping Gwo), Beowulf Linux cluster. ABSTRACT A simple genetic algorithm (SGA) was implemented on a cluster of Linux PCs
Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms
Tsutsui, Shigeyoshi
Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms Abstract In this paper, we proposedsimplex crossover (SPX), a multi- parent recombination operator for real-coded genetic. Introduction In many Evolutionary Algorithms (EAs), a recombination operation with two parents is commonly used
Multi-parent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring
Tsutsui, Shigeyoshi
Multi-parent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring coded genetic algorithms with several types of multi-parent recombination operators and found evidence that multi-parent recombination with center of mass crossover (CMX) seems a good choice for real coded GAs
A novel approach for synthetic aperture radar image processing based on Genetic Algorithm
M. Emre Aydemir; T. Gunel; Ism Erer; S. Kurnaz
2003-01-01
In this study, an evolutionary computing algorithm is utilized for data preparation and analysis of synthetic aperture radar (SAR) imagery for planetary geology. Since its invention by J.H. Holland in the 1990s, the Genetic Algorithm (GA) has already gained popularity in a wide range of engineering applications. The genetic approach is used for processing of SAR imagery to find a
A genetic algorithm approach for scheduling of resources in well-services companies
Monica Fira Lucian Fira Adrian Brezulianu
2012-01-01
In this paper, two examples of resources scheduling in well-services companies are solved by means of genetic algorithms: resources for call solving, people scheduling. The results demonstrate that the genetic algorithm approach can provide acceptable solutions to this type of call solving for scheduling in well-services companies. The suggested approach could be easily extended to various resource scheduling areas: process
Genetic Algorithms based Parameter Identification of Yeast Fed-Batch Cultivation
Mustakerov, Ivan
Genetic Algorithms based Parameter Identification of Yeast Fed-Batch Cultivation Maria Angelova of a fermentation process. Altogether eight realizations of genetic algorithms have been presented - four of simple the others. 1 INTRODUCTION Fermentation processes (FP) are widely used in different branches of industry, i
Exploring the Evolutionary Details of a Two-Population Genetic Algorithm
Kimbrough, Steven Orla
Exploring the Evolutionary Details of a Two-Population Genetic Algorithm Steven Orla Kimbrough1 University of Delaware {wood}@cis.udel.edu Abstract. A two-population Genetic Algorithm for constrained opti toward optimality. Their infeasible but promising offspring are transferred to a second, infeasible
A. T. G. Fuller; B. Nowrouzian; F. Ashrafzadeh
1998-01-01
In a recent paper a novel approach was presented for the restoration of canonical signed-digit (CSD) numbers to their correct format after the application of crossover and mutation operations in genetic algorithms. This paper is concerned with the development of a new technique for the optimization of FIR digital filters over the CSD coefficient space based on genetic algorithms. This
Hisao Ishibuchi; Tsutomu Doi; Yusuke Nojima
2006-01-01
We implement a cellular genetic algorithm with two neighborhood structures following the concept of structured demes: One is for interaction among individuals and the other is for mating. The effect of using these two neighborhood structures on the search ability of cellular genetic algorithms is examined through computational experiments on function optimization prob- lems. Experimental results show that good results
A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem
Günther R. Raidl; Bryant A. Julstrom
2000-01-01
The coding by which chromosomes represent candidate solu- tions is a fundamental design choice in a genetic algorithm. This paper describes a novel coding of spanning trees in a genetic algorithm for the degree-constrained minimum span- ning tree problem. For a connected, weighted graph, this problem seeks to identify the shortest spanning tree whose degree does not exceed an upper
Genetic Algorithms in Optimization of Strength and Ductility of Low-Carbon Steels
S. Ganguly; S. Datta; N. Chakraborti
2007-01-01
A comparative study between the conventional goal attainment strategy and an evolutionary approach using a genetic algorithm has been conducted for the multiobjective optimization of the strength and ductility of low-carbon ferrite-pearlite steels. The optimization is based upon the composition and microstructural relations of the mechanical properties suggested earlier through regression analyses. After finding that a genetic algorithm is more
Genetic algorithms for the design of looped irrigation water distribution networks
Juan Reca; Juan Martínez
2006-01-01
A new computer model called Genetic Algorithm Pipe Network Optimization Model (GENOME) has been developed with the aim of optimizing the design of new looped irrigation water distribution networks. The model is based on a genetic algorithm method, although relevant modifications and improvements have been implemented to adapt the model to this specific problem. It makes use of the robust
A Monte-Carlo study of genetic algorithm initial population generation methods
Raymond R. Hill; Wright-Patterson AFB
1999-01-01
We briefly describe genetic algorithms (GAs) and focus attention on initial population generation methods for two- dimensional knapsack problems. Based on work describing the probability a random solution vector is feasible for 0-1 knapsack problems, we propose a simple heuristic for randomly generating good initial populations for genetic algorithm applications to two-dimensional knapsack problems. We report on an experiment comparing
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) Department of Computer Science & Engineering Michigan State University 2340 Engineering Building East Lansing
Cláudio M. N. A. Pereira; Celso M. F. Lapa
2003-01-01
This work extends the research related to genetic algorithms (GA) in core design optimization problems, which basic investigations were presented in previous work. Here we explore the use of the Island Genetic Algorithm (IGA), a coarse-grained parallel GA model, comparing its performance to that obtained by the application of a traditional non-parallel GA. The optimization problem consists on adjusting several
Fernandez, Thomas
Comparative application of artificial neural networks and genetic algorithms for multivariate time-series of artificial neural networks and genetic algorithms in terms of forecasting and understanding of algal blooms neural networks allow seven-days-ahead predictions of timing and magnitudes of algal blooms
GENETIC-ALGORITHM SEEDING OF IDIOTYPIC NETWORKS FOR MOBILE-ROBOT NAVIGATION
Aickelin, Uwe
GENETIC-ALGORITHM SEEDING OF IDIOTYPIC NETWORKS FOR MOBILE-ROBOT NAVIGATION Amanda M. Whitbrook@cs.nott.ac.uk Keywords: Mobile-robot navigation, genetic algorithm, artificial immune system, idiotypic network. Abstract to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne's idiotypic
Genetic Algorithms Based on Primal-Dual Chromosomes for Royal Road Functions
Yang, Shengxiang
Genetic Algorithms Based on Primal-Dual Chromosomes for Royal Road Functions SHENGXIANG YANG on a pair of chromosomes that are primal-dual to each other in the sense of Hamming distance in genotype. We functions for different performance measures. Key-Words: Genetic algorithm, primal-dual chromosomes, schema
Automatic Calibration of Modified FM Synthesis to Harmonic Sounds using Genetic Algorithms
Smyth, Tamara
Automatic Calibration of Modified FM Synthesis to Harmonic Sounds using Genetic Algorithms Matthieu scheme. Genetic algorithms (GA) have been used rather exten- sively for this purpose, and in particular to further explore its modified counterpart, Modified FM (ModFM), which has not been used as widely, and its
Genetic Algorithms in Visual Art and Music. Colin G. Johnson and Juan Jesus Romero Cardalda.
Kent, University of
Genetic Algorithms in Visual Art and Music. Colin G. Johnson and Juan Jesus Romero Cardalda. Colin). Information and Communications Technology Department. University of A Coru~na. 15071 A Coru~na. Spain. Email Algorithms in Visual Art and Music, which arose from a workshop at the 2000 Genetic and Evolutionary
Wenwu Mao; Lijuan Yu; Hui Zhang; Haihang Wang; Peiliang Ling; Jie Wang
2010-01-01
Due to the complexity, multiplicity and randomness of table tennis matches, the paper presents skill and tactic diagnostic model for table-tennis matches of elite athletes with artificial neural network and genetic algorithm. A back propagation network is used to build basic structure of the model and genetic algorithm is established to optimize the connection weights and threshold values of the
Finding Effective Software Metrics to Classify Maintainability Using a Parallel Genetic Algorithm
Rodrigo A. Vivanco; Nicolino J. Pizzi
2004-01-01
The ability to predict the quality of a software object can be viewed as a classification problem, where software metrics are the features and expert quality rankings the class labels. Evolutionary computational techniques such as genetic algorithms can be used to find a subset of metrics that provide an optimal classification for the quality of software objects. Genetic algorithms are
A BIASED RANDOM-KEY GENETIC ALGORITHM FOR ROAD CONGESTION MINIMIZATION
Fisher, Kathleen
A BIASED RANDOM-KEY GENETIC ALGORITHM FOR ROAD CONGESTION MINIMIZATION LUCIANA S. BURIOL, MICHAEL J to a user equilibrium solution. Obtaining high-quality solutions for this framework is a challenge for large random-key genetic algorithm for the optimization of transportation network performance by strategically
Multi-objective pattern and feature selection by a genetic algorithm
Hisao Ishibuchi; Tomoharu Nakashima
2000-01-01
This paper discusses a genetic-algorithm-based approach for selecting a small number of representative instances from a given data set i n a pattern classification p roblem. The genetic algorithm also selects a small number of significant features. That is, instances and features are simultaneously selected for finding a compact data set. The selected instances and features are used as a
Yanqiu Wang; Jian Zhang; Yueling Zhao; Yu Wang
2008-01-01
Its deficiency was revealed because of traffic pattern identification method of elevator group control system based on using BP neural network, and a new traffic patten identification model is proposed which is based on optimizing fuzzy neural network by genetic algorithm. The genetic algorithm is used to train fuzzy BP neural network, which can overcome the shortcoming of local minimum
Segmentation of MR and CT Images Using a Hybrid Neural Network Trained by Genetic Algorithms
Zümray Dokur
2002-01-01
A novel hybrid neural network trained by the genetic algorithms is presented. Genetic algorithms are used to improve the neural net's classification performance while minimizing the number of nodes. Each node of the network forms a closed region in the input space. The closed regions, which are formed by the nodes, intersect each other. The performance of the proposed hybrid
A customizable FPGA IP core implementation of a general purpose Genetic Algorithm engine
Pradeep Fernando; Hariharan Sankaran; Srinivas Katkoori; Didier Keymeulen; Adrian Stoica; Ricardo Salem Zebulum; Rajeshuni Ramesham
2008-01-01
Hardware implementation of genetic algorithms (GA) is gaining importance as genetic algorithms can be effectively used as an optimization engine for real-time applications (for e.g., evolvable hardware). In this work, we report the design of an IP core that implements a general purpose GA engine which has been successfully synthesized and verified on a Xilinx Virtex II Pro FPGA device
Ivan T. Tanev; Takashi Uozumi; Yoshiharu Morotome
This paper presents the genetic representation in the developed hybrid evolutionary algorithm, applied for real-world case of flexible job shop scheduling problem. The hybrid evolutionary algorithm, which combines priority-dispatching rules (PDRs) with genetic algorithms (GA), is discussed. PDRs offer the advantage of simplicity and low computational cost. GA incorporated into proposed algorithm addresses the myopic nature of PDRs and the
Poli, Riccardo
On the Search Biases of Homologous Crossover in Linear Genetic Programming and Variable-length Genetic Algorithms Riccardo Poli Department of Computer Science University of Essex, UK rpoli of linkage equilibrium. 1 INTRODUCTION Search algorithms typically include three main steps which
On Application of the Local Search and the Genetic Algorithms ...
2010-04-29
Obviously, the changes of the questionnaire, carried out at each step of local search, ... However in a substantial number of cases, the proposed algorithm did not ... tree structure of the questionnaire and the properties of the cost function ...
A multi-population genetic algorithm for a constrained two ...
2008-12-10
Algorithms and Optimization Research Department, AT&T Labs Research,. 180 Park Avenue, Room C241 ..... Current Generation. Next Generation. Best ...... International Journal of Industrial Engineering 4, 130 139. Leung, T., C. Chan, and M.
Double Motor Coordinated Control Based on Hybrid Genetic Algorithm and CMAC
NASA Astrophysics Data System (ADS)
Cao, Shaozhong; Tu, Ji
A novel hybrid cerebellar model articulation controller (CMAC) and online adaptive genetic algorithm (GA) controller is introduced to control two Brushless DC motor (BLDCM) which applied in a biped robot. Genetic Algorithm simulates the random learning among the individuals of a group, and CMAC simulates the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments have been done in MATLAB/SIMULINK. Analysis among GA, hybrid GA-CMAC and CMAC feed-forward control is also given. The results prove that the torque ripple of the coordinated control system is eliminated by using the hybrid GA-CMAC algorithm.
Efficient Improvement of Silage Additives by Using Genetic Algorithms
Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.
2000-01-01
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives. PMID:10742224
Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms
Pollack, Jordan B.
. This kind of genetic integration is quite different from the transfer of genetic information in sexualSymbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms Richard A Brandeis University Waltham, MA USA richardw@cs.brandeis.edu Abstract. Recombination in the Genetic
Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms
Pollack, Jordan B.
. This kind of genetic integration is quite different from the transfer of genetic information in sexualSymbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms Richard A -- Brandeis University -- Waltham, MA -- USA richardw@cs.brandeis.edu Abstract. Recombination in the Genetic
Synthesis of low-power DSP systems using a genetic algorithm
Mark S. Bright; Tughrul Arslan
2001-01-01
Abstract, This paper presents a new tool for the synthesis of low-power VLSI designs, specifically, those designs targeting dig-ital signal processing applications. The synthesis tool genetic algo-rithm for low-power synthesis (GALOPS) uses a genetic algorithm to apply power-reducing transformations to high-level signal-pro-cessing designs, producing designs that satisfy power requirements as well as timing and area constraints. GALOPS uses problem-spe-cific genetic
Zixing Cai; Zhihong Peng
2002-01-01
In this paper, path planning of cooperative multi-mobile robot systems, an example of multi-agent systems, is discussed with the proposal of a novel Cooperative Coevolutionary Adaptive Genetic Algorithm (CCAGA). At the same time, for such genetic algorithms based path planning, a novel fixed-length decimal encoding mechanism for paths of each mobile robot is also proposed. Such cooperative coevolutionary adaptive genetic
Cláudio M. N. A. Pereira; Celso M. F. Lapa
2003-01-01
In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been
Hirsh, Haym
optimizer. Moreover, the results suggest that the modification makes the genetic algorithm less sensitiveIn Proceedings of the 1997 International Conference on Genetic Algorithms Using CaseBased Learning to Improve GeneticAlgorithmBased Design Optimization Khaled Rasheed Computer Science Department Rutgers
Daniel S. Weile; Eric Michielssen
2000-01-01
The application of domain decomposition genetic algorithms to the design of frequency selective surfaces (FSSs) is discussed. The analysis of FSS screens is briefly reviewed, along with a method of accelerating their characterization with a rational Krylov model order reduction technique. Using this technique, a hybrid genetic algorithm (which is a type of domain decomposition genetic algorithm that incorporates a
A Moving Target Environment for Computer Configurations Using Genetic Algorithms
Crouse, Michael; Fulp, Errin W.
2011-10-31
Moving Target (MT) environments for computer systems provide security through diversity by changing various system properties that are explicitly defined in the computer configuration. Temporal diversity can be achieved by making periodic configuration changes; however in an infrastructure of multiple similarly purposed computers diversity must also be spatial, ensuring multiple computers do not simultaneously share the same configuration and potential vulnerabilities. Given the number of possible changes and their potential interdependencies discovering computer configurations that are secure, functional, and diverse is challenging. This paper describes how a Genetic Algorithm (GA) can be employed to find temporally and spatially diverse secure computer configurations. In the proposed approach a computer configuration is modeled as a chromosome, where an individual configuration setting is a trait or allele. The GA operates by combining multiple chromosomes (configurations) which are tested for feasibility and ranked based on performance which will be measured as resistance to attack. The result of successive iterations of the GA are secure configurations that are diverse due to the crossover and mutation processes. Simulations results will demonstrate this approach can provide at MT environment for a large infrastructure of similarly purposed computers by discovering temporally and spatially diverse secure configurations.
Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics
Hofler, Alicia [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Terzic, Balsa [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States) and Old Dominion University, Norfolk, VA (United States); Kramer, Matthew [University of California, Berkeley, CA (United States); Zvezdin, Anton [Stony Brook University, Stony Brook, NY (United States); Morozov, Vasiliy [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Roblin, Yves [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Lin, Fanglei [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Jarvis, Colin [Macalester College, Saint Paul, MN (United States)
2013-01-01
The genetic algorithm (GA) is a relatively new technique that implements the principles nature uses in biological evolution in order to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing CEBAF facility, the proposed MEIC at Jefferson Lab, and a radio frequency (RF) gun based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, including a newly devised enhancement, which leads to improved convergence to the optimum and make recommendations for future GA developments and accelerator applications.
Improved satellite image compression and reconstruction via genetic algorithms
NASA Astrophysics Data System (ADS)
Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary
2008-10-01
A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.
Feature selection using genetic algorithms for fetal heart rate analysis.
Xu, Liang; Redman, Christopher W G; Payne, Stephen J; Georgieva, Antoniya
2014-07-01
The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. PMID:24854596
Impact load identification of composite structure using genetic algorithms
NASA Astrophysics Data System (ADS)
Yan, Gang; Zhou, Li
2009-01-01
For structural health monitoring of composite structure, it is important to quickly and accurately identify the impact load whenever an impact event occurs. This paper proposes a genetic algorithms (GA)-based approach for impact load identification, which can identify the impact location and reconstruct the impact force history simultaneously. In this study, impact load is represented by a set of parameters, thus the impact load identification problem in both space (impact location) and time (impact force history) domains is transformed to a parameter identification problem. A forward model characterizes the dynamic response of the structure subject to a known impact force is incorporated in the identification procedure. By minimizing the difference between the analytical responses given by the forward model and the measured ones, GA adaptively identify the impact location and force history with its global search capability. This new impact identification approach is applied to a stiffened composite panel. The stiffened composite panel is modeled as an equivalent laminate with varying properties and the forward response is obtained by using an assumed modes approach. To improve the computational efficiency, micro-GA (?GA) is employed to perform the identification task. Numerical simulation studies are conducted to demonstrate the effectiveness and applicability of the proposed method.
Programmable genetic algorithm IP core for sensing and surveillance applications
NASA Astrophysics Data System (ADS)
Katkoori, Srinivas; Fernando, Pradeep; Sankaran, Hariharan; Stoica, Adrian; Keymeulen, Didier; Zebulum, Ricardo
2009-05-01
Real-time evolvable systems are possible with a hardware implementation of Genetic Algorithms (GA). We report the design of an IP core that implements a general purpose GA engine which has been successfully synthesized and verified on a Xilinx Virtex II Pro FPGA Device (XC2VP30). The placed and routed IP core has an area utilization of only 13% and clock speed of 50MHz. The GA core can be customized in terms of the population size, number of generations, cross-over and mutation rates, and the random number generator seed. The GA engine can be tailored to a given application by interfacing with the application specific fitness evaluation module as well as the required storage memory (to store the current and new populations). The core is soft in nature i.e., a gate-level netlist is provided which can be readily integrated with the user's system. The GA IP core can be readily used in FPGA based platforms for space and military applications (for e.g., surveillance, target tracking). The main advantages of the IP core are its programmability, small footprint, and low power consumption. Examples of concept systems in sensing and surveillance domains will be presented.
Aerodynamics Design and Genetic Algorithms for Optimization of Airship Bodies
NASA Astrophysics Data System (ADS)
Nejati, Vahid; Matsuuchi, Kazuo
A special and effective aerodynamics calculation method has been applied for the flow field around a body of revolution to find the drag coefficient for a wide range of Reynolds numbers. The body profile is described by a first order continuous axial singularity distribution. The solution of the direct problem then gives the radius and inviscid velocity distribution. Viscous effects are considered by means of an integral boundary layer procedure, and for determination of the transition location the forced transition criterion is applied. By avoiding those profiles, which result in the separation of the boundary layer, the drag can be calculated at the end of the body by using Young's formula. In this study, a powerful optimization procedure known as a Genetic Algorithms (GA) is used for the first time in the shape optimization of airship hulls. GA represents a particular artificial intelligence technique for large spaces, striking a remarkable balance between exploration and exploitation of search space. This method could reach to minimum objective function through a better path, and also could minimize the drag coefficient faster for different Reynolds number regimes. It was found that GA is a powerful method for such multi-dimensional, multi-modal and nonlinear objective function.
Experimental optimization of protein refolding with a genetic algorithm
Anselment, Bernd; Baerend, Danae; Mey, Elisabeth; Buchner, Johannes; Weuster-Botz, Dirk; Haslbeck, Martin
2010-01-01
Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74–100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes. PMID:20799347
Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling
Not Available
2007-05-01
Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.
Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework
Alicia Hofler, Pavel Evtushenko, Frank Marhauser
2009-09-01
Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.
Topology-changing shape optimization with the genetic algorithm
NASA Astrophysics Data System (ADS)
Lamberson, Steven E., Jr.
The goal is to take a traditional shape optimization problem statement and modify it slightly to allow for prescribed changes in topology. This modification enables greater flexibility in the choice of parameters for the topology optimization problem, while improving the direct physical relevance of the results. This modification involves changing the optimization problem statement from a nonlinear programming problem into a form of mixed-discrete nonlinear programing problem. The present work demonstrates one possible way of using the Genetic Algorithm (GA) to solve such a problem, including the use of "masking bits" and a new modification to the bit-string affinity (BSA) termination criterion specifically designed for problems with "masking bits." A simple ten-bar truss problem proves the utility of the modified BSA for this type of problem. A more complicated two dimensional bracket problem is solved using both the proposed approach and a more traditional topology optimization approach (Solid Isotropic Microstructure with Penalization or SIMP) to enable comparison. The proposed approach is able to solve problems with both local and global constraints, which is something traditional methods cannot do. The proposed approach has a significantly higher computational burden --- on the order of 100 times larger than SIMP, although the proposed approach is able to offset this with parallel computing.
Innovative applications of genetic algorithms to problems in accelerator physics
NASA Astrophysics Data System (ADS)
Hofler, Alicia; Terzi?, Balša; Kramer, Matthew; Zvezdin, Anton; Morozov, Vasiliy; Roblin, Yves; Lin, Fanglei; Jarvis, Colin
2013-01-01
The genetic algorithm (GA) is a powerful technique that implements the principles nature uses in biological evolution to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing Continuous Electron Beam Accelerator Facility nuclear physics machine, the proposed Medium-energy Electron-Ion Collider at Jefferson Lab, and a radio frequency gun-based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, include a newly devised enhancement which leads to improved convergence to the optimum, and make recommendations for future GA developments and accelerator applications.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500