Genetic Algorithms, Floating Point Numbers and Applications
NASA Astrophysics Data System (ADS)
Hardy, Yorick; Steeb, Willi-Hans; Stoop, Ruedi
The core in most genetic algorithms is the bitwise manipulations of bit strings. We show that one can directly manipulate the bits in floating point numbers. This means the main bitwise operations in genetic algorithm mutations and crossings are directly done inside the floating point number. Thus the interval under consideration does not need to be known in advance. For applications, we consider the roots of polynomials and finding solutions of linear equations.
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
The Applications of Genetic Algorithms in Medicine
Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin
2015-01-01
A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060
The Applications of Genetic Algorithms in Medicine.
Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin
2015-11-01
A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].
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 genetic algorithm to steganalysis
NASA Astrophysics Data System (ADS)
Knapik, Timothy; Lo, Ephraim; Marsh, John A.
2006-05-01
We present a novel application of genetic algorithm (GA) to optimal feature set selection in supervised learning using support vector machine (SVM) for steganalysis. Steganalysis attempts to determine whether a cover object (in our case an image file) contains hidden information. This is a bivariate classification problem: the image either does or does not contain hidden data. Our SVM classifier uses a training set of images with known classification to "learn" how to classify images with unknown classification. The SVM uses a feature set, essentially a set of statistical quantities extracted from the image. The performance of the SVM classifier is heavily dependent on the feature set used. Too many features not only increase computation time but decrease performance, and too few features do not provide enough information for accurate classification. Our steganalysis technique uses entropic features that yield up to 240 features per image. The selection of an optimum feature set is a problem that lends itself well to genetic algorithm optimization. We describe this technique in detail and present a "GA optimized" feature set of 48 features that, for our application, optimizes the tradeoff between computation time and classification accuracy.
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.
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
The multi-niche crowding genetic algorithm: Analysis and applications
Cedeno, Walter
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.
Software For Genetic Algorithms
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steve E.
1992-01-01
SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.
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.
Grefenstette, J.J.
1994-12-31
Genetic algorithms solve problems by using principles inspired by natural population genetics: They maintain a population of knowledge structures that represent candidate solutions, and then let that population evolve over time through competition and controlled variation. GAs are being applied to a wide range of optimization and learning problems in many domains.
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.
Innovative applications of genetic algorithms to problems in accelerator physics
NASA Astrophysics Data System (ADS)
Hofler, Alicia; Terzić, 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.
Constraint genetic algorithm and its application in sintering proportioning
NASA Astrophysics Data System (ADS)
Wu, Tiebin; Liu, Yunlian; Tang, Wenyan; Li, Xinjun; Yu, Yi
2017-09-01
This paper puts forward a method for constrained optimization problems based on self-adaptive penalty function and improved genetic algorithm. In order to improve the speed of convergence and avoid premature convergence, a method based on good-point set theory has been proposed. By using good point set method for generating initial population, the initial population is uniformly distributed in the solution space. This paper Designs an elite reverse learning strategy, and proposes a mechanism to automatically adjust the crossover probability according to the individual advantages and disadvantages. The tests indicate that the proposed constrained genetic algorithm is efficient and feasible.
Genetic Algorithms and Their Application to the Protein Folding Problem
1993-12-01
mutation, genetic algorithms simulate the Darwin theory of survival of the fittest. The search space is represented by a population of strings upon which... Darwin theory of survival of the fittest by representing the search space as a population of strings upon which genetic operators act to create new...34 International Conference on Tools for Artificial Intelligence, IEEE-TAI 90, 322-7. Cartwright , H. M. & Mott, G. F. (1991). "Looking A:;und: Using Clues
Applications of hybrid genetic algorithms in seismic tomography
NASA Astrophysics Data System (ADS)
Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet T.; Papazachos, Constantinos
2011-11-01
Almost all earth sciences inverse problems are nonlinear and involve a large number of unknown parameters, making the application of analytical inversion methods quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem equations, adopting an iterative procedure which typically employs partial derivatives in order to optimize the starting (initial) model by minimizing a misfit (penalty) function. Unfortunately, especially for highly non-linear cases, the final model strongly depends on the initial model, hence it is prone to solution-entrapment in local minima of the misfit function, while the derivative calculation is often computationally inefficient and creates instabilities when numerical approximations are used. An alternative is to employ global techniques which do not rely on partial derivatives, are independent of the misfit form and are computationally robust. Such methods employ pseudo-randomly generated models (sampling an appropriately selected section of the model space) which are assessed in terms of their data-fit. A typical example is the class of methods known as genetic algorithms (GA), which achieves the aforementioned approximation through model representation and manipulations, and has attracted the attention of the earth sciences community during the last decade, with several applications already presented for several geophysical problems. In this paper, we examine the efficiency of the combination of the typical regularized least-squares and genetic methods for a typical seismic tomography problem. The proposed approach combines a local (LOM) and a global (GOM) optimization method, in an attempt to overcome the limitations of each individual approach, such as local minima and slow convergence, respectively. The potential of both optimization methods is tested and compared, both independently and jointly, using the several test models and synthetic refraction travel-time date sets
Application of genetic algorithm to hexagon-based motion estimation.
Kung, Chih-Ming; Cheng, Wan-Shu; Jeng, Jyh-Horng
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.
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
Application of a Genetic Algorithm to Optimize Quality Assurance in Software Development
1993-09-01
NAVAL POSTGRADUATE SCHOOL Monterey, California AD-A273 193 THESIS APPLICATION OF A GENETIC ALGORITHM TO OPTIMIZE QUALITY ASSURANCE IN SOFTWARE ...Procurement Instrument Identification Number I(if applicable) Address (ity, state, and ZIP code) 10 Source of Funding Numbers Program Element No Project No...Task No Work Unit Accession No II Title (include security classification) APPLICATION OF A GENETIC ALGORITHM TO OPTIMIZE QUALITY ASSURANCE IN SOFTWARE
Genetic algorithms and their applications in accelerator physics
Hofler, Alicia S.
2013-12-01
Multi-objective optimization techniques are widely used in an extremely broad range of fields. Genetic optimization for multi-objective optimization was introduced in the accelerator community in relatively recent times and quickly spread becoming a fundamental tool in multi-dimensional optimization problems. This discussion introduces the basics of the technique and reviews applications in accelerator problems.
Application of Genetic Algorithms in Nonlinear Heat Conduction Problems
Khan, Waqar A.
2014-01-01
Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry. PMID:24695517
Application of genetic algorithms in nonlinear heat conduction problems.
Kadri, Muhammad Bilal; Khan, Waqar A
2014-01-01
Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry.
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.
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.
NASA Astrophysics Data System (ADS)
Narwadi, Teguh; Subiyanto
2017-03-01
The Travelling Salesman Problem (TSP) is one of the best known NP-hard problems, which means that no exact algorithm to solve it in polynomial time. This paper present a new variant application genetic algorithm approach with a local search technique has been developed to solve the TSP. For the local search technique, an iterative hill climbing method has been used. The system is implemented on the Android OS because android is now widely used around the world and it is mobile system. It is also integrated with Google API that can to get the geographical location and the distance of the cities, and displays the route. Therefore, we do some experimentation to test the behavior of the application. To test the effectiveness of the application of hybrid genetic algorithm (HGA) is compare with the application of simple GA in 5 sample from the cities in Central Java, Indonesia with different numbers of cities. According to the experiment results obtained that in the average solution HGA shows in 5 tests out of 5 (100%) is better than simple GA. The results have shown that the hybrid genetic algorithm outperforms the genetic algorithm especially in the case with the problem higher complexity.
Credit card fraud detection: An application of the gene expression messy genetic algorithm
Kargupta, H.; Gattiker, J.R.; Buescher, K.
1996-05-01
This paper describes an application of the recently introduced gene expression messy genetic algorithm (GEMGA) (Kargupta, 1996) for detecting fraudulent transactions of credit cards. It also explains the fundamental concepts underlying the GEMGA in the light of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) (Kargupta, 1995) framework.
Optimization of meander line antennas for RFID applications by using genetic algorithm
NASA Astrophysics Data System (ADS)
Bucuci, Stefania C.; Anchidin, Liliana; Dumitrascu, Ana; Danisor, Alin; Berescu, Serban; Tamas, Razvan D.
2015-02-01
In this paper, we propose an approach of optimization of meander line antennas by using genetic algorithm. Such antennas are used in RFID applications. As opposed to other approaches for meander antennas, we propose the use of only two optimization objectives, i.e. gain and size. As an example, we have optimized a single meander dipole antenna, resonating at 869 MHz.
Nonyane, Bareng AS; Foulkes, Andrea S
2008-01-01
Background Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. Methods and Results In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. Conclusion Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation. PMID:19014573
Nonyane, Bareng A S; Foulkes, Andrea S
2008-11-14
Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation.
NASA Astrophysics Data System (ADS)
Yang, Xiaohua; Yang, Zhifeng; Yin, Xinan; Li, Jianqiang
2008-10-01
In order to reduce the computational amount and improve computational precision for nonlinear optimizations and pollution source identification in convection-diffusion equation, a new algorithm, chaos gray-coded genetic algorithm (CGGA) is proposed, in which initial population are generated by chaos mapping, and new chaos mutation and Hooke-Jeeves evolution operation are used. With the shrinking of searching range, CGGA gradually directs to an optimal result with the excellent individuals obtained by gray-coded genetic algorithm. Its convergence is analyzed. It is very efficient in maintaining the population diversity during the evolution process of gray-coded genetic algorithm. This new algorithm overcomes any Hamming-cliff phenomena existing in other encoding genetic algorithm. Its efficiency is verified by application of 20 nonlinear test functions of 1-20 variables compared with standard binary-coded genetic algorithm and improved genetic algorithm. The position and intensity of pollution source are well found by CGGA. Compared with Gray-coded hybrid-accelerated genetic algorithm and pure random search algorithm, CGGA has rapider convergent speed and higher calculation precision.
Genetic algorithms as discovery programs
Hilliard, M.R.; Liepins, G.
1986-01-01
Genetic algorithms are mathematical counterparts to natural selection and gene recombination. As such, they have provided one of the few significant breakthroughs in machine learning. Used with appropriate reward functions and apportionment of credit, they have been successfully applied to gas pipeline operation, x-ray registration and mathematical optimization problems. This paper discusses the basics of genetic algorithms, describes a few successes, and reports on current progress at Oak Ridge National Laboratory in applications to set covering and simulated robots.
Sankaran, Ramanan; Angel, Jordan; Brown, W. Michael
2015-04-08
The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on the performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.
Sankaran, Ramanan; Angel, Jordan; Brown, W. Michael
2015-04-08
The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on themore » performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.« less
Problem solving with genetic algorithms and Splicer
NASA Technical Reports Server (NTRS)
Bayer, Steven E.; Wang, Lui
1991-01-01
Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.
Problem solving with genetic algorithms and Splicer
NASA Technical Reports Server (NTRS)
Bayer, Steven E.; Wang, Lui
1991-01-01
Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.
NASA Astrophysics Data System (ADS)
Jude Hemanth, Duraisamy; Umamaheswari, Subramaniyan; Popescu, Daniela Elena; Naaji, Antoanela
2016-01-01
Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.
Where genetic algorithms excel.
Baum, E B; Boneh, D; Garrett, C
2001-01-01
We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve "implicit parallelism" in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.
NASA Astrophysics Data System (ADS)
Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.
2013-03-01
is 93.02%, whereas units without landslide occurrence are predicted with an accuracy of 81.13%. To sum up, the verification shows satisfactory agreement with an accuracy of 86.46% between the susceptibility map and the landslide locations. In the landslide susceptibility assessment, ten new slopes were predicted to show potential for failure, which can be confirmed by the engineering geological conditions of these slopes. It was also observed that some disadvantages could be overcome in the application of the neural networks with back propagation, for example, the low convergence rate and local minimum, after the network was optimized using genetic algorithms. To conclude, neural networks with back propagation that are optimized by genetic algorithms are an effective method to predict landslide susceptibility with high accuracy.
NASA Astrophysics Data System (ADS)
Zheng, Q.
2011-12-01
On the application of the genetic algorithm to the predictability problems involving "on-off" switches ZHENG Qin(1,2), DAI Yi(1), ZHANG Lu(1)and LU Xiaoqing(1) (1)Institute of Science, PLA University of Science and Technology, Nanjing 211101, China; (2)State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Abstract The lower bound of maximum predictable time can be formulated into a constrained nonlinear optimization problem, and the traditional solution to this problem is the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named as the ADJ-CNOP, hereinafter. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the "on-off" switch problem, which affects tremendously the effectiveness of the conventional gradient descent algorithm based on the adjoint method. This paper attempts to apply a genetic algorithm (GA) to the CNOP method, named as the GA-CNOP, to solve the predictability problems involving the "on-off" switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results are taken as benchmarks and a series of comparisons between the ADJ-CNOP and the GA-CNOP are performed. It is revealed that the GA-CNOP can always figure out the accurate lower bound of maximum predictable time even in discontinuous cases, while the ADJ-CNOP, owing to the effect of "on-off" switches, often yields the incorrect lower bound of maximum predictable time. This would suggest that in non-smooth cases, using a GA to solve the predictability problems is more effective than using the conventional
Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease.
Stokes, Matthew E; Visweswaran, Shyam
2012-12-03
Identification of genetic variants that are associated with disease is an important goal in elucidating the genetic causes of diseases. The genetic patterns that are associated with common diseases are complex and may involve multiple interacting genetic variants. The Relief family of algorithms is a powerful tool for efficiently identifying genetic variants that are associated with disease, even if the variants have nonlinear interactions without significant main effects. Many variations of Relief have been developed over the past two decades and several of them have been applied to single nucleotide polymorphism (SNP) data. We developed a new spatially weighted variation of Relief called Sigmoid Weighted ReliefF Star (SWRF*), and applied it to synthetic SNP data. When compared to ReliefF and SURF*, which are two algorithms that have been applied to SNP data for identifying interactions, SWRF* had significantly greater power. Furthermore, we developed a framework called the Modular Relief Framework (MoRF) that can be used to develop novel variations of the Relief algorithm, and we used MoRF to develop the SWRF* algorithm. MoRF allows easy development of new Relief algorithms by specifying different interchangeable functions for the component terms. Using MORF, we developed a new Relief algorithm called SWRF* that had greater ability to identify interacting genetic variants in synthetic data compared to existing Relief algorithms.
Mazaheri Tehrani, Mostafa; Ehtiati, Ahmad; Sharifi Azghandi, Shadi
2017-04-01
The aim of this study was to find the optimum extrusion process conditions for texturized soybean meal as a meat analogue for food formulations using genetic algorithm. The defatted soybean meal was replaced with whole soybean meal at 10% and extruded in the temperature range of 150-200 °C, screw speed of 270-300 rpm and 20-25% feed moisture content based on the Box-Behnken design. The barrel temperature effect was markedly greater than those of the feed moisture content and screw speed on the product functional properties and appearance. Higher temperatures led to a higher rehydration capacity, water and oil absorption capacity, however, it had a negative effect on the product brightness. It was found that the extrusion at lower moisture content improved soy protein functionality. Genetic algorithm technique was applied to find the best process conditions. The optimized process conditions were found to be the temperature of 198.8 °C, screw speed of 291 rpm and feed moisture content of 20.2%. Overall, the whole soybean treatment was applicable to overcome the oil separation issue during extrusion and the process was optimized to produce texturized soy protein with the maximum attainable functionality.
Application of genetic algorithm for automatic recognition of partially occluded objects
NASA Astrophysics Data System (ADS)
Sadjadi, Firooz A.
1994-07-01
Automatic recognition of partially occluded objects that are sensed by imaging sensors is a challenging problem in image understanding (IU), automatic target recognition (ATR), and computer vision fields. In this paper I address this problem by using a genetic algorithm (GA) as part of a model-based recognition scheme. The partially occluded object segments are rotated, translated, and scaled. Then each transform parameter is encoded into a binary string and used in a genetic algorithm. The suggested transformation is then applied to the sensed segment and the resulting object is matched against a library of stored targets. The fitness criterion is a distance function that measures the similarity between the segmented object and the stored target models. The GA by performing the process of mutation, reproduction, and crossover suggests optimum transform parameter sets. The empirical results of the application of the approach on a set of real ladar data of military targets shows that correct recognition for up to 50% target occlusion is possible.
NASA Astrophysics Data System (ADS)
Sastry, Kumara Narasimha
2007-03-01
Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common
Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes.
Tumuluru, Jaya Shankar; McCulloch, Richard
2016-11-09
Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA), which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods.
Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes
Tumuluru, Jaya Shankar; McCulloch, Richard
2016-01-01
Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA), which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods. PMID:28231171
NASA Astrophysics Data System (ADS)
Hessburg, Thomas; Lee, Michael; Takagi, Hideyuki; Tomizuka, Masayoshi
1993-12-01
A method of tuning a fuzzy logic controller (FLC) by a genetic algorithm (GA) is proposed for lane following maneuvers in an automated highway system. The GA simultaneously determines the shape of membership functions, number of rules, and consequent parameters of the FLC. The GA approach operates on binary representations of FLCs and uses an expression for a fitness score to be maximized, which takes into account the tracking error, yaw rate error, lateral acceleration error, rate of lateral acceleration, front wheel steering angle, and rate of front wheel steering angle, to find an optimal controller. Apriori knowledge about both the physical application and FLCs is incorporated into the design method to increase the performance of the design method and the resulting controller. The controllers designed by this method are compared in simulation to a conventional PID controller, a frequency shaped linear quadratic controller, and previously designed FLCs tuned manually.
NASA Astrophysics Data System (ADS)
Johar, F. M.; Azmin, F. A.; Shibghatullah, A. S.; Suaidi, M. K.; Ahmad, B. H.; Abd Aziz, M. Z. A.; Salleh, S. N.; Shukor, M. Md
2014-04-01
Attenuation of GSM, GPS and personal communication signal leads to poor communication inside the building using regular shapes of energy saving glass coating. Thus, the transmission is very low. A brand new type of band pass frequency selective surface (FSS) for energy saving glass application is presented in this paper for one unit cell. Numerical Periodic Method of Moment approach according to a previous study has been applied to determine the new optimum design of one unit cell energy saving glass coating structure. Optimization technique based on the Genetic Algorithm (GA) is used to obtain an improved in return loss and transmission signal. The unit cell of FSS is designed and simulated using the CST Microwave Studio software at based on industrial, scientific and medical bands (ISM). A unique and irregular shape of an energy saving glass coating structure is obtained with lower return loss and improved transmission coefficient.
Application of genetic algorithms to the optimization design of electron optical system
NASA Astrophysics Data System (ADS)
Gu, Changxin; Wu, M. Q.; Shan, Liying; Lin, G.
2001-12-01
The application of Genetic Algorithms (GAs) to the optimization design method, such as Simplex method and Powell method etc, can determine the final optimum structure and electric parameters of an electron optical system from given electron optical properties, but it may be landed in the localization of optimum search process. The GAs is a novel direct search optimization method based on principles of natural selection and survival of the fittest from natural evolution. Through the reproduction, crossover, and mutation iterative process, GAs can search the global optimum result. We applied the GAs to optimize an electron emission system and an extended field lens (EFL) respectively. The optimal structure and corresponding electrical parameters with a criterion of minimum objective function value, crossover radius for electron emission system and spherical aberration coefficient for EFL, have been searched and presented in this paper. The GAs, as a direct search method and an adaptive search technique, has significant advantage in the optimization design of electron optical systems.
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 for DNA sequence assembly.
Parsons, R; Forrest, S; Burks, C
1993-01-01
This paper describes a genetic algorithm application to the DNA sequence assembly problem. The genetic algorithm uses a sorted order representation for representing the orderings of fragments. Two different fitness functions, both based on pairwise overlap strengths between fragments, are tested. The paper concludes that the genetic algorithm is a promising method for fragment assembly problems, achieving usable solutions quickly, but that the current fitness functions are flawed and that other representations might be more appropriate.
Treier, Katrin; Berg, Annette; Diederich, Patrick; Lang, Katharina; Osberghaus, Anna; Dismer, Florian; Hubbuch, Jürgen
2012-10-01
Compared to traditional strategies, application of high-throughput experiments combined with optimization methods can potentially speed up downstream process development and increase our understanding of processes. In contrast to the method of Design of Experiments in combination with response surface analysis (RSA), optimization approaches like genetic algorithms (GAs) can be applied to identify optimal parameter settings in multidimensional optimizations tasks. In this article the performance of a GA was investigated applying parameters applicable in high-throughput downstream process development. The influence of population size, the design of the initial generation and selection pressure on the optimization results was studied. To mimic typical experimental data, four mathematical functions were used for an in silico evaluation. The influence of GA parameters was minor on landscapes with only one optimum. On landscapes with several optima, parameters had a significant impact on GA performance and success in finding the global optimum. Premature convergence increased as the number of parameters and noise increased. RSA was shown to be comparable or superior for simple systems and low to moderate noise. For complex systems or high noise levels, RSA failed, while GA optimization represented a robust tool for process optimization. Finally, the effect of different objective functions is shown exemplarily for a refolding optimization of lysozyme. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
NASA Astrophysics Data System (ADS)
Başokur, Ahmet T.; Akca, Irfan
2011-08-01
A new target-oriented parameterization scheme, named the object-based model, is suggested to represent man-made or natural targets as regular shapes embedded in a two-dimensional resistivity background. The numerical values of the target parameters (size, depth, location and resistivity) are estimated in three steps consisting of conventional regularized inversion, exclusion of anomalous regions and delineation of target bodies. The method produces sharp edges and sharp variation in intrinsic resistivity between the targets and background. The number of target objects is decided by the visual inspection of the 2D resistivity section derived from the application of a conventional cell-based regularized inversion. The 2D background is also extracted from the same section. A genetic algorithm approach is used at the final stage to test a large number of distinct models. Each test model consists of the same number of objects buried in the 2D background. The size, depth, location and resistivity of the targets are estimated from a class of models generated by the application of biological rules. The derived images of buried bodies have sharp edges and can then be understood by engineers and archeologists. However, if the hypothesis about the 'conceptual model' is very different from the geometry of the subsurface, the proposed approach will not be able to produce satisfactory results.
Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS
NASA Astrophysics Data System (ADS)
Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin
Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.
Xu, Feng; Wan, Mingxi; Lu, Mingzhu
2008-10-01
The genetic optimal algorithm and sound field calculation approach for the spherical-section phased array are presented in this paper. The in-house manufactured 256-element phased array focused ultrasound surgery system is briefly described. The on-axis single focus and off-axis single focus are simulated along with the axis-symmetric six-focus patter and the axis-asymmetric four-focus pattern using a 256-element phased array and the genetic optimal algorithm and sound field calculation approach. The experimental results of the described 256-element phased array focused ultrasound surgery system acting on organic glass and phantom are also analyzed. The results of the simulations and experiments confirm the applicability of the genetic algorithm and field calculation approaches in accurately steering three dimensional foci and focus.
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.
1991-01-01
A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three
Incremental genetic K-means algorithm and its application in gene expression data analysis.
Lu, Yi; Lu, Shiyong; Fotouhi, Farshad; Deng, Youping; Brown, Susan J
2004-10-28
In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm. Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
Guo, Y C; Wang, H; Zhang, B L
2015-10-02
This study proposes use of the DNA genetic artificial fish swarm constant modulus blind equalization algorithm (DNA-G-AFS-CMBEA) to overcome the local convergence of the CMBEA. In this proposed algorithm, after the fusion of the fast convergence of the AFS algorithm and the global search capability of the DNA-G algorithm to drastically optimize the position vector of the artificial fish, the global optimal position vector is obtained and used as the initial optimal weight vector of the CMBEA. The result of application of this improved method in medical image processing demonstrates that the proposed algorithm outperforms the CMBEA and the AFS-CMBEA in removing the noise in a medical image and improving the peak signal to noise ratio.
NASA Astrophysics Data System (ADS)
Keane, A. J.
1995-08-01
In the majority of aerospace structures, vibration transmission problems are dealt with by the application of heavy, viscoelastic damping materials. More recently, interest has focussed on using active vibration control methods to reduce noise transmission. This paper examines a third, and potentially much cheaper method: that of redesigning the load bearing structure so that it has intrinsic, passive noise filtration characteristics. It shows that very significant, broadband noise isolation characteristics (of around 60 dB over a 100 Hz band) can be achieved without compromising other aspects of the design. Here, the genetic algorithm (GA), which is one of a number of recently developed evolutionary computing methods, is employed to produce the desired designs. The problem is set up as one in multi-dimensional optimization where the geometric parameters of the design are the free variables and the band averaged noise transmission the objective function. The problem is then constrained by the need to maintain structural integrity. Set out in this way, even a simple structural problem has many tens of variables; a real structure would have many hundreds. Consequently, the optimization domain is very time consuming for traditional methods to deal with. This is where modern evolutionary techniques become so useful: their convergence rates are typically less rapidly worsened by increases in the number of variables than those of more traditional methods. Even so, they must be used with some care to gain the best results.
Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay
2009-06-03
Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.
Yu, Y; Schell, M C; Zhang, J B
1997-11-01
Treatment planning for stereotactic radiosurgery and fractionated radiotherapy is currently a labor intensive, operator-dependent process. Many degrees of freedom exist to make rigorous optimization intractable except by computationally intelligent techniques. The quality of a given plan is determined by an aggregate of clinical objectives, most of which are subject to competing tradeoffs. In this work, we present an autonomous scheme that couples decision theoretic guidance with a genetic algorithm for optimization. Ordinal ranking among a population of viable treatment plans is based on a generalized distance metric, which promotes a decreasing hyperfrontier of the efficient solution set. The solution set is driven toward efficiency by the genetic algorithm, which uses the tournament selection mechanism based on the ordinal ranking. Goals and satisficing conditions can be defined to signal the ultimate and the minimum achievement levels in a given objective. A conventionally challenging case in radiosurgery was used to demonstrate the practical utility and the problem-solving power of the decision theoretic genetic algorithm. Treatment plans with one isocenter and four isocenters were derived under the autonomous scheme and compared to the actual treatment plan manually optimized by the expert planner. Quality assessment based on dose-volume histograms and normal tissue complication probabilities suggested that computational optimization could be driven to offer varying degrees of dosimetric improvement over a human-guided optimization effort. Furthermore, it was possible to achieve a high degree of isodose conformity to the target volume in computational optimization by increasing the degree of freedom in the treatment parameters. The time taken to derive an efficient planning solution was comparable and usually shorter than in the manual planning process, and can be scaled down almost linearly with the number of processors. Overall, the autonomous genetic
Research and application of genetic algorithm in path planning of logistics distribution vehicle
NASA Astrophysics Data System (ADS)
Wang, Yong; Zhou, Heng; Wang, Ying
2017-08-01
The core of the logistics distribution system is the vehicle routing planning, research path planning problem, provide a better solution has become an important issue. In order to provide the decision support for logistics and distribution operations, this paper studies the problem of vehicle routing with capacity constraints (CVRP). By establishing a mathematical model, the genetic algorithm is used to plan the path of the logistics vehicle to meet the minimum logistics and transportation costs.
Optimization of fuels from waste composition with application of genetic algorithm.
Małgorzata, Wzorek
2014-05-01
The objective of this article is to elaborate a method to optimize the composition of the fuels from sewage sludge (PBS fuel - fuel based on sewage sludge and coal slime, PBM fuel - fuel based on sewage sludge and meat and bone meal, PBT fuel - fuel based on sewage sludge and sawdust). As a tool for an optimization procedure, the use of a genetic algorithm is proposed. The optimization task involves the maximization of mass fraction of sewage sludge in a fuel developed on the basis of quality-based criteria for the use as an alternative fuel used by the cement industry. The selection criteria of fuels composition concerned such parameters as: calorific value, content of chlorine, sulphur and heavy metals. Mathematical descriptions of fuel compositions and general forms of the genetic algorithm, as well as the obtained optimization results are presented. The results of this study indicate that the proposed genetic algorithm offers an optimization tool, which could be useful in the determination of the composition of fuels that are produced from waste.
An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail
NASA Astrophysics Data System (ADS)
Kaplan, Selçuk
2017-04-01
The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.
Fusion of qualitative bond graph and genetic algorithms: a fault diagnosis application.
Lo, C H; Wong, Y K; Rad, A B; Chow, K M
2002-10-01
In this paper, the problem of fault diagnosis via integration of genetic algorithms (GA's) and qualitative bond graphs (QBG's) is addressed. We suggest that GA's can be used to search for possible fault components among a system of qualitative equations. The QBG is adopted as the modeling scheme to generate a set of qualitative equations. The qualitative bond graph provides a unified approach for modeling engineering systems, in particular, mechatronic systems. In order to demonstrate the performance of the proposed algorithm, we have tested the proposed algorithm on an in-house designed and built floating disc experimental setup. Results from fault diagnosis in the floating disc system are presented and discussed. Additional measurements will be required to localize the fault when more than one fault candidate is inferred. Fault diagnosis is activated by a fault detection mechanism when a discrepancy between measured abnormal behavior and predicted system behavior is observed. The fault detection mechanism is not presented here.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
Deng, Haishan; Xie, Shaofei; Xiang, Bingren; Zhan, Ying; Li, Wei; Li, Xiaohua; Jiang, Caiyun; Wu, Xiaohong; Liu, Dan
2014-01-01
Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e., S/N and peak shape) and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses.
NASA Astrophysics Data System (ADS)
Sun, Xun; Zhang, Weiguo; Yin, Wei; Li, Aijun
2006-11-01
As enlarging of the flight envelop, the aerodynamic derivative of the airplane varies enormous. The gain scheduling method is usually used to deal with it. But the workload is enormously and the stability is difficulty to be assured. To solve the above problem, a large envelope wavelet neural network gain scheduling flight control law design method based on genetic algorithm is presented in this paper. Wavelet has good time accuracy in high frequency-domain and the good frequency accuracy in low frequency-domain. Neural network has the self-learning character. In this method, wavelet function instead of Sigmoid function as the excitation function. So the two merits are merged and the high nonlinear function approximation capability could be achieved. In order to obtain higher accuracy and faster speed, genetic algorithm is used to optimize the parameters of the wavelet neural network. This method is used in design the large envelope gain scheduling flight control law. This simulation results show that good control capability could be achieved in large envelope and the system is still stable when modeling error is 20%. In the situation of 20% modeling error, the maximum overshoot is only 12m and it is 35% of the maximum overshoot using normal method.
A genetic engineering approach to genetic algorithms.
Gero, J S; Kazakov, V
2001-01-01
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.
NASA Astrophysics Data System (ADS)
Eladj, Said; bansir, fateh; ouadfeul, sid Ali
2016-04-01
The application of genetic algorithm starts with an initial population of chromosomes representing a "model space". Chromosome chains are preferentially Reproduced based on Their fitness Compared to the total population. However, a good chromosome has a Greater opportunity to Produce offspring Compared To other chromosomes in the population. The advantage of the combination HGA / SAA is the use of a global search approach on a large population of local maxima to Improve Significantly the performance of the method. To define the parameters of the Hybrid Genetic Algorithm Steepest Ascent Auto Statics (HGA / SAA) job, we Evaluated by testing in the first stage of "Steepest Ascent," the optimal parameters related to the data used. 1- The number of iterations "Number of hill climbing iteration" is equal to 40 iterations. This parameter defines the participation of the algorithm "SA", in this hybrid approach. 2- The minimum eigenvalue for SA '= 0.8. This is linked to the quality of data and S / N ratio. To find an implementation performance of hybrid genetic algorithms in the inversion for estimating of the residual static corrections, tests Were Performed to determine the number of generation of HGA / SAA. Using the values of residual static corrections already calculated by the Approaches "SAA and CSAA" learning has Proved very effective in the building of the cross-correlation table. To determine the optimal number of generation, we Conducted a series of tests ranging from [10 to 200] generations. The application on real seismic data in southern Algeria allowed us to judge the performance and capacity of the inversion with this hybrid method "HGA / SAA". This experience Clarified the influence of the corrections quality estimated from "SAA / CSAA" and the optimum number of generation hybrid genetic algorithm "HGA" required to have a satisfactory performance. Twenty (20) generations Were enough to Improve continuity and resolution of seismic horizons. This Will allow
NASA Astrophysics Data System (ADS)
Hooper, James; Ismail, Arif; Giorgi, Javier B.; Woo, Tom K.
2010-06-01
A genetic algorithm (GA)-inspired method to effectively map out low-energy configurations of doped metal oxide materials is presented. Specialized mating and mutation operations that do not alter the identity of the parent metal oxide have been incorporated to efficiently sample the metal dopant and oxygen vacancy sites. The search algorithms have been tested on lanthanide-doped ceria (L=Sm,Gd,Lu) with various dopant concentrations. Using both classical and first-principles density-functional-theory (DFT) potentials, we have shown the methodology reproduces the results of recent systematic searches of doped ceria at low concentrations (3.2% L2O3 ) and identifies low-energy structures of concentrated samarium-doped ceria (3.8% and 6.6% L2O3 ) which relate to the experimental and theoretical findings published thus far. We introduce a tandem classical/DFT GA algorithm in which an inexpensive classical potential is first used to generate a fit gene pool of structures to enhance the overall efficiency of the computationally demanding DFT-based GA search.
Peng, Tingting; Jiang, Bo; Guo, Jiangfeng; Lu, Hongfei; Du, Liqun
2015-01-01
Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40°C with 5°C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25°C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15°C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy. PMID:26154163
Pi, Erxu; Qu, Liqun; Tang, Xi; Peng, Tingting; Jiang, Bo; Guo, Jiangfeng; Lu, Hongfei; Du, Liqun
2015-01-01
Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40 °C with 5 °C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25 °C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15 °C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.
NASA Technical Reports Server (NTRS)
Sanyal, Soumya; Jain, Amit; Das, Sajal K.; Biswas, Rupak
2003-01-01
In this paper, we propose a distributed approach for mapping a single large application to a heterogeneous grid environment. To minimize the execution time of the parallel application, we distribute the mapping overhead to the available nodes of the grid. This approach not only provides a fast mapping of tasks to resources but is also scalable. We adopt a hierarchical grid model and accomplish the job of mapping tasks to this topology using a scheduler tree. Results show that our three-phase algorithm provides high quality mappings, and is fast and scalable.
A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...
A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...
Cost-sensitive case-based reasoning using a genetic algorithm: application to medical diagnosis.
Park, Yoon-Joo; Chun, Se-Hak; Kim, Byung-Chun
2011-02-01
The paper studies the new learning technique called cost-sensitive case-based reasoning (CSCBR) incorporating unequal misclassification cost into CBR model. Conventional CBR is now considered as a suitable technique for diagnosis, prognosis and prescription in medicine. However it lacks the ability to reflect asymmetric misclassification and often assumes that the cost of a positive diagnosis (an illness) as a negative one (no illness) is the same with that of the opposite situation. Thus, the objective of this research is to overcome the limitation of conventional CBR and encourage applying CBR to many real world medical cases associated with costs of asymmetric misclassification errors. The main idea involves adjusting the optimal cut-off classification point for classifying the absence or presence of diseases and the cut-off distance point for selecting optimal neighbors within search spaces based on similarity distribution. These steps are dynamically adapted to new target cases using a genetic algorithm. We apply this proposed method to five real medical datasets and compare the results with two other cost-sensitive learning methods-C5.0 and CART. Our finding shows that the total misclassification cost of CSCBR is lower than other cost-sensitive methods in many cases. Even though the genetic algorithm has limitations in terms of unstable results and over-fitting training data, CSCBR results with GA are better overall than those of other methods. Also the paired t-test results indicate that the total misclassification cost of CSCBR is significantly less than C5.0 and CART for several datasets. We have proposed a new CBR method called cost-sensitive case-based reasoning (CSCBR) that can incorporate unequal misclassification costs into CBR and optimize the number of neighbors dynamically using a genetic algorithm. It is meaningful not only for introducing the concept of cost-sensitive learning to CBR, but also for encouraging the use of CBR in the medical area
Calibration of neural networks using genetic algorithms, with application to optimal path planning
NASA Technical Reports Server (NTRS)
Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel
1987-01-01
Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.
NASA Astrophysics Data System (ADS)
Zhu, Lianqing; Chen, Yunfang; Chen, Qingshan; Meng, Hao
2010-12-01
According to minimum zone condition, a method for evaluating the profile error of Archimedes helicoid surface based on Genetic Algorithm (GA) is proposed. The mathematic model of the surface is provided and the unknown parameters in the equation of surface are acquired through least square method. Principle of GA is explained. Then, the profile error of Archimedes Helicoid surface is obtained through GA optimization method. To validate the proposed method, the profile error of an Archimedes helicoid surface, Archimedes Cylindrical worm (ZA worm) surface, is evaluated. The results show that the proposed method is capable of correctly evaluating the profile error of Archimedes helicoid surface and satisfy the evaluation standard of the Minimum Zone Method. It can be applied to deal with the measured data of profile error of complex surface obtained by three coordinate measurement machines (CMM).
NASA Astrophysics Data System (ADS)
Zhu, Lianqing; Chen, Yunfang; Chen, Qingshan; Meng, Hao
2011-05-01
According to minimum zone condition, a method for evaluating the profile error of Archimedes helicoid surface based on Genetic Algorithm (GA) is proposed. The mathematic model of the surface is provided and the unknown parameters in the equation of surface are acquired through least square method. Principle of GA is explained. Then, the profile error of Archimedes Helicoid surface is obtained through GA optimization method. To validate the proposed method, the profile error of an Archimedes helicoid surface, Archimedes Cylindrical worm (ZA worm) surface, is evaluated. The results show that the proposed method is capable of correctly evaluating the profile error of Archimedes helicoid surface and satisfy the evaluation standard of the Minimum Zone Method. It can be applied to deal with the measured data of profile error of complex surface obtained by three coordinate measurement machines (CMM).
Application of genetic algorithm in quasi-static fiber grating wavelength demodulation technology
NASA Astrophysics Data System (ADS)
Teng, Feng-Cheng; Yin, Wen-Wen; Wu, Fei; Li, Zhi-Quang; Wu, Ti-Hua
2007-07-01
A modified genetic algorithm (GA) has been proposed, which was used to wavelength demodulation in quasi-static fiber grating sensing system. The modification method of GA has been introduced and the relevant mathematical model has been established. The objective function and individual fitness evaluation strategy interrelated with GA are also established. The influence of population size, chromosome size, generations, crossover probability and mutation probability on the GA has been analyzed, and the optimal parameters of modified GA have been obtained. The simulations and experiments, show that the modified GA can be applied to quasi-static fiber grating sensing system, and the wavelength demodulation precision is equal to or less than 3 pm.
Application of Transinformation Entropy And Genetic Algorithm for Raingauge Site Selection
NASA Astrophysics Data System (ADS)
Karimi Hosseini, Azadeh; Bozorg Haddad, Omid
2010-05-01
To assure the success of water projects, providing accurate rainfall data has great importance. Recognition of all the processes that create the surface water is the first step in water projects. Principle of this understanding is based on the information from climate and precipitation characteristics. So, the necessity of rainfall data in water projects and regional programming is undeniable. Collection of rainfall stations in a region or basin is called rainfall network. An efficient network is a network which provides us with accurate and trustable data due to proper raingauges density. So, an acceptable estimation of the needed climatic and hydrologic factors could be provided. Therefore, spatial distribution of these stations should give a comprehensive understanding of the spatial variability of rainfall in the region. The rainfall network design includes the number of raingauges and their locations. In this paper, an entropy-based method is applied to determine the location of new rainfall stations in Gav-khuni basin located in central region of Iran. For this purpose, the measurement of transinformation entropy that represents the transmitted information has been used. Because of spatial variations in annual rainfall in the area, it has been divided into three zones based on mean annual rainfall of its stations and the correlation coefficient between each pair of stations. Then, the transinformation-distance curve is developed for observed data of each zone to find the transinformation-distance relationship in each area. A set of potential points are created in center of cells of a 5km×5km grid. Then through the optimization by genetic algorithm the best locations for new monitoring stations in each zone have been achieved and a set of 20, 15 and 9 potential points are determined as additional raingauges to be added to zone one, two and three, respectively, which improve the mean transinformation entropy in that zone through the new developed rainfall
Messy genetic algorithms: Recent developments
Kargupta, H.
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.
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.
NASA Astrophysics Data System (ADS)
Coulot, D.; Collilieux, X.; Pollet, A.; Berio, P.; Gobinddass, M. L.; Soudarin, L.; Willis, P.
2009-04-01
In this study, we apply Genetic Algorithms (GAs) in order to optimize the referencing (and consequently the precision - stability - and the accuracy) of the EOPs with respect to ITRF2005. These EOPs are derived from SLR or DORIS data at a daily sampling, simultaneously with weekly station positions. GAs are evolutionary algorithms, i.e. stochastic algorithms based on the evolution theory and using some genetic operators such as chromosome crossover and gene mutations. They are currently used for a broad spectrum of activities, from medicine to defence to finance. They have also been used in Earth and Space sciences (remote sensing, geophysics, meteorology, astrophysics, astronomy, etc.) since the early nineties. But, as far as we know, the present work is the first application of GAs in the framework of Space Geodesy. In this work, we use an algorithm based on GAs to find weekly optimal sub-networks over which applying minimum constraints in order to reference EOPs. Each week, the three rotations of the involved terrestrial frames are forced to be zero with respect to ITRF2005 through minimum constraints applied over these sub-networks, which are called Genetically Modified Networks (GMNs). The reference system effects are used as objectives to optimize with GAs. Regarding SLR, our approach provides an improvement of 10 % in accuracy for polar motion in comparison to the results obtained with the network specially designed for EOP referencing by the Analysis Working Group of the International Laser Ranging Service. This improvement of nearly 25 as represents 50 % of the current precision of the IERS 05 C04 reference series. We also show preliminary results regarding such GMNs for the DORIS technique using two different solutions (IGN and CNES/CLS solutions). Finally, for practical applications, we also test, for the SLR and the DORIS techniques, the possible emergence of global core networks to be used for EOP referencing on the basis of GAs.
2014-01-01
Background Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Methods Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Results Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. Conclusions This study shows that if prediction accuracy is the objective, the GA
Morphological evolution of protective works by Genetic Algorithms: An application to Mt Etna
NASA Astrophysics Data System (ADS)
Marocco, Davide; Spataro, William; D'Ambrosio, Donato; Filippone, Giuseppe; Rongo, Rocco; Iovine, Giulio; Neri, Marco
2013-04-01
The hazard induced by dangerous flow-type phenomena - e.g. lava flows, earth flows, debris flows, and debris avalanches - has increased in recent years due to continuous urbanization. In many cases, the numerical simulation of hypothetical events can help to forecast the flow path in advance and therefore give indications about the areas that can be considered for the construction of protective works - e.g. earth barriers or channels. In this way, urbanized areas, as well as cultural heritage sites or even important infrastructures, can be protected by diverting the flow towards lower interest regions. Here, we have considered the numerical Cellular Automata model Sciara-fv2 for simulating lava flows at Mt Etna and Genetic Algorithms for optimizing the position, orientation and extension of an earth barrier built to protect the Rifugio Sapienza, a well-known touristic facility located near the summit of the volcano. The Rifugio Sapienza area was in fact interested by a lava flow in 2003, which destroyed a Service Center, a parking area and a Cafeteria. In this study, a perimeter was devised around the Rifugio (i.e., security perimeter), which delimitates the area that has to be protected by the flow. Furthermore, another perimeter was devised (i.e., work perimeter), specifying the area in which the earth barrier can be located. The barrier is specified by three parameters, namely the two geographic coordinates of the vertex and the height. In fact, in this preliminary analysis the barrier was modeled as a segment (in plant) having a constant height. Though preliminary, the study has produced extremely positive results. Among different alternatives generated by the genetic algorithm, an interesting scenario consists of a 35 meters barrier high solution, which completely deviates the flow avoiding that the lava reaches the inhabited area. The relative elevated height of the barrier is high due to the fact that the crater is located close to the area to be protected
Luque-Baena, Rafael Marcos; Urda, Daniel; Subirats, Jose Luis; Franco, Leonardo; Jerez, Jose M
2014-05-07
Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results
Genetic Algorithms: A gentle introduction
Jong, K.D.
1994-12-31
Information is presented on genetic algorithms in outline form. The following topics are discussed: how are new samples generated, a genotypic viewpoint, a phenotypic viewpoint, an optimization viewpoint, an intuitive view, parameter optimization problems, evolving production rates, genetic programming, GAs and NNs, formal analysis, Lemmas and theorems, discrete Walsh transforms, deceptive problems, Markov chain analysis, and PAC learning analysis.
Application of Genetic Algorithms to Sorting, Swapping and Shimming of the SOLEIL Undulator Magnets
Chubar, O.; Rudenko, O.; Benabderrahmane, C.; Marcouille, O.; Filhol, J. M.; Couprie, M. E.
2007-01-19
Undulators are typically composed of a large number of individual magnets, from tens to many hundreds, depending on undulator type and technology. Characteristics of real magnets often deviate from ideal values, resulting in degradation of the undulator performance. It is known that the imperfections of individual magnets can be mutually compensated, to certain extent, by appropriate sorting of the magnets and/or magnet modules, based on measurements of their individual characteristics, performed before the insertion device (ID) assembly. After the assembly, the quality of the ID magnetic field can be improved by magnet swapping and by shimming, consisting either in small displacements of some of the magnets or in adding small pieces of permanent magnet or soft iron material to appropriate locations of the structure. Mathematically, the magnet sorting, swapping and shimming can be considered as multi-objective constrained optimization problems with large numbers of variables. We show that each of these problems can be efficiently solved by means of Genetic Algorithms with 3D magnetostatics methods and magnetic measurements data used for the fitness calculation. First practical results obtained with three different undulators of the SOLEIL Synchrotron are presented.
Application of random number generators in genetic algorithms to improve rainfall-runoff modelling
NASA Astrophysics Data System (ADS)
Chlumecký, Martin; Buchtele, Josef; Richta, Karel
2017-10-01
The efficient calibration of rainfall-runoff models is a difficult issue, even for experienced hydrologists. Therefore, fast and high-quality model calibration is a valuable improvement. This paper describes a novel methodology and software for the optimisation of a rainfall-runoff modelling using a genetic algorithm (GA) with a newly prepared concept of a random number generator (HRNG), which is the core of the optimisation. The GA estimates model parameters using evolutionary principles, which requires a quality number generator. The new HRNG generates random numbers based on hydrological information and it provides better numbers compared to pure software generators. The GA enhances the model calibration very well and the goal is to optimise the calibration of the model with a minimum of user interaction. This article focuses on improving the internal structure of the GA, which is shielded from the user. The results that we obtained indicate that the HRNG provides a stable trend in the output quality of the model, despite various configurations of the GA. In contrast to previous research, the HRNG speeds up the calibration of the model and offers an improvement of rainfall-runoff modelling.
NASA Astrophysics Data System (ADS)
Bates, I.; Lawton, A.; Breikin, T.; Dunlop, M.
Space Systems Group, University of Sheffield, U.K. Automatic Control and Systems Engineering, University of Sheffield, U.K. 3 Imperial College, London, U.K.A Genetic Algorithm (GA) approach is presented to solve a problem for turbulent space plasma system modelling in the form of Generalised Frequency Response Functions (GFRFs), using in-situ multi-satellite magnetic field measurements of the plasma turbulence. Soft Computing techniques have now been used for many years in Industry for nonlinear system identification. These techniques approach the problem of understanding a system, e.g. a chemical plant or a jet engine, by model structure selection and fitting parameters of the chosen model for the system using measured inputs and outputs of the system, which can then be used to determine physical characteristics of the system. GAs are one such technique that has been developed, providing essentially a series of solutions that evolve in a way to improve the model. Experimental space plasma turbulence studies have benefited from these System Identification techniques. Multi-point satellite observations provide input and output measurements of the turbulent plasma system. In previous work it was found natural to fit parameters to GFRFs, which derive from Volterra series and lead to quantitative measurements of linear wave-field growth and higher order wave-wave interactions. In previous work these techniques were applied using a Least Squares (LS) parameter fit. Results using GAs are compared to results obtained from the LS approach.
NASA Astrophysics Data System (ADS)
Manu, V. S.; Veglia, Gianluigi
2016-12-01
Identity operation in the form of π pulses is widely used in NMR spectroscopy. For an isolated single spin system, a sequence of even number of π pulses performs an identity operation, leaving the spin state essentially unaltered. For multi-spin systems, trains of π pulses with appropriate phases and time delays modulate the spin Hamiltonian to perform operations such as decoupling and recoupling. However, experimental imperfections often jeopardize the outcome, leading to severe losses in sensitivity. Here, we demonstrate that a newly designed Genetic Algorithm (GA) is able to optimize a train of π pulses, resulting in a robust identity operation. As proof-of-concept, we optimized the recoupling sequence in the transferred-echo double-resonance (TEDOR) pulse sequence, a key experiment in biological magic angle spinning (MAS) solid-state NMR for measuring multiple carbon-nitrogen distances. The GA modified TEDOR (GMO-TEDOR) experiment with improved recoupling efficiency results in a net gain of sensitivity up to 28% as tested on a uniformly 13C, 15N labeled microcrystalline ubiquitin sample. The robust identity operation achieved via GA paves the way for the optimization of several other pulse sequences used for both solid- and liquid-state NMR used for decoupling, recoupling, and relaxation experiments.
NASA Astrophysics Data System (ADS)
Friedel, Michael; Buscema, Massimo
2016-04-01
Aquatic ecosystem models can potentially be used to understand the influence of stresses on catchment resource quality. Given that catchment responses are functions of natural and anthropogenic stresses reflected in sparse and spatiotemporal biological, physical, and chemical measurements, an ecosystem is difficult to model using statistical or numerical methods. We propose an artificial adaptive systems approach to model ecosystems. First, an unsupervised machine-learning (ML) network is trained using the set of available sparse and disparate data variables. Second, an evolutionary algorithm with genetic doping is applied to reduce the number of ecosystem variables to an optimal set. Third, the optimal set of ecosystem variables is used to retrain the ML network. Fourth, a stochastic cross-validation approach is applied to quantify and compare the nonlinear uncertainty in selected predictions of the original and reduced models. Results are presented for aquatic ecosystems (tens of thousands of square kilometers) undergoing landscape change in the USA: Upper Illinois River Basin and Central Colorado Assessment Project Area, and Southland region, NZ.
Application of genetic algorithm for discovery of core effective formulae in TCM clinical data.
Yang, Ming; Poon, Josiah; Wang, Shaomo; Jiao, Lijing; Poon, Simon; Cui, Lizhi; Chen, Peiqi; Sze, Daniel Man-Yuen; Xu, Ling
2013-01-01
Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.
Applications of information theory, genetic algorithms, and neural models to predict oil flow
NASA Astrophysics Data System (ADS)
Ludwig, Oswaldo; Nunes, Urbano; Araújo, Rui; Schnitman, Leizer; Lepikson, Herman Augusto
2009-07-01
This work introduces a new information-theoretic methodology for choosing variables and their time lags in a prediction setting, particularly when neural networks are used in non-linear modeling. The first contribution of this work is the Cross Entropy Function (XEF) proposed to select input variables and their lags in order to compose the input vector of black-box prediction models. The proposed XEF method is more appropriate than the usually applied Cross Correlation Function (XCF) when the relationship among the input and output signals comes from a non-linear dynamic system. The second contribution is a method that minimizes the Joint Conditional Entropy (JCE) between the input and output variables by means of a Genetic Algorithm (GA). The aim is to take into account the dependence among the input variables when selecting the most appropriate set of inputs for a prediction problem. In short, theses methods can be used to assist the selection of input training data that have the necessary information to predict the target data. The proposed methods are applied to a petroleum engineering problem; predicting oil production. Experimental results obtained with a real-world dataset are presented demonstrating the feasibility and effectiveness of the method.
Application of Genetic Algorithm for Discovery of Core Effective Formulae in TCM Clinical Data
Yang, Ming; Poon, Josiah; Wang, Shaomo; Jiao, Lijing; Poon, Simon; Cui, Lizhi; Chen, Peiqi; Sze, Daniel Man-Yuen; Xu, Ling
2013-01-01
Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach. PMID:24288577
NASA Astrophysics Data System (ADS)
KIm, T. W.; Park, D. H.; Kang, D.; Ahn, J. H.
2016-12-01
There are a number of methods to estimate the effective rainfall, i.e., Φ-index, W-index, NRCS-CN, constant fraction method, constant loss rate method, initial loss-constant loss rate method. The NRCS-CN (Natural Resources Conservation Service-Curve Number) method has been generally applied for estimating effective rainfalls. However, there are no criteria that reflect the geographic characteristics of Korea having more than 70% of mountainous and rice paddy areas, leading to significant errors in runoff calculation. Thus, it is required to estimate the runoff curve number considering Korea land use classification. However, there are practical difficulties to conduct the accurate research and experimentation. In this study, after selecting target areas (urban, agriculture, forest), we performed the runoff analysis to estimate appropriate CN values for the selected basins. To do this, curve numbers for soil type A were estimated using genetic algorithm, and then curve numbers for other soil types (B, C, D) were estimated using CN aligner equation. Comparing the curve numbers provided by the NRCS with those estimated in this study, it was observed that the slightly differences at several basins. Through the above process, this study proposed new curve numbers to reflect observed data of rainfall and runoff. Acknowledgement This research was supported by a grant [MPSS-NH-2015-79] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.
Deceptiveness and genetic algorithm dynamics
Liepins, G.E. ); Vose, M.D. )
1990-01-01
We address deceptiveness, one of at least four reasons genetic algorithms can fail to converge to function optima. We construct fully deceptive functions and other functions of intermediate deceptiveness. For the fully deceptive functions of our construction, we generate linear transformations that induce changes of representation to render the functions fully easy. We further model genetic algorithm selection recombination as the interleaving of linear and quadratic operators. Spectral analysis of the underlying matrices allows us to draw preliminary conclusions about fixed points and their stability. We also obtain an explicit formula relating the nonuniform Walsh transform to the dynamics of genetic search. 21 refs.
NASA Astrophysics Data System (ADS)
Hongesombut, Komsan; Mitani, Yasunori; Tsuji, Kiichiro
Fuzzy logic control has been applied to various applications in power systems. Its control rules and membership functions are typically obtained by trial and error methods or experience knowledge. Proposed here is the application of a micro-genetic algorithm (micro-GA) to simultaneously design optimal membership functions and control rules for STATCOM. First, we propose a simple approach to extract membership functions and fuzzy logic control rules based on observed signals. Then a proposed GA will be applied to optimize membership functions and its control rules. To validate the effectiveness of the proposed approach, several simulation studies have been performed on a multimachine power system. Simulation results show that the proposed fuzzy logic controller with STATCOM can effectively and robustly enhance the damping of oscillations.
NASA Astrophysics Data System (ADS)
Harker, Brian J.
The measurement of vector magnetic fields on the sun is one of the most important diagnostic tools for characterizing solar activity. The ubiquitous solar wind is guided into interplanetary space by open magnetic field lines in the upper solar atmosphere. Highly-energetic solar flares and Coronal Mass Ejections (CMEs) are triggered in lower layers of the solar atmosphere by the driving forces at the visible "surface" of the sun, the photosphere. The driving forces there tangle and interweave the vector magnetic fields, ultimately leading to an unstable field topology with large excess magnetic energy, and this excess energy is suddenly and violently released by magnetic reconnection, emitting intense broadband radiation that spans the electromagnetic spectrum, accelerating billions of metric tons of plasma away from the sun, and finally relaxing the magnetic field to lower-energy states. These eruptive flaring events can have severe impacts on the near-Earth environment and the human technology that inhabits it. This dissertation presents a novel inversion method for inferring the properties of the vector magnetic field from telescopic measurements of the polarization states (Stokes vector) of the light received from the sun, in an effort to develop a method that is fast, accurate, and reliable. One of the long-term goals of this work is to develop such a method that is capable of rapidly-producing characterizations of the magnetic field from time-sequential data, such that near real-time projections of the complexity and flare- productivity of solar active regions can be made. This will be a boon to the field of solar flare forecasting, and should help mitigate the harmful effects of space weather on mankind's space-based endeavors. To this end, I have developed an inversion method based on genetic algorithms (GA) that have the potential for achieving such high-speed analysis.
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.
Fisz, Jacek J
2006-12-07
The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi
Research on Routing Selection Algorithm Based on Genetic Algorithm
NASA Astrophysics Data System (ADS)
Gao, Guohong; Zhang, Baojian; Li, Xueyong; Lv, Jinna
The hereditary algorithm is a kind of random searching and method of optimizing based on living beings natural selection and hereditary mechanism. In recent years, because of the potentiality in solving complicate problems and the successful application in the fields of industrial project, hereditary algorithm has been widely concerned by the domestic and international scholar. Routing Selection communication has been defined a standard communication model of IP version 6.This paper proposes a service model of Routing Selection communication, and designs and implements a new Routing Selection algorithm based on genetic algorithm.The experimental simulation results show that this algorithm can get more resolution at less time and more balanced network load, which enhances search ratio and the availability of network resource, and improves the quality of service.
Genetic algorithm for neural networks optimization
NASA Astrophysics Data System (ADS)
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
NASA Astrophysics Data System (ADS)
Yang, S.; Chang, L.; Chen, Y.; Jung, C.; Huang, C.; Chen, J.; Tsai, P. J.
2010-12-01
As groundwater resources are vital to the regional water supply, protection of groundwater resources have become important issues. A systematic approach to locating high recharge areas is the first step in protection of groundwater. In a previous study proposed by the Central Geological Survey (CGS), the CGS used a factor-based approach to determine the spatial distribution of recharge potential (RP). Seven factors to define RP were selected, including “land usage”, “type of surface soil”, “drainage density”, “average annual rainfall”, “correlation between rainfall and variation of groundwater level”, “variation of unit aquifer storage”, and “hydraulic conductivity”. Features of each factor were transformed into factor scores, which ranged from 0 to 100. A high score implied high recharge capacity. The features of five factors (“drainage density”, “average annual rainfall”, “correlation between rainfall and variation of groundwater level”, “variation of unit aquifer storage”, and “hydraulic conductivity”) were continuous variables and could be linearly normalized as factor scores. The features of the remaining factors were discrete variables. In the abovementioned study, factor scores were subjectively assigned based on the experience, opinion, or judgment of the researchers. The factor scores of the seven factors were weighted and summed as the value of RP. The weighted values of various factors were based on a schematic sketch that showed the interactive influence of factors in regards to properties of recharge. The sketch, proposed by Shaban et. al. (2006), showed major and minor interactive influences. Shaban et. al. set the ratio of minor influence to major influence to 0.5. To increase objectivity in the calculation of RP, this study used a genetic algorithm (GA) to determine optimal scores for the two discrete factors. Because the spatial distribution of nitrate concentration (NC) was positively correlated with
Genetic algorithms for protein threading.
Yadgari, J; Amir, A; Unger, R
1998-01-01
Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).
NASA Astrophysics Data System (ADS)
Stramaglia, Sebastiano; Satalino, Giuseppe; Sternieri, A.; Anelli, P.; Blonda, Palma N.; Pasquariello, Guido
1998-10-01
We consider the problem of classification of remote sensed data from LANDSAT Thematic Mapper images. The data have been acquired in July 1986 on an area locate din South Italy. We compare the performance obtained by feed-forward neural networks designed by a parallel genetic algorithm to determine their topology with the ones obtained by means of a multi-layer perceptron trained with Back Propagation learning rule. The parallel genetic algorithm, implemented on the APE100/Quadrics platform, is based on the coding scheme recently proposed by Sternieri and Anelli and exploits a recently proposed environment for genetic algorithms on Quadrics, called AGAPE. The SASIMD architecture of Quadrics forces the chromosome representation. The coding scheme provides that the connections weights of the neural network are organized as a floating point string. The parallelization scheme adopted is the elitistic coarse grained stepping stone model, with migration occurring only towards neighboring processors. The fitness function depends on the mean square error.After fixing the total number of individuals and running the algorithm on Quadrics architectures with different number of processors, the proposed parallel genetic algorithm displayed a superlinear speedup. We report results obtained on a data set made of 1400 patterns.
Boiler-turbine control system design using a genetic algorithm
Dimeo, R.; Lee, K.Y.
1995-12-01
This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.
Genetic algorithms for the vehicle routing problem
NASA Astrophysics Data System (ADS)
Volna, Eva
2016-06-01
The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.
Genetic algorithms using SISAL parallel programming language
Tejada, S.
1994-05-06
Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.
Specific optimization of genetic algorithm on special algebras
NASA Astrophysics Data System (ADS)
Habiballa, Hashim; Novak, Vilem; Dyba, Martin; Schenk, Jiri
2016-06-01
Searching for complex finite algebras can be succesfully done by the means of genetic algorithm as we showed in former works. This genetic algorithm needs specific optimization of crossover and mutation. We present details about these optimizations which are already implemented in software application for this task - EQCreator.
TIP: protein backtranslation aided by genetic algorithms.
Moreira, Andrés; Maass, Alejandro
2004-09-01
Several applications require the backtranslation of a protein sequence into a nucleic acid sequence. The degeneracy of the genetic code makes this process ambiguous; moreover, not every translation is equally viable. The usual answer is to mimic the codon usage of the target species; however, this does not capture all the relevant features of the 'genomic styles' from different taxa. The program TIP ' Traducción Inversa de Proteínas') applies genetic algorithms to improve the backtranslation, by minimizing the difference of some coding statistics with respect to their average value in the target. http://www.cmm.uchile.cl/genoma/tip/
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.
Kuo, J T; Cheng, W C; Chen, L
2003-01-01
Multipurpose operation is adopted by most reservoirs in Taiwan in order to maximize the benefits of power generation, water supply, irrigation and recreational purposes. A multiobjective approach can be used to obtain trade-off curves among these multipurpose targets. The weighting method, in which different weighting factors are used for different purposes, was used in this research work. In Taiwan, most major reservoirs are operated by rule curves. Genetic algorithms with characteristics of artificial intelligence were applied to obtain the optimal rule curves of the multireservoir system under multipurpose operation in Chou-Shui River Basin in central Taiwan. The model results reveal that different shapes of rule curves under different weighting factors on targets can be efficiently obtained by genetic algorithms. Pareto optimal solutions for a trade-off between water supply and hydropower were obtained and analyzed.
Medical image segmentation using genetic algorithms.
Maulik, Ujjwal
2009-03-01
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 therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.
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.
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.
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.
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…
NASA Astrophysics Data System (ADS)
Tang, Shihua; Li, Feida; Liu, Yintao; Lan, Lan; Zhou, Conglin; Huang, Qing
2015-12-01
With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods'. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.
Filter selection using genetic algorithms
NASA Astrophysics Data System (ADS)
Patel, Devesh
1996-03-01
Convolution operators act as matched filters for certain types of variations found in images and have been extensively used in the analysis of images. However, filtering through a bank of N filters generates N filtered images, consequently increasing the amount of data considerably. Moreover, not all these filters have the same discriminatory capabilities for the individual images, thus making the task of any classifier difficult. In this paper, we use genetic algorithms to select a subset of relevant filters. Genetic algorithms represent a class of adaptive search techniques where the processes are similar to natural selection of biological evolution. The steady state model (GENITOR) has been used in this paper. The reduction of filters improves the performance of the classifier (which in this paper is the multi-layer perceptron neural network) and furthermore reduces the computational requirement. In this study we use the Laws filters which were proposed for the analysis of texture images. Our aim is to recognize the different textures on the images using the reduced filter set.
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.
Excursion-Set-Mediated Genetic Algorithm
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.
Excursion-Set-Mediated Genetic Algorithm
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.
Genetic Algorithms Viewed as Anticipatory Systems
NASA Astrophysics Data System (ADS)
Mocanu, Irina; Kalisz, Eugenia; Negreanu, Lorina
2010-11-01
This paper proposes a new version of genetic algorithms—the anticipatory genetic algorithm AGA. The performance evaluation included in the paper shows that AGA is superior to traditional genetic algorithm from both speed and accuracy points of view. The paper also presents how this algorithm can be applied to solve a complex problem: image annotation, intended to be used in content based image retrieval systems.
Irregular Applications: Architectures & Algorithms
Feo, John T.; Villa, Oreste; Tumeo, Antonino; Secchi, Simone
2012-02-06
Irregular applications are characterized by irregular data structures, control and communication patterns. Novel irregular high performance applications which deal with large data sets and require have recently appeared. Unfortunately, current high performance systems and software infrastructures executes irregular algorithms poorly. Only coordinated efforts by end user, area specialists and computer scientists that consider both the architecture and the software stack may be able to provide solutions to the challenges of modern irregular applications.
Liu, Li; Shi, Haiying; Huo, Liqin; Zhang, Feng; Zheng, Chongxun; You, Jia; He, Xining; Zhang, Jie
2011-10-01
This paper is to provide a basis for the establishment of an early diagnostic system for hypoxic-ischemic encephalopathy (HIE) by performing segmentation and feature extraction of lesions on the MR images of neonatal babies with HIE. The segmentation on MR images of HIE based on the genetic algorithm (GA) combined with a pulse-coupled neural network (PCNN) were carried out. There were better segmentation results by using PCNN segmentation based on GA than PCNN segmentation with fixed parameters. The data suggested that a PCNN based on GA could provide effective assistance for diagnosis and research.
MacInnis, Robert J; Antoniou, Antonis C; Eeles, Rosalind A; Severi, Gianluca; Olama, Ali Amin Al; McGuffog, Lesley; Kote-Jarai, Zsofia; Guy, Michelle; O'Brien, Lynne T; Hall, Amanda L; Wilkinson, Rosemary A; Sawyer, Emma; Ardern-Jones, Audrey T; Dearnaley, David P.; Horwich, Alan; Khoo, Vincent S.; Parker, Christopher C.; Huddart, Robert A.; Van As, Nicholas; McCredie, Margaret R; English, Dallas R; Giles, Graham G; Hopper, John L; Easton, Douglas F
2014-01-01
Genome Wide Association Studies have identified several Single Nucleotide Polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where P∼N(0,σP2). Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, PK∼N(0,σK2), and the residual polygenic component due to the postulated but as yet unknown genetic variants, PU∼N(0,σU2). The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist. PMID:21769933
Optical flow optimization using parallel genetic algorithm
NASA Astrophysics Data System (ADS)
Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe
2011-06-01
A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.
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.
NASA Astrophysics Data System (ADS)
Lim, Kyoung Jae; Park, Youn Shik; Kim, Jonggun; Shin, Yong-Chul; Kim, Nam Won; Kim, Seong Joon; Jeon, Ji-Hong; Engel, Bernard A.
2010-07-01
Many hydrologic and water quality computer models have been developed and applied to assess hydrologic and water quality impacts of land use changes. These models are typically calibrated and validated prior to their application. The Long-Term Hydrologic Impact Assessment (L-THIA) model was applied to the Little Eagle Creek (LEC) watershed and compared with the filtered direct runoff using BFLOW and the Eckhardt digital filter (with a default BFI max value of 0.80 and filter parameter value of 0.98), both available in the Web GIS-based Hydrograph Analysis Tool, called WHAT. The R2 value and the Nash-Sutcliffe coefficient values were 0.68 and 0.64 with BFLOW, and 0.66 and 0.63 with the Eckhardt digital filter. Although these results indicate that the L-THIA model estimates direct runoff reasonably well, the filtered direct runoff values using BFLOW and Eckhardt digital filter with the default BFI max and filter parameter values do not reflect hydrological and hydrogeological situations in the LEC watershed. Thus, a BFI max GA-Analyzer module (BFI max Genetic Algorithm-Analyzer module) was developed and integrated into the WHAT system for determination of the optimum BFI max parameter and filter parameter of the Eckhardt digital filter. With the automated recession curve analysis method and BFI max GA-Analyzer module of the WHAT system, the optimum BFI max value of 0.491 and filter parameter value of 0.987 were determined for the LEC watershed. The comparison of L-THIA estimates with filtered direct runoff using an optimized BFI max and filter parameter resulted in an R2 value of 0.66 and the Nash-Sutcliffe coefficient value of 0.63. However, L-THIA estimates calibrated with the optimized BFI max and filter parameter increased by 33% and estimated NPS pollutant loadings increased by more than 20%. This indicates L-THIA model direct runoff estimates can be incorrect by 33% and NPS pollutant loading estimation by more than 20%, if the accuracy of the baseflow
Genetic-based unit commitment algorithm
Maifeld, T.T.; Sheble, G.B.
1996-08-01
This paper presents a new unit commitment scheduling algorithm. The proposed algorithm consist of using a genetic algorithm with domain specific mutation operators. The proposed algorithm can easily accommodate any constraint that can be true costed. Robustness of the proposed algorithm is demonstrated by comparison to a Lagrangian relaxation unit commitment algorithm on three different utilities. Results show the proposed algorithm finds good unit commitment schedules in a reasonable amount of computation time. Included in the appendix is an explanation of the true costing approach.
Genetic algorithms for route discovery.
Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy
2006-12-01
Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.
Predicting Protein Structure Using Parallel Genetic Algorithms.
1994-12-01
34 IEEE Transactions on Systems, Man and Cybernetics, 10(9) (September 1980). 16. De Jong, Kenneth A. "On Using Genetic Algoriths to Search Program...By " Predicting rotein Structure D istribticfiar.. ................ Using Parallel Genetic Algorithms ,Avaiu " ’ •"... Dist THESIS I IGeorge H...iiLite-d Approved for public release; distribution unlimited AFIT/ GCS /ENG/94D-03 Predicting Protein Structure Using Parallel Genetic Algorithms
NASA Astrophysics Data System (ADS)
Bang, Jeongho; Yoo, Seokwon
2014-12-01
We propose a genetic-algorithm-based method to find the unitary transformations for any desired quantum computation. We formulate a simple genetic algorithm by introducing the "genetic parameter vector" of the unitary transformations to be found. In the genetic algorithm process, all components of the genetic parameter vectors are supposed to evolve to the solution parameters of the unitary transformations. We apply our method to find the optimal unitary transformations and to generalize the corresponding quantum algorithms for a realistic problem, the one-bit oracle decision problem, or the often-called Deutsch problem. By numerical simulations, we can faithfully find the appropriate unitary transformations to solve the problem by using our method. We analyze the quantum algorithms identified by the found unitary transformations and generalize the variant models of the original Deutsch's algorithm.
Zagrouba, M.; Sellami, A.; Bouaicha, M.; Ksouri, M.
2010-05-15
In this paper, we propose to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters (I{sub s}, I{sub ph}, R{sub s}, R{sub sh}, and n) of photovoltaic (PV) solar cells and modules. These parameters were used to determine the corresponding maximum power point (MPP) from the illuminated current-voltage (I-V) characteristic. The one diode type approach is used to model the AM1.5 I-V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as a non convex optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case of non convex optimization criteria. Compared to other methods, we find that the GAs is a very efficient technique to estimate the electrical parameters of PV solar cells and modules. Indeed, the race of the algorithm stopped after five generations in the case of PV solar cells and seven generations in the case of PV modules. The identified parameters are then used to extract the maximum power working points for both cell and module. (author)
Solving Maximal Clique Problem through Genetic Algorithm
NASA Astrophysics Data System (ADS)
Rajawat, Shalini; Hemrajani, Naveen; Menghani, Ekta
2010-11-01
Genetic algorithm is one of the most interesting heuristic search techniques. It depends basically on three operations; selection, crossover and mutation. The outcome of the three operations is a new population for the next generation. Repeating these operations until the termination condition is reached. All the operations in the algorithm are accessible with today's molecular biotechnology. The simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability.
Selfish Gene Algorithm Vs Genetic Algorithm: A Review
NASA Astrophysics Data System (ADS)
Ariff, Norharyati Md; Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed
2016-11-01
Evolutionary algorithm is one of the algorithms inspired by the nature. Within little more than a decade hundreds of papers have reported successful applications of EAs. In this paper, the Selfish Gene Algorithms (SFGA), as one of the latest evolutionary algorithms (EAs) inspired from the Selfish Gene Theory which is an interpretation of Darwinian Theory ideas from the biologist Richards Dawkins on 1989. In this paper, following a brief introduction to the Selfish Gene Algorithm (SFGA), the chronology of its evolution is presented. It is the purpose of this paper is to present an overview of the concepts of Selfish Gene Algorithm (SFGA) as well as its opportunities and challenges. Accordingly, the history, step involves in the algorithm are discussed and its different applications together with an analysis of these applications are evaluated.
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.
Genetic algorithms at UC Davis/LLNL
Vemuri, V.R.
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.
Genetic algorithms and the immune system
Forrest, S. . Dept. of Computer Science); Perelson, A.S. )
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.
Evolving evolutionary algorithms using linear genetic programming.
Oltean, Mihai
2005-01-01
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
NASA Astrophysics Data System (ADS)
Yang, Haiqing; Yang, Haiqing; He, Yong
In order to detect leaf chlorophyll level nondestructively and instantly, VIS/NIR reflection spectroscopy technique was examined. In the test, 70 leaf samples were collected for model calibration and another 50 for model verification. Each leaf sample was optically measured by USB4000, a modular spectrometer. By the observation of spectral curves, the spectral range between 650nm and 750nm was found significant for mathematic modeling of leaf chlorophyll level. SPAD-502 meter was used for chemometrical measurement of leaf chlorophyll value. In the test, it was found necessary to put leaf thickness into consideration. The procedure of shaping the prediction model is as follows: First, leaf chlorophyll level prediction equation was created with uncertain parameters. Second, a genetic algorithm was programmed by Visual Basic 6.0 for parameter optimization. As the result of the calculation, the optimal spectral range was narrowed within 683.24nm and 733.91nm. Compared with the R2=0.2309 for calibration set and R2=0.5675 for on the spectral modeling is significant: the R2 of calibration set and verification set has been improved as high as 0.8658 and 0.9161 respectively. The test showed that it is practical to use VIS/NIR reflection spectrometer for the quantitative determination of leaf chlorophyll level.
Chen, Xiyuan; Song, Rui; Shen, Chong; Zhang, Hong
2014-09-10
The fiber-optic gyroscope (FOG) has been widely used as a satellite and automobile attitude sensor in many industrial and defense fields such as navigation and positioning. Based on the fact that the FOG is sensitive to temperature variation, a novel (to our knowledge) error-processing technique for the FOG through a set of temperature experiment results and error analysis is presented. The method contains two parts: one is denoising, and the other is modeling and compensating. After the denoising part, a novel modeling method which is based on the dynamic modified Elman neural network (ENN) is proposed. In order to get the optimum parameters of the ENN, the genetic algorithm (GA) is applied and the optimization objective function was set as the difference between the predicted data and real data. The modeling and compensating results indicate that the drift caused by the varying temperature can be reduced and compensated effectively by the proposed model; the prediction accuracy of the GA-ENN is improved 20% over the ENN.
NASA Astrophysics Data System (ADS)
Ahn, Joong-Bae; Lee, Joonlee
2016-08-01
A new multimodel ensemble (MME) method that uses a genetic algorithm (GA) is developed and applied to the prediction of winter surface air temperature (SAT) and precipitation. The GA based on the biological process of natural evolution is a nonlinear method which solves nonlinear optimization problems. Hindcast data of winter SAT and precipitation from the six coupled general circulation models participating in the seasonal MME prediction system of the Asia-Pacific Economic Conference Climate Center are used. Three MME methods using GA (MME/GAs) are examined in comparison with a simple composite MME strategy (MS0): MS1 which applies GA to single-model ensembles (SMEs), MS2 which applies GA to each ensemble member and then performs a simple composite method for MME, and MS3 which applies GA to both MME and SME. MS3 shows the highest predictability compared to MS0, MS1, and MS2 for both winter SAT and precipitation. These results indicate that biases of ensemble members of each model and model ensemble are more reduced with MS3 than with other MME/GAs and MS0. The predictability of the MME/GAs shows a greater improvement than that of MS0, particularly in higher-latitude land areas. The reason for the more improved increase of predictability over the land area, particularly in MS3, seems to be the fact that GA is more efficient in finding an optimum solution in a complex region where nonlinear physical properties are evident.
Refined genetic algorithm -- Economic dispatch example
Sheble, G.B.; Brittig, K.
1995-02-01
A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.
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.
Segmentation of color images using genetic algorithm with image histogram
NASA Astrophysics Data System (ADS)
Sneha Latha, P.; Kumar, Pawan; Kahu, Samruddhi; Bhurchandi, Kishor M.
2015-02-01
This paper proposes a family of color image segmentation algorithms using genetic approach and color similarity threshold in terns of Just noticeable difference. Instead of segmenting and then optimizing, the proposed technique directly uses GA for optimized segmentation of color images. Application of GA on larger size color images is computationally heavy so they are applied on 4D-color image histogram table. The performance of the proposed algorithms is benchmarked on BSD dataset with color histogram based segmentation and Fuzzy C-means Algorithm using Probabilistic Rand Index (PRI). The proposed algorithms yield better analytical and visual results.
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.
OPC recipe optimization using genetic algorithm
NASA Astrophysics Data System (ADS)
Asthana, Abhishek; Wilkinson, Bill; Power, Dave
2016-03-01
Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.
An investigation of messy genetic algorithms
NASA Technical Reports Server (NTRS)
Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley
1990-01-01
Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.
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.
NASA Astrophysics Data System (ADS)
Miyamae, Atsushi; Sakuma, Jun; Ono, Isao; Kobayashi, Shigenobu
The stabilization control of nonholonomic systems have been extensively studied because it is essential for nonholonomic robot control problems. The difficulty in this problem is that the theoretical derivation of control policy is not necessarily guaranteed achievable. In this paper, we present a reinforcement learning (RL) method with instance-based policy (IBP) representation, in which control policies for this class are optimized with respect to user-defined cost functions. Direct policy search (DPS) is an approach for RL; the policy is represented by parametric models and the model parameters are directly searched by optimization techniques including genetic algorithms (GAs). In IBP representation an instance consists of a state and an action pair; a policy consists of a set of instances. Several DPSs with IBP have been previously proposed. In these methods, sometimes fail to obtain optimal control policies when state-action variables are continuous. In this paper, we present a real-coded GA for DPSs with IBP. Our method is specifically designed for continuous domains. Optimization of IBP has three difficulties; high-dimensionality, epistasis, and multi-modality. Our solution is designed for overcoming these difficulties. The policy search with IBP representation appears to be high-dimensional optimization; however, instances which can improve the fitness are often limited to active instances (instances used for the evaluation). In fact, the number of active instances is small. Therefore, we treat the search problem as a low dimensional problem by restricting search variables only to active instances. It has been commonly known that functions with epistasis can be efficiently optimized with crossovers which satisfy the inheritance of statistics. For efficient search of IBP, we propose extended crossover-like mutation (extended XLM) which generates a new instance around an instance with satisfying the inheritance of statistics. For overcoming multi-modality, we
Genetic warfarin dosing: tables versus algorithms.
Finkelman, Brian S; Gage, Brian F; Johnson, Julie A; Brensinger, Colleen M; Kimmel, Stephen E
2011-02-01
The aim of this study was to compare the accuracy of genetic tables and formal pharmacogenetic algorithms for warfarin dosing. Pharmacogenetic algorithms based on regression equations can predict warfarin dose, but they require detailed mathematical calculations. A simpler alternative, recently added to the warfarin label by the U.S. Food and Drug Administration, is to use genotype-stratified tables to estimate warfarin dose. This table may potentially increase the use of pharmacogenetic warfarin dosing in clinical practice; however, its accuracy has not been quantified. A retrospective cohort study of 1,378 patients from 3 anticoagulation centers was conducted. Inclusion criteria were stable therapeutic warfarin dose and complete genetic and clinical data. Five dose prediction methods were compared: 2 methods using only clinical information (empiric 5 mg/day dosing and a formal clinical algorithm), 2 genetic tables (the new warfarin label table and a table based on mean dose stratified by genotype), and 1 formal pharmacogenetic algorithm, using both clinical and genetic information. For each method, the proportion of patients whose predicted doses were within 20% of their actual therapeutic doses was determined. Dosing methods were compared using McNemar's chi-square test. Warfarin dose prediction was significantly more accurate (all p < 0.001) with the pharmacogenetic algorithm (52%) than with all other methods: empiric dosing (37%; odds ratio [OR]: 2.2), clinical algorithm (39%; OR: 2.2), warfarin label (43%; OR: 1.8), and genotype mean dose table (44%; OR: 1.9). Although genetic tables predicted warfarin dose better than empiric dosing, formal pharmacogenetic algorithms were the most accurate. Copyright Â© 2011 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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.
Genetic Algorithms for Digital Quantum Simulations.
Las Heras, U; Alvarez-Rodriguez, U; Solano, E; Sanz, M
2016-06-10
We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.
Equilibrium stellar systems with genetic algorithms
NASA Astrophysics Data System (ADS)
Gularte, E.; Carpintero, D. D.
In 1979, M Schwarzschild showed that it is possible to build an equilibrium triaxial stellar system. However, the linear programmation used to that goal was not able to determine the uniqueness of the solution, nor even if that solution was the optimum one. Genetic algorithms are ideal tools to find a solution to this problem. In this work, we use a genetic algorithm to reproduce an equilibrium spherical stellar system from a suitable set of predefined orbits, obtaining the best solution attainable with the provided set. FULL TEXT IN SPANISH
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.
Naturally selecting solutions: the use of genetic algorithms in bioinformatics.
Manning, Timmy; Sleator, Roy D; Walsh, Paul
2013-01-01
For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
3D Protein structure prediction with genetic tabu search algorithm
2010-01-01
Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill
3D protein structure prediction with genetic tabu search algorithm.
Zhang, Xiaolong; Wang, Ting; Luo, Huiping; Yang, Jack Y; Deng, Youping; Tang, Jinshan; Yang, Mary Qu
2010-05-28
Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic
Automatic page layout using genetic algorithms for electronic albuming
NASA Astrophysics Data System (ADS)
Geigel, Joe; Loui, Alexander C. P.
2000-12-01
In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.
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.
MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
Predicting complex mineral structures using genetic algorithms.
Mohn, Chris E; Kob, Walter
2015-10-28
We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases.
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.
A systematic study of genetic algorithms with genotype editing
Huang, C. F.; Rocha, L. M.
2004-01-01
This paper presents our systematic study on an RNA-editing computational model of Genetic Algorithms (GA). This model is constructed based on several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed by [15]. The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally regulated. The systematic study of this RNA-editing model has shed some light into the evolutionary implications of RNA editing and how to select proper RNA editors for design of more robust GAS. The results will also show promising applications to complex real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation.
NASA Astrophysics Data System (ADS)
Abedini, M. J.; Nasseri, M.; Burn, D. H.
2012-04-01
In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.
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. Copyright © 2014 Elsevier Inc. All rights reserved.
Zhou, Hui; Vonk, Brenda; Roubos, Johannes A; Bovenberg, Roel A L; Voigt, Christopher A
2015-12-02
Optimizing bio-production involves strain and process improvements performed as discrete steps. However, environment impacts genotype and a strain that is optimal under one set of conditions may not be under different conditions. We present a methodology to simultaneously vary genetic and process factors, so that both can be guided by design of experiments (DOE). Advances in DNA assembly and gene insulation facilitate this approach by accelerating multi-gene pathway construction and the statistical interpretation of screening data. This is applied to a 6-aminocaproic acid (6-ACA) pathway in Escherichia coli consisting of six heterologous enzymes. A 32-member fraction factorial library is designed that simultaneously perturbs expression and media composition. This is compared to a 64-member full factorial library just varying expression (0.64 Mb of DNA assembly). Statistical analysis of the screening data from these libraries leads to different predictions as to whether the expression of enzymes needs to increase or decrease. Therefore, if genotype and media were varied separately this would lead to a suboptimal combination. This is applied to the design of a strain and media composition that increases 6-ACA from 9 to 48 mg/l in a single optimization step. This work introduces a generalizable platform to co-optimize genetic and non-genetic factors.
Zhou, Hui; Vonk, Brenda; Roubos, Johannes A.; Bovenberg, Roel A.L.; Voigt, Christopher A.
2015-01-01
Optimizing bio-production involves strain and process improvements performed as discrete steps. However, environment impacts genotype and a strain that is optimal under one set of conditions may not be under different conditions. We present a methodology to simultaneously vary genetic and process factors, so that both can be guided by design of experiments (DOE). Advances in DNA assembly and gene insulation facilitate this approach by accelerating multi-gene pathway construction and the statistical interpretation of screening data. This is applied to a 6-aminocaproic acid (6-ACA) pathway in Escherichia coli consisting of six heterologous enzymes. A 32-member fraction factorial library is designed that simultaneously perturbs expression and media composition. This is compared to a 64-member full factorial library just varying expression (0.64 Mb of DNA assembly). Statistical analysis of the screening data from these libraries leads to different predictions as to whether the expression of enzymes needs to increase or decrease. Therefore, if genotype and media were varied separately this would lead to a suboptimal combination. This is applied to the design of a strain and media composition that increases 6-ACA from 9 to 48 mg/l in a single optimization step. This work introduces a generalizable platform to co-optimize genetic and non-genetic factors. PMID:26519464
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.
Fashion sketch design by interactive genetic algorithms
NASA Astrophysics Data System (ADS)
Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.
2012-11-01
Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.
Predicting mining activity with parallel genetic algorithms
Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,
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.
Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?
NASA Astrophysics Data System (ADS)
Jones, Erika
2015-04-01
Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.
A biased random-key genetic algorithm for data clustering.
Festa, P
2013-09-01
Cluster analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneous and/or well separated. Starting from the 1990s, cluster analysis has been applied to several domains with numerous applications. It has emerged as one of the most exciting interdisciplinary fields, having benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biology, biochemistry, mathematics, and computer science. The last decade has brought several new algorithms, which are able to solve larger sized and real-world instances. We will give an overview of the main types of clustering and criteria for homogeneity or separation. Solution techniques are discussed, with special emphasis on the combinatorial optimization perspective, with the goal of providing conceptual insights and literature references to the broad community of clustering practitioners. A new biased random-key genetic algorithm is also described and compared with several efficient hybrid GRASP algorithms recently proposed to cluster biological data.
Genetic algorithm optimization of atomic clusters
Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E. |
1996-12-31
The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process.
Navigation Constellation Design Using a Multi-Objective Genetic Algorithm
2015-03-26
NAVIGATION CONSTELLATION DESIGN USING A MULTI-OBJECTIVE GENETIC ALGORITHM THESIS MARCH 2015...the United States. AFIT-ENY-MS-15-M-245 NAVIGATION CONSTELLATION DESIGN USING A MULTI-OBJECTIVE GENETIC ALGORITHM THESIS Presented to...DISTRIBUTION UNLIMITED. AFIT-ENY-MS-15-M-245 NAVIGATION CONSTELLATION DESIGN USING A MULTI-OBJECTIVE GENETIC ALGORITHM Heather C. Diniz
Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali
2013-04-01
The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.
PDoublePop: An implementation of parallel genetic algorithm for function optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Tzallas, Alexandros; Tsalikakis, Dimitris
2016-12-01
A software for the implementation of parallel genetic algorithms is presented in this article. The underlying genetic algorithm is aimed to locate the global minimum of a multidimensional function inside a rectangular hyperbox. The proposed software named PDoublePop implements a client-server model for parallel genetic algorithms with advanced features for the local genetic algorithms such as: an enhanced stopping rule, an advanced mutation scheme and periodical application of a local search procedure. The user may code the objective function either in C++ or in Fortran77. The method is tested on a series of well-known test functions and the results are reported.
NASA Astrophysics Data System (ADS)
Lee, J.; Ahn, J. B.
2016-12-01
-model Ensemble Method using Non-linear Genetic Algorithm: an Application to Boreal Winter Surface Air Temperature and Precipitation Prediction, J. Geophys. Res.
Genetic algorithms for adaptive real-time control in space systems
NASA Technical Reports Server (NTRS)
Vanderzijp, J.; Choudry, A.
1988-01-01
Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.
Optical Constants Determined by Genetic Algorithms
NASA Astrophysics Data System (ADS)
Smith, David Y.; Karstens, William; Malghani, Shaheen M.
2005-03-01
A recent determination^a of the complex refractive index, n(λ) + i κ(λ), of porous silicon employed a genetic^b algorithm to fit the Fresnel equations to reflectance spectra. The procedure appeared to involve more unknowns than explicit equations available for fitting, an indeterminate problem. However, the index values obtained were reasonable, and predicted the properties of porous-silicon multilayes. We have traced this success to the interpolation formulas used for n and κ in the fitting algorithm. They amount to an implicit optical-constant model with the de facto assumption of an analytic complex index that can be approximated by a cubic polynomial. Our analysis suggests the procedure can be improved by explicitly using a more appropriate model, e.g., one that uses wave number as the expansion variable and requires that n and κ be even and odd functions of λ, respectively. ^a V. Torres-Costa, R. J. Mart'in-Palma, and J. M. Mart'inez-Duart, J. Appl. Phys. 96, 4197 (2004). ^b D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, 1989).
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 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.
1992-12-01
model of schema growth for solution sharing(36) is refined and extended to predict the behavior of three solution sharing strategies. Experiments...complexity. GAs are inspired by and modeled after the processes of natural selection. The most commonly implemented GAs are "simple" GAs, which use three...solution. Efficiency is the al- gorithmic complexity of the algorithm. Current theory of genetic algorithm behavior is based on the assumptions of zero
Flexible ligand docking using a genetic algorithm
NASA Astrophysics Data System (ADS)
Oshiro, C. M.; Kuntz, I. D.; Dixon, J. Scott
1995-04-01
Two computational techniques have been developed to explore the orientational and conformational space of a flexible ligand within an enzyme. Both methods use the Genetic Algorithm (GA) to generate conformationally flexible ligands in conjunction with algorithms from the DOCK suite of programs to characterize the receptor site. The methods are applied to three enzyme-ligand complexes: dihydrofolate reductase-methotrexate, thymidylate synthase-phenolpthalein and HIV protease-thioketal haloperidol. Conformations and orientations close to the crystallographically determined structures are obtained, as well as alternative structures with low energy. The potential for the GA method to screen a database of compounds is also examined. A collection of ligands is evaluated simultaneously, rather than docking the ligands individually into the enzyme.
Genetic Algorithms for solving SVM-ICA
NASA Astrophysics Data System (ADS)
Górriz, J. M.; Puntonet, C. G.
2004-11-01
In this paper we solve the well known ill-posed problem called Independent Component Analysis using the Support Vector Machines (SVM) methodology and proposing a genetic algorithm to minimize a nonconvex and nonlinear cost function based on statistical estimators. In this way a novel method for blindly separating unobservable independent component signals from their linear and non linear (using mapping functions) mixtures is devised. The GA presented in this work is able to extract independent components with faster rate than the previous independent component analysis algorithms based on Higher Order Statistics (HOS) as input space dimension increases showing significant accuracy and robustness. Using a suitable mathematically notation we derive independent functions equivalent to the Maximum Entropy principle.
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.
Refined Genetic Algorithms for Polypeptide Structure Prediction.
1996-12-01
fo cused ev olution program. 2.5 Parallel Genetic Algorithms General information ab out parallel computing is a v ailable in App endix C. There are t w...to generate a more t \\feasible" candidate, and th us, escap e the lo cal minim a. T able 14. Final minim um energies (k cal/mol) for [Met]-enk...prop osed the tec hnique kno wn as ev olutionary programming. Ev olu- tionary programmi ng tries to generate computational biological ev olution
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.
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.
Rammal, Abbas; Perrin, Eric; Vrabie, Valeriu; Assaf, Rabih; Fenniri, Hassan
2017-07-01
Infrared spectroscopy provides useful information on the molecular compositions of biological systems related to molecular vibrations, overtones, and combinations of fundamental vibrations. Mid-infrared (MIR) spectroscopy is sensitive to organic and mineral components and has attracted growing interest in the development of biomarkers related to intrinsic characteristics of lignocellulose biomass. However, not all spectral information is valuable for biomarker construction or for applying analysis methods such as classification. Better processing and interpretation can be achieved by identifying discriminating wavenumbers. The selection of wavenumbers has been addressed through several variable- or feature-selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune, and others require additional information, such as concentrations. This paper proposes a new approach by combining a naïve Bayesian classifier with a genetic algorithm to identify discriminating spectral wavenumbers. The genetic algorithm uses a linear combination of an a posteriori probability and the Bayes error rate as the fitness function for optimization. Such a function allows the improvement of both the compactness and the separation of classes. This approach was tested to classify a small set of maize roots in soil according to their biodegradation process based on their MIR spectra. The results show that this optimization method allows better discrimination of the biodegradation process, compared with using the information of the entire MIR spectrum, the use of the spectral information at wavenumbers selected by a genetic algorithm based on a classical validity index or the use of the spectral information selected by combining a genetic algorithm with other methods, such as Linear Discriminant Analysis. The proposed method selects wavenumbers that correspond to principal vibrations of chemical functional groups of compounds that undergo degradation
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. *
A genetic algorithm for layered multisource video distribution
NASA Astrophysics Data System (ADS)
Cheok, Lai-Tee; Eleftheriadis, Alexandros
2005-03-01
We propose a genetic algorithm -- MckpGen -- for rate scaling and adaptive streaming of layered video streams from multiple sources in a bandwidth-constrained environment. A genetic algorithm (GA) consists of several components: a representation scheme; a generator for creating an initial population; a crossover operator for producing offspring solutions from parents; a mutation operator to promote genetic diversity and a repair operator to ensure feasibility of solutions produced. We formulated the problem as a Multiple-Choice Knapsack Problem (MCKP), a variant of Knapsack Problem (KP) and a decision problem in combinatorial optimization. MCKP has many successful applications in fault tolerance, capital budgeting, resource allocation for conserving energy on mobile devices, etc. Genetic algorithms have been used to solve NP-complete problems effectively, such as the KP, however, to the best of our knowledge, there is no GA for MCKP. We utilize a binary chromosome representation scheme for MCKP and design and implement the components, utilizing problem-specific knowledge for solving MCKP. In addition, for the repair operator, we propose two schemes (RepairSimple and RepairBRP). Results show that RepairBRP yields significantly better performance. We further show that the average fitness of the entire population converges towards the best fitness (optimal) value and compare the performance at various bit-rates.
Threshold matrix for digital halftoning by genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero
1998-10-01
Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.
Optimisation of nonlinear motion cueing algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Asadi, Houshyar; Mohamed, Shady; Rahim Zadeh, Delpak; Nahavandi, Saeid
2015-04-01
Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching
Genetic algorithm identification of alternative sensor parameter sets for monitoring
NASA Astrophysics Data System (ADS)
Ramers, Douglas L.
2003-08-01
We are faced with the problem of identifying and selecting the most significant data sources in developing monitoring applications for which data from a variety of sensors are available. We may also be concerned with identifying suitable alternative data sources when a preferred sensor may be temporarily unavailable or unreliable. This work describes how genetic algorithms (GA) were used to select useful sets of parameters from sensors and implicit knowledge to construct artificial neural networks to detect levels of chlorophyll-a in the Neuse River. The available parameters included six multispectral bands of Landsat imagery, chemical data (temperature, pH, salinity), and knowledge implicit in location and season. Experiments were conducted to determine which parameters the genetic algorithms would select based on the availability of other parameters, e.g., which parameter would be chosen when temperature wasn't available as compared to when near infrared data was not available.
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 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.
Instrument design and optimization using genetic algorithms
Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter
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.
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.
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 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.
An improved genetic algorithm with dynamic topology
NASA Astrophysics Data System (ADS)
Cai, Kai-Quan; Tang, Yan-Wu; Zhang, Xue-Jun; Guan, Xiang-Min
2016-12-01
The genetic algorithm (GA) is a nature-inspired evolutionary algorithm to find optima in search space via the interaction of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topologies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence. Project supported by the National Natural Science Foundation for Young Scientists of China (Grant No. 61401011), the National Key Technologies R & D Program of China (Grant No. 2015BAG15B01), and the National Natural Science Foundation of China (Grant No. U1533119).
Multiple source localization using genetic algorithms.
McNay, D; Michielssen, E; Rogers, R L; Taylor, S A; Akhtari, M; Sutherling, W W
1996-02-01
We present a new procedure for localizing simultaneously active multiple brain sources that overlap in both space and time on EEG recordings. The source localization technique was based on a spatio-temporal model and a genetic algorithm search routine. The method was successfully applied to the localization of two dipole sources from several sets of simulated potentials with various signal-to-noise ratios (SNR). The different SNR values resembled evoked responses and epileptic spikes as commonly seen in the laboratory. Results of the simulation studies yielded localization accuracy ranging from 0.01 to 0.07 cm with an SNR of 10; from 0.02 to 0.26 cm with an SNR of 5; and from 0.06 to 0.73 cm when the SNR was equal to 2. Additionally, two sets of simulations were based on the dipole arrangements and time activities of data obtained during electrical stimulation of the median nerve in human subjects. These studies yielded localization accuracy within 0.1 cm. We also studied the localization accuracy of the algorithm using a physical model incorporating potential measurements of two current dipoles embedded in a sphere. In this situation the algorithm was successful in localizing the two simultaneously active sources to within 0.07-0.15 cm.
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
Investigation for digital speckle correlation method based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Ting; Chen, Haoyuan; Yang, Xiankun; Zheng, Xitao
2011-10-01
A digital speckle correlation method (DSCM) to measure surface displacements and strains have been developed. The correlate search algorithm plays an important role in DSCM. The traditional correlate search algorithms depend on initial value selection seriously. For this defect, this paper presents DSCM which uses genetic algorithm (GA) to measure surface deformation. In order to overcome the premature convergence of tradition genetic algorithm, this paper utilizes multi-parent crossover and adaptive mutation probability to perform genetic operation. The simulation demonstrates that improved GA increases the speed and the precision of search. With the application of DSCM in measuring deformation of composite laminates, the test reveals an agreement with the anticipated result.
High-Speed General Purpose Genetic Algorithm Processor.
Hoseini Alinodehi, Seyed Pourya; Moshfe, Sajjad; Saber Zaeimian, Masoumeh; Khoei, Abdollah; Hadidi, Khairollah
2016-07-01
In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in [Formula: see text] process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.
Saffar, Saber; Abdullah, Amir
2012-01-01
The effective ultrasonic energy radiation into the air of piezoelectric transducers requires using multilayer matching systems with accurately selected acoustic impedances and the thickness of particular layers. One major problem of ultrasonic transducers, radiating acoustic energy into air, is to find the proper acoustic impedances of one or more matching layers. This work aims at developing an original solution to the acoustic impedance mismatch between transducer and air. If the acoustic impedance defences between transducer and air be more, then finding best matching layer(s) is harder. Therefore we consider PZT (lead zirconate titanate piezo electric) transducer and air that has huge acoustic impedance deference. The vibration source energy (PZT), which is used to generate the incident wave, consumes a part of the mechanical energy and converts it to an electrical one in theoretical calculation. After calculating matching layers, we consider the energy source as layer to design a transducer. However, this part of the mechanical energy will be neglected during the mathematical work. This approximation is correct only if the transducer is open-circuit. Since the possibilities of choosing material with required acoustic impedance are limited (the counted values cannot always be realized and applied in practice) it is necessary to correct the differences between theoretical values and the possibilities of practical application of given acoustic impedances. Such a correction can be done by manipulating other parameters of matching layers (e.g. by changing their thickness). The efficiency of the energy transmission from the piezoceramic transducer through different layers with different thickness and different attenuation enabling a compensation of non-ideal real values by changing their thickness was computer analyzed (base on genetic algorithm). Firstly, three theoretical solutions were investigated. Namely, Chebyshev, Desilets and Souquet theories. However, the
2011-01-01
Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA. PMID:21269434
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
Self-calibration of a noisy multiple-sensor system with genetic algorithms
NASA Astrophysics Data System (ADS)
Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua
1996-01-01
This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.
Training product unit neural networks with genetic algorithms
NASA Technical Reports Server (NTRS)
Janson, D. J.; Frenzel, J. F.; Thelen, D. C.
1991-01-01
The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.
Genetic Algorithms as a Tool for Phased Array Radar Design
2002-06-01
NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS Approved for public release; distribution is unlimited. GENETIC ALGORITHMS AS A...REPORT DATE June 2002 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE: Genetic Algorithms as a Tool for Phased Array Radar...creative ways to design multi-function phased array radars. This thesis proposes that Genetic Algorithms, computer programs that mimic natural selection
Convergence Analysis of Genetic Algorithms for Topology Control in MANETs
2009-01-01
tering algorithm in mobile ad hoc networks using genetic algorith - mic approach,” in Prof. of the Global Telecommunications Conference (GLOBECOM...Convergence Analysis of Genetic Algorithms for Topology Control in MANETs Cem Şafak Şahin, Stephen Gundry, Elkin Urrea, M. Ümit Uyar, Michael...Christian.Pizzo@us.army.mil Abstract—We describe and verify convergence properties of our forced-based genetic algorithm (FGA) as a decentralized topology
van der Lee, J H; Svrcek, W Y; Young, B R
2008-01-01
Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.
Band-structure parameters by genetic algorithm
Starrost, F.; Bornholdt, S.; Solterbeck, C.; Schattke, W.
1996-05-01
A genetic algorithm has been used to solve a complex multidimensional parameter-fitting problem. We will focus on the parameters of an empirical tight-binding Hamiltonian. The method is used to approximate the electronic energy band structure if energy values are known for a few wave vectors of high symmetry. Compared to the usual manual procedure this method is more accurate and automatic. This approach, based on the extended H{umlt u}ckel theory (EHT), has provided a list of EHT parameters for IV-IV and III-V semiconductors with zinc-blende structure and helped us to find a symmetry in the EHT. {copyright} {ital 1996 The American Physical Society.}
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.
Optimized dynamical decoupling via genetic algorithms
NASA Astrophysics Data System (ADS)
Quiroz, Gregory; Lidar, Daniel A.
2013-11-01
We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
SAGA: sequence alignment by genetic algorithm.
Notredame, C; Higgins, D G
1996-01-01
We describe a new approach to multiple sequence alignment using genetic algorithms and an associated software package called SAGA. The method involves evolving a population of alignments in a quasi evolutionary manner and gradually improving the fitness of the population as measured by an objective function which measures multiple alignment quality. SAGA uses an automatic scheduling scheme to control the usage of 22 different operators for combining alignments or mutating them between generations. When used to optimise the well known sums of pairs objective function, SAGA performs better than some of the widely used alternative packages. This is seen with respect to the ability to achieve an optimal solution and with regard to the accuracy of alignment by comparison with reference alignments based on sequences of known tertiary structure. The general attraction of the approach is the ability to optimise any objective function that one can invent. PMID:8628686
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.
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.
Zheng, Zi-Yi; Guo, Xiao-Na; Zhu, Ke-Xue; Peng, Wei; Zhou, Hui-Ming
2017-07-15
Methoxy-ρ-benzoquinone (MBQ) and 2, 6-dimethoxy-ρ-benzoquinone (DMBQ) are two potential anticancer compounds in fermented wheat germ. In present study, modeling and optimization of added macronutrients, microelements, vitamins for producing MBQ and DMBQ was investigated using artificial neural network (ANN) combined with genetic algorithm (GA). A configuration of 16-11-1 ANN model with Levenberg-Marquardt training algorithm was applied for modeling the complicated nonlinear interactions among 16 nutrients in fermentation process. Under the guidance of optimized scheme, the total contents of MBQ and DMBQ was improved by 117% compared with that in the control group. Further, by evaluating the relative importance of each nutrient in terms of the two benzoquinones' yield, macronutrients and microelements were found to have a greater influence than most of vitamins. It was also observed that a number of interactions between nutrients affected the yield of MBQ and DMBQ remarkably. Copyright © 2017 Elsevier Ltd. All rights reserved.
GAMPMS: Genetic algorithm managed peptide mutant screening.
Long, Thomas; McDougal, Owen M; Andersen, Tim
2015-06-30
The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor-ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure-based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) α3β2-isoform with a library of 64,000 α-conotoxin (α-CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α-CTx MII peptide mutants with the α3β2-nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 α-CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening.
Kumar, A.; Roy, G.G.
2005-12-01
A novel methodology has been developed to calculate the kinetic parameters associated with reduction of ore-coal composite mixtures and to describe the time course of reduction of hematite to iron. The empirical parameters, namely, the three sets of activation energies and frequency factors, have been estimated by employing an evolutionary optimization tool, the genetic algorithm (GA). The model prediction matches well with the experimental literature data. The estimated activation energies are higher than the corresponding intrinsic values, indicating the role of heat transfer in the process.
A novel mating approach for genetic algorithms.
Galán, Severino F; Mengshoel, Ole J; Pinter, Rafael
2013-01-01
Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.
Optimizing doped libraries by using genetic algorithms
NASA Astrophysics Data System (ADS)
Tomandl, Dirk; Schober, Andreas; Schwienhorst, Andreas
1997-01-01
The insertion of random sequences into protein-encoding genes in combination with biologicalselection techniques has become a valuable tool in the design of molecules that have usefuland possibly novel properties. By employing highly effective screening protocols, a functionaland unique structure that had not been anticipated can be distinguished among a hugecollection of inactive molecules that together represent all possible amino acid combinations.This technique is severely limited by its restriction to a library of manageable size. Oneapproach for limiting the size of a mutant library relies on `doping schemes', where subsetsof amino acids are generated that reveal only certain combinations of amino acids in a proteinsequence. Three mononucleotide mixtures for each codon concerned must be designed, suchthat the resulting codons that are assembled during chemical gene synthesis represent thedesired amino acid mixture on the level of the translated protein. In this paper we present adoping algorithm that `reverse translates' a desired mixture of certain amino acids into threemixtures of mononucleotides. The algorithm is designed to optimally bias these mixturestowards the codons of choice. This approach combines a genetic algorithm with localoptimization strategies based on the downhill simplex method. Disparate relativerepresentations of all amino acids (and stop codons) within a target set can be generated.Optional weighing factors are employed to emphasize the frequencies of certain amino acidsand their codon usage, and to compensate for reaction rates of different mononucleotidebuilding blocks (synthons) during chemical DNA synthesis. The effect of statistical errors thataccompany an experimental realization of calculated nucleotide mixtures on the generatedmixtures of amino acids is simulated. These simulations show that the robustness of differentoptima with respect to small deviations from calculated values depends on their concomitantfitness. Furthermore
Optimizing doped libraries by using genetic algorithms.
Tomandl, D; Schober, A; Schwienhorst, A
1997-01-01
The insertion of random sequences into protein-encoding genes in combination with biological selection techniques has become a valuable tool in the design of molecules that have useful and possibly novel properties. By employing highly effective screening protocols, a functional and unique structure that had not been anticipated can be distinguished among a huge collection of inactive molecules that together represent all possible amino acid combinations. This technique is severely limited by its restriction to a library of manageable size. One approach for limiting the size of a mutant library relies on 'doping schemes', where subsets of amino acids are generated that reveal only certain combinations of amino acids in a protein sequence. Three mononucleotide mixtures for each codon concerned must be designed, such that the resulting codons that are assembled during chemical gene synthesis represent the desired amino acid mixture on the level of the translated protein. In this paper we present a doping algorithm that "reverse translates' a desired mixture of certain amino acids into three mixtures of mononucleotides. The algorithm is designed to optimally bias these mixtures towards the codons of choice. This approach combines a genetic algorithm with local optimization strategies based on the downhill simplex method. Disparate relative representations of all amino acids (and stop codons) within a target set can be generated. Optional weighing factors are employed to emphasize the frequencies of certain amino acids and their codon usage, and to compensate for reaction rates of different mononucleotide building blocks (synthons) during chemical DNA synthesis. The effect of statistical errors that accompany an experimental realization of calculated nucleotide mixtures on the generated mixtures of amino acids is simulated. These simulations show that the robustness of different optima with respect to small deviations from calculated values depends on their concomitant
A Multi-Objective Genetic Algorithm for Outlier Removal.
Nahum, Oren E; Yosipof, Abraham; Senderowitz, Hanoch
2015-12-28
Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.
Geçen, Nazmiye; Sarıpınar, Emin; Yanmaz, Ersin; Sahin, Kader
2012-01-01
Two different approaches, namely the electron conformational and genetic algorithm methods (EC-GA), were combined to identify a pharmacophore group and to predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors. To identify the pharmacophore, electron conformational matrices of congruity (ECMC)-which include atomic charges as diagonal elements and bond orders and interatomic distances as off-diagonal elements-were arranged for all compounds. The ECMC of the compound with the highest activity was chosen as a template and compared with the ECMCs of other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA) that refers to the pharmacophore. The genetic algorithm was employed to search for the best subset of parameter combinations that contributes the most to activity. Applying the model with the optimum 10 parameters to training (50 compounds) and test (22 compounds) sets gave satisfactory results (R(2)(training)= 0.848, R(2)(test))= 0.904, with a cross-validated q(2) = 0.780).
Parameterization of interatomic potential by genetic algorithms: A case study
Ghosh, Partha S. Arya, A.; Dey, G. K.; Ranawat, Y. S.
2015-06-24
A framework for Genetic Algorithm based methodology is developed to systematically obtain and optimize parameters for interatomic force field functions for MD simulations by fitting to a reference data base. This methodology is applied to the fitting of ThO{sub 2} (CaF{sub 2} prototype) – a representative of ceramic based potential fuel for nuclear applications. The resulting GA optimized parameterization of ThO{sub 2} is able to capture basic structural, mechanical, thermo-physical properties and also describes defect structures within the permissible range.
Optimization of broadband semiconductor chirped mirrors with genetic algorithm
NASA Astrophysics Data System (ADS)
Dems, Maciej; Wnuk, Paweł; Wasylczyk, Piotr; Zinkiewicz, Łukasz; Wójcik-Jedlińska, Anna; Regiński, Kazimierz; Hejduk, Krzysztof; Jasik, Agata
2016-10-01
Genetic algorithm was applied for optimization of dispersion properties in semiconductor Bragg reflectors for applications in femtosecond lasers. Broadband, large negative group-delay dispersion was achieved in the optimized design: The group-delay dispersion (GDD) as large as -3500 fs2 was theoretically obtained over a 10-nm bandwidth. The designed structure was manufactured and tested, providing GDD -3320 fs2 over a 7-nm bandwidth. The mirror performance was verified in semiconductor structures grown with molecular beam epitaxy. The mirror was tested in a passively mode-locked Yb:KYW laser.
Synthesis design of artificial magnetic metamaterials using a genetic algorithm.
Chen, P Y; Chen, C H; Wang, H; Tsai, J H; Ni, W X
2008-08-18
In this article, we present a genetic algorithm (GA) as one branch of artificial intelligence (AI) for the optimization-design of the artificial magnetic metamaterial whose structure is automatically generated by computer through the filling element methodology. A representative design example, metamaterials with permeability of negative unity, is investigated and the optimized structures found by the GA are presented. It is also demonstrated that our approach is effective for the synthesis of functional magnetic and electric metamaterials with optimal structures. This GA-based optimization-design technique shows great versatility and applicability in the design of functional metamaterials.
Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience
NASA Astrophysics Data System (ADS)
Li, Rui; Alvarez-Rodriguez, Unai; Lamata, Lucas; Solano, Enrique
2017-07-01
It has been proven that quantum adders are forbidden by the laws of quantum mechanics. We analyze theoretical proposals for the implementation of approximate quantum adders and optimize them by means of genetic algorithms, improving previous protocols in terms of efficiency and fidelity. Furthermore, we experimentally realize a suitable approximate quantum adder with the cloud quantum computing facilities provided by IBM Quantum Experience. The development of approximate quantum adders enhances the toolbox of quantum information protocols, paving the way for novel applications in quantum technologies.
A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates
ERIC Educational Resources Information Center
Venables, Anne; Tan, Grace
2007-01-01
Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…
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 "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates
ERIC Educational Resources Information Center
Venables, Anne; Tan, Grace
2007-01-01
Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…
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…
Automatic image generation by genetic algorithms for testing halftoning methods
NASA Astrophysics Data System (ADS)
Mantere, Timo J.; Alander, Jarmo T.
2000-10-01
Automatic test image generation by genetic algorithms is introduced in this work. In general the proposed method has potential in functional software testing. This study was done by joining two different projects: the first one concentrates on software test data generation by genetic algorithms and the second one studied digital halftoning for an ink jet marking machine also by genetic algorithm optimization. The object software halftones images with different image filters. The goal was to reveal, if genetic algorithm is able to generate images that re difficult for the object software to halftone, in other words to find if some prominent characteristics of the original image disappear or ghost images appear due to the halftoning process. The preliminary results showed that genetic algorithm is able to find images that are considerable changed when halftoned, and thus reveal potential problems with the halftoning method, i.e. essentially tests for errors in the halftoning software.
Optimal design of link systems using successive zooming genetic algorithm
NASA Astrophysics Data System (ADS)
Kwon, Young-Doo; Sohn, Chang-hyun; Kwon, Soon-Bum; Lim, Jae-gyoo
2009-07-01
Link-systems have been around for a long time and are still used to control motion in diverse applications such as automobiles, robots and industrial machinery. This study presents a procedure involving the use of a genetic algorithm for the optimal design of single four-bar link systems and a double four-bar link system used in diesel engine. We adopted the Successive Zooming Genetic Algorithm (SZGA), which has one of the most rapid convergence rates among global search algorithms. The results are verified by experiment and the Recurdyn dynamic motion analysis package. During the optimal design of single four-bar link systems, we found in the case of identical input/output (IO) angles that the initial and final configurations show certain symmetry. For the double link system, we introduced weighting factors for the multi-objective functions, which minimize the difference between output angles, providing balanced engine performance, as well as the difference between final output angle and the desired magnitudes of final output angle. We adopted a graphical method to select a proper ratio between the weighting factors.
Optimization of Circular Ring Microstrip Antenna Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Sathi, V.; Ghobadi, Ch.; Nourinia, J.
2008-10-01
Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity model analysis along with the genetic optimization algorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimization algorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.
A New Challenge for Compression Algorithms: Genetic Sequences.
ERIC Educational Resources Information Center
Grumbach, Stephane; Tahi, Fariza
1994-01-01
Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…
A New Challenge for Compression Algorithms: Genetic Sequences.
ERIC Educational Resources Information Center
Grumbach, Stephane; Tahi, Fariza
1994-01-01
Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Hajima, Ryoichi
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
Optimisation of assembly scheduling in VCIM systems using genetic algorithm
NASA Astrophysics Data System (ADS)
Dao, Son Duy; Abhary, Kazem; Marian, Romeo
2017-01-01
Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.
Optimisation of assembly scheduling in VCIM systems using genetic algorithm
NASA Astrophysics Data System (ADS)
Dao, Son Duy; Abhary, Kazem; Marian, Romeo
2017-01-01
Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.
Sensor network algorithms and applications.
Trigoni, Niki; Krishnamachari, Bhaskar
2012-01-13
A sensor network is a collection of nodes with processing, communication and sensing capabilities deployed in an area of interest to perform a monitoring task. There has now been about a decade of very active research in the area of sensor networks, with significant accomplishments made in terms of both designing novel algorithms and building exciting new sensing applications. This Theme Issue provides a broad sampling of the central challenges and the contributions that have been made towards addressing these challenges in the field, and illustrates the pervasive and central role of sensor networks in monitoring human activities and the environment.
Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-03-01
When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.
Robot path planning using a genetic algorithm
NASA Technical Reports Server (NTRS)
Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu
1988-01-01
Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.
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.
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.
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.
Protein complexes predictions within protein interaction networks using genetic algorithms.
Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz
2016-07-25
Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .
Multiobjective Economic Load Dispatch in 3-D Space by Genetic Algorithm
NASA Astrophysics Data System (ADS)
Jain, N. K.; Nangia, Uma; Singh, Iqbal
2017-08-01
This paper presents the application of genetic algorithm to Multiobjective Economic Load Dispatch (MELD) problem considering fuel cost, transmission losses and environmental pollution as objective functions. The MELD problem has been formulated using constraint method. The non-inferior set for IEEE 5, 14 and 30-bus system has been generated by using genetic algorithm and the target point has been obtained by using maximization of minimum relative attainments.
Kaur, Jaspreet; Nygren, Anders; Vigmond, Edward J
2014-01-01
Fitting parameter sets of non-linear equations in cardiac single cell ionic models to reproduce experimental behavior is a time consuming process. The standard procedure is to adjust maximum channel conductances in ionic models to reproduce action potentials (APs) recorded in isolated cells. However, vastly different sets of parameters can produce similar APs. Furthermore, even with an excellent AP match in case of single cell, tissue behaviour may be very different. We hypothesize that this uncertainty can be reduced by additionally fitting membrane resistance (Rm). To investigate the importance of Rm, we developed a genetic algorithm approach which incorporated Rm data calculated at a few points in the cycle, in addition to AP morphology. Performance was compared to a genetic algorithm using only AP morphology data. The optimal parameter sets and goodness of fit as computed by the different methods were compared. First, we fit an ionic model to itself, starting from a random parameter set. Next, we fit the AP of one ionic model to that of another. Finally, we fit an ionic model to experimentally recorded rabbit action potentials. Adding the extra objective (Rm, at a few voltages) to the AP fit, lead to much better convergence. Typically, a smaller MSE (mean square error, defined as the average of the squared error between the target AP and AP that is to be fitted) was achieved in one fifth of the number of generations compared to using only AP data. Importantly, the variability in fit parameters was also greatly reduced, with many parameters showing an order of magnitude decrease in variability. Adding Rm to the objective function improves the robustness of fitting, better preserving tissue level behavior, and should be incorporated.
NASA Astrophysics Data System (ADS)
Bezada, Maximiliano J.; Zelt, Colin A.
2011-05-01
Crustal density models derived from seismic velocity models by means of velocity-density conversions typically reproduce the main features of the observed gravity anomaly over the area but often show significant misfits. Given the uncertainty in the relationship between velocity and density, seismically derived density models should be regarded as an initial estimate of the true subsurface density structure. In this paper, we present a method for estimating the adjustments necessary to a seismically derived density model to improve the fit to gravity data. The method combines the Genetic Algorithm paradigm with linear inversion as a way to approach the non-linear and linear aspects of the problem. The models are divided into three layers representing the sedimentary column, the crystalline crust and the lithospheric mantle; the depths of these layers are determined from the seismic velocity model. Each of the layers is divided into a number of provinces and a density adjustment (Δρ) value is found for each province so that the residual gravity (difference between the observed gravity anomaly and the anomaly calculated for the seismically derived model) is minimized while keeping Δρ between predefined bounds. The preferred position of the province boundaries is found through the artificial evolution of a population of solutions. Given the stochastic nature of the algorithm and the non-uniqueness of the problem, different realizations can yield different solutions. By performing multiple realizations we can analyse a set of solutions by taking their mean and standard deviation, providing not only an estimate of the Δρ distribution in the subsurface but also an estimate of the associated uncertainty. Synthetic tests prove the ability of the algorithm to accurately recover the location of province boundaries and the Δρ values for a known model when using noise-free synthetic data. When noise is added to the data, the algorithm broadly recovers the features that
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
Feline Genetics: Clinical Applications and Genetic Testing
Lyons, Leslie A.
2010-01-01
DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately thirty-three genes contain fifty mutations that cause feline health problems or alterations in the cat’s appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab using a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat’s internal genome. PMID:21147473
Feline genetics: clinical applications and genetic testing.
Lyons, Leslie A
2010-11-01
DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately 33 genes contain 50 mutations that cause feline health problems or alterations in the cat's appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab with a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat's internal genome. Copyright © 2010 Elsevier Inc. All rights reserved.
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.
NASA Astrophysics Data System (ADS)
Wang, Zhenwu; Hut, Rolf; van de Giesen, Nick
2017-04-01
Particle filtering is a nonlinear and non-Gaussian dynamical filtering system. It has found widespread applications in hydrological data assimilation. In order to solve the loss of particle diversity exiting in resampling process of particle filter, this research proposes an improved particle filter algorithm using genetic algorithm optimization and Gamma test. This method combines the genetic algorithm and Gamma test into the resampling procedure of particle filter to improve the adaptability and performance of particle filter in data assimilation. First, the particles are classified to three different groups based on resampling method. The particles with high weight values remain unchanged. Then genetic algorithm is used to cross and variate the rest of the particles. In the process of the optimization, the Gamma test method is applied for monitoring the quality of the new generated particles. When the gamma statistic stays stable, the algorithm will end the optimization and continue to perturb next observations in particle algorithm. The algorithm is illustrated for the three-dimensional Lorenz model and the much more complex 40-dimensional Lorenz model. The results demonstrate this method can keep the diversity of the particles and enhance the performance of the particle filter, leading to the promising conjecture that the method is applicable to realistic hydrological problems.
Mobile robot dynamic path planning based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Yong; Zhou, Heng; Wang, Ying
2017-08-01
In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.
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.
Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms.
Jaffe, Jacob D; Feeney, Caitlin M; Patel, Jinal; Lu, Xiaodong; Mani, D R
2016-11-01
Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.
Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Jaffe, Jacob D.; Feeney, Caitlin M.; Patel, Jinal; Lu, Xiaodong; Mani, D. R.
2016-11-01
Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques.
Absolute GPS Positioning Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Ramillien, G.
A new inverse approach for restoring the absolute coordinates of a ground -based station from three or four observed GPS pseudo-ranges is proposed. This stochastic method is based on simulations of natural evolution named genetic algorithms (GA). These iterative procedures provide fairly good and robust estimates of the absolute positions in the Earth's geocentric reference system. For comparison/validation, GA results are compared to the ones obtained using the classical linearized least-square scheme for the determination of the XYZ location proposed by Bancroft (1985) which is strongly limited by the number of available observations (i.e. here, the number of input pseudo-ranges must be four). The r.m.s. accuracy of the non -linear cost function reached by this latter method is typically ~10-4 m2 corresponding to ~300-500-m accuracies for each geocentric coordinate. However, GA can provide more acceptable solutions (r.m.s. errors < 10-5 m2), even when only three instantaneous pseudo-ranges are used, such as a lost of lock during a GPS survey. Tuned GA parameters used in different simulations are N=1000 starting individuals, as well as Pc=60-70% and Pm=30-40% for the crossover probability and mutation rate, respectively. Statistical tests on the ability of GA to recover acceptable coordinates in presence of important levels of noise are made simulating nearly 3000 random samples of erroneous pseudo-ranges. Here, two main sources of measurement errors are considered in the inversion: (1) typical satellite-clock errors and/or 300-metre variance atmospheric delays, and (2) Geometrical Dilution of Precision (GDOP) due to the particular GPS satellite configuration at the time of acquisition. Extracting valuable information and even from low-quality starting range observations, GA offer an interesting alternative for high -precision GPS positioning.
An Intelligent Model for Pairs Trading Using Genetic Algorithms.
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236
Genetic-algorithm cancellation of sinusoidal powerline interference in electrocardiograms.
Kumaravel, N; Nithiyanandam, N
1998-03-01
The paper describes a method, based on a genetic algorithm, to remove sinusoidal powerline interference in electrocardiograms. There is a report on the use of the genetic algorithm to remove powerline interference for two different types of interference, powerline interference with frequency drift, and interference with frequency drift as well as third- harmonic distortion. The studies are conducted on electrocardiograms with simulated interference and also on actual noisy electrocardiogram records. The results obtained using the genetic algorithm in these cases of interference are presented.
Restart-Based Genetic Algorithm for the Quadratic Assignment Problem
NASA Astrophysics Data System (ADS)
Misevicius, Alfonsas
The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a "restart-based genetic algorithm" (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.
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.
Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)
NASA Astrophysics Data System (ADS)
Xin-ran, Li; Xin, Wang
2017-04-01
When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.
Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity
Louis, S.J.; Raines, G.L.
2003-01-01
We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.
Modelling ionosphere using combined memory based and genetic algorithms over the Europe region.
NASA Astrophysics Data System (ADS)
Tomasik, L.
2009-04-01
The state of the ionosphere depends on space weather factors like the intensity of solar activity, magnetic activity, local time, season, unfortunately the dependence is not thoroughly known. Memory based method like K Nearest Neighbor algorithm allows predictions by the similarity between ionosphere states. In mostly the nearest neighbour method is a case based on statistical approach used to predict the ionospheric conditions. In this case part of KNN algorithm was modificated by genetic programming. Genetic programming is a method that permit to automatically making algorithms knowing only general definition of a problem. The paper presents the using KNN whit genetic core algorithm in space weather applications and the use of this method for forecasting and mapping of the ionosphere characteristics, as foF2, M(3000)F2. The proposed algorithm is demonstrating for European area.
A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification
NASA Astrophysics Data System (ADS)
Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.
MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.
The convergence analysis of parallel genetic algorithm based on allied strategy
NASA Astrophysics Data System (ADS)
Lin, Feng; Sun, Wei; Chang, K. C.
2010-04-01
Genetic algorithms (GAs) have been applied to many difficult optimization problems such as track assignment and hypothesis managements for multisensor integration and data fusion. However, premature convergence has been a main problem for GAs. In order to prevent premature convergence, we introduce an allied strategy based on biological evolution and present a parallel Genetic Algorithm with the allied strategy (PGAAS). The PGAAS can prevent premature convergence, increase the optimization speed, and has been successfully applied in a few applications. In this paper, we first present a Markov chain model in the PGAAS. Based on this model, we analyze the convergence property of PGAAS. We then present the proof of global convergence for the PGAAS algorithm. The experiments results show that PGAAS is an efficient and effective parallel Genetic algorithm. Finally, we discuss several potential applications of the proposed methodology.
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.
Optimization of computer-generated binary holograms using genetic algorithms
NASA Astrophysics Data System (ADS)
Cojoc, Dan; Alexandrescu, Adrian
1999-11-01
The aim of this paper is to compare genetic algorithms against direct point oriented coding in the design of binary phase Fourier holograms, computer generated. These are used as fan-out elements for free space optical interconnection. Genetic algorithms are optimization methods which model the natural process of genetic evolution. The configuration of the hologram is encoded to form a chromosome. To start the optimization, a population of different chromosomes randomly generated is considered. The chromosomes compete, mate and mutate until the best chromosome is obtained according to a cost function. After explaining the operators that are used by genetic algorithms, this paper presents two examples with 32 X 32 genes in a chromosome. The crossover type and the number of mutations are shown to be important factors which influence the convergence of the algorithm. GA is demonstrated to be a useful tool to design namely binary phase holograms of complicate structures.
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.
Superscattering of light optimized by a genetic algorithm
Mirzaei, Ali Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.
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.
Shape Optimization of Cochlear Implant Electrode Array Using Genetic Algorithms
2007-11-02
Shape Optimization of Cochlear Implant Electrode Array using Genetic Algorithms Charles T.M. Choi, Ph.D., senior member, IEEE Department of...c.t.choi@ieee.org Abstract−Finite element analysis is used to compute the current distribution of the human cochlea during cochlear implant electrical...stimulation. Genetic algorithms are then applied in conjunction with the finite element analysis to optimize the shape of cochlear implant electrode array
Genetic-Algorithm Tool For Search And Optimization
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven
1995-01-01
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
Internal quantum efficiency analysis of solar cell by genetic algorithm
Xiong, Kanglin; Yang, Hui; Lu, Shulong; Zhou, Taofei; Wang, Rongxin; Qiu, Kai; Dong, Jianrong; Jiang, Desheng
2010-11-15
To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of genetic algorithm is illustrated. (author)
A novel genetic algorithm for k-LCS
NASA Astrophysics Data System (ADS)
Zheng, Li; Yang, Guoyu; Zhang, Rui
2017-08-01
A new fitness function model is designed, which considers that the outstanding sequence must be a common subsequence and the longer the better. However, the sequence which is not a common subsequence should be eliminated. Then, a novel genetic algorithm is proposed and described in details. Finally, the experimental results show that the new fitness function associated with the novel genetic algorithm can find out better solution.
Carneiro, Renato L; Braga, Jez W B; Bottoli, Carla B G; Poppi, Ronei J
2007-07-09
A variable selection methodology based on genetic algorithm (GA) was applied in a bilinear least squares model (BLLS) with second-order advantage, in three distinct situations, for determination by HPLC-DAD of the pesticides carbaryl (CBL), methyl thiophanate (TIO), simazin (SIM) and dimethoate (DMT) and the metabolite phthalimide (PTA) in wine. The chromatographic separation was carried out using an isocratic elution with 50:50 (v/v) acetonitrile:water as mobile phase. Preprocessing methods were performed for correcting the chromatographic time shifts, baseline variation and background. The optimization by GA provided a significant reduction of the errors, where for SIM and PTA a decrease of three times the value obtained using all variables, and an improvement in the distribution of them, reducing the observed bias in the results were observed. Comparing the RMSEP of the optimized model with the uncertainty estimates of the reference values it is observed that GA can be a very useful tool in second-order models.
NASA Astrophysics Data System (ADS)
Kim, Sang-Kyu; Ito, Kazuma; Yoshihara, Daisuke; Wakisaka, Tomoyuki
For numerically predicting the combustion processes in homogeneous charge compression ignition (HCCI) engines, practical chemical kinetic models have been explored. A genetic algorithm (GA) has been applied to the optimization of the rate constants in detailed chemical kinetic models, and a detailed kinetic model (592 reactions) for gasoline reference fuels with arbitrary octane number between 60 and 100 has been obtained from the detailed reaction schemes for iso-octane and n-heptane proposed by Golovitchev. The ignition timing in a gasoline HCCI engine has been predicted reasonably well by zero-dimensional simulation using the CHEMKIN code with this detailed kinetic model. An original reduced reaction scheme (45 reactions) for dimethyl ether (DME) has been derived from Curran’s detailed scheme, and the combustion process in a DME HCCI engine has been predicted reasonably well in a practical computation time by three-dimensional simulation using the authors’ GTT code, which has been linked to the CHEMKIN subroutines with the proposed reaction scheme and also has adopted a modified eddy dissipation combustion model.
NASA Astrophysics Data System (ADS)
Zhong, Jian; Dong, Gang; Sun, Yimei; Zhang, Zhaoyang; Wu, Yuqin
2016-11-01
The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. Project supported by the National Natural Science Foundation of China (Grant Nos. 41230421 and 41605075) and the National Basic Research Program of China (Grant No. 2013CB430101).
Modchang, Charin; Triampo, Wannapong; Lenbury, Yongwimon
2008-05-01
G-protein-coupled receptors (GPCRs) constitute a large and diverse family of proteins whose primary function is to transduce extracellular stimuli into intracellular signals. These receptors play a critical role in signal transduction, and are among the most important pharmacological drug targets. Upon binding of extracellular ligands, these receptor molecules couple to one or several subtypes of G-protein which reside at the intracellular side of the plasma membrane to trigger intracellular signaling events. The question of how GPCRs select and activate a single or multiple G-protein subtype(s) has been the topic of intense investigations. Evidence is also accumulating; however, that certain GPCRs can be internalized via lipid rafts and caveolae. In many cases, the mechanisms responsible for this still remain to be elucidated. In this work, we extend the mathematical model proposed by Chen et al. [Modelling of signalling via G-protein coupled receptors: pathway-dependent agonist potency and efficacy, Bull. Math. Biol. 65 (5) (2003) 933-958] to take into account internalization, recycling, degradation and synthesis of the receptors. In constructing the model, we assume that the receptors can exist in multiple conformational states allowing for a multiple effecter pathways. As data on kinetic reaction rates in the signalling processes measured in reliable in vivo and in vitro experiments is currently limited to a small number of known values. In this paper, we also apply a genetic algorithm (GA) to estimate the parameter values in our model.
Disaggregated Imaging Spacecraft Constellation Optimization with a Genetic Algorithm
2014-03-27
algorithm used throughout. Genetic algorithms are meant to simulate biological evolution and Charles Darwin’s theory of natural selection [41, 42]. Just like...Advances in artificial life: Darwin meets von Neumann - Volume Part II , ECAL’09, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 334–341, Ac- cessed
Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
NASA Astrophysics Data System (ADS)
Lee, Jaehun; Chung, Wooyong; Kim, Euntai
A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.
Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm
Wen, Xian-Bin; Zhang, Hua; Jiang, Ze-Tao
2008-01-01
A valid unsupervised and multiscale segmentation of synthetic aperture radar (SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization (EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive (MMAR) model is introduced to characterize and exploit the scale-to-scale statistical variations and statistical variations in the same scale in SAR imagery due to radar speckle, and a segmentation method is given by combining the GA algorithm with the EM algorithm. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of the Genetic and the EM algorithm by combination of both into a single procedure. The population-based stochastic search of the genetic algorithm (GA) explores the search space more thoroughly than the EM method. Therefore, our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization. Some experiment results are given based on our proposed approach, and compared to that of the EM algorithms. The experiments on the SAR images show that the GA-EM outperforms the EM method. PMID:27879787
Optimization of genomic selection training populations with a genetic algorithm
USDA-ARS?s Scientific Manuscript database
In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...
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…
Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.
ERIC Educational Resources Information Center
Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand
2003-01-01
Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…
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…
Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.
ERIC Educational Resources Information Center
Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand
2003-01-01
Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…
Evolving aerodynamic airfoils for wind turbines through a genetic algorithm
NASA Astrophysics Data System (ADS)
Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI
2017-01-01
Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.
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.
Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms
2002-03-01
material on data mining, classification, feature subset selection and weighting problem and genetic algorithms. 1.1.1 What is Data Mining? Data...to find the individual from the search space with the best “genetic material ” . A wide range of genetic representations (e.g., bit vectors, LISP...of non-coding material makes crossover less active within building block boundaries, but does not affect mutation’s constructive and destructive
Visibility conflict resolution for multiple antennae and multi-satellites via genetic algorithm
NASA Astrophysics Data System (ADS)
Lee, Junghyun; Hyun, Chung; Ahn, Hyosung; Wang, Semyung; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee
Satellite mission control systems typically are operated by scheduling missions to the visibility between ground stations and satellites. The communication for the mission is achieved by interacting with satellite visibility and ground station support. Specifically, the satellite forms a cone-type visibility passing over a ground station, and the antennas of ground stations support the satellite. When two or more satellites pass by at the same time or consecutively, the satellites may generate a visibility conflict. As the number of satellites increases, solving visibility conflict becomes important issue. In this study, we propose a visibility conflict resolution algorithm of multi-satellites by using a genetic algorithm (GA). The problem is converted to scheduling optimization modeling. The visibility of satellites and the supports of antennas are considered as tasks and resources individually. The visibility of satellites is allocated to the total support time of antennas as much as possible for users to obtain the maximum benefit. We focus on a genetic algorithm approach because the problem is complex and not defined explicitly. The genetic algorithm can be applied to such a complex model since it only needs an objective function and can approach a global optimum. However, the mathematical proof of global optimality for the genetic algorithm is very challenging. Therefore, we apply a greedy algorithm and show that our genetic approach is reasonable by comparing with the performance of greedy algorithm application.
Solving Classification Problems Using Genetic Programming Algorithms on GPUs
NASA Astrophysics Data System (ADS)
Cano, Alberto; Zafra, Amelia; Ventura, Sebastián
Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.
NASA Astrophysics Data System (ADS)
Sheer, D.; Sheer, A.; Lebherz, S.
2009-12-01
Lakes Rotorua and Rotoiti are two sizeable, culturally and economically important lakes on the North Island of New Zealand. Rotorua outflows traverse the short Ohau Channel before entering Lake Rotoiti. Ohau channel flows are partially controlled by a stoplog structure. Rotoiti outflows to the Kaituna River are fully controlled by the Okere Gate structure. The structures are managed by Environment Bay of Plenty (EBOP), a government agency. Management objectives include maintaining minimum lake levels to support recreational boating, restricting maximum lake levels to avoid residential flooding, minimum instream flows below the lower lake to maintain aquatic ecosystems, limits on maximum releases to control erosion and prevent flooding. In addition, management seeks to provide for a minimum annual variation in lake levels to control the growth of aquatic plants in the littoral zone and to periodically expose beaches with important cultural value to the indigenous Maori population. The levels necessary to expose beaches may be lower than the minimum levels desired to support boating. Records of beach exposure are scant; the existence of beaches may depend on climate cycles. There is flow dependent recreational rafting below Okere Gates. This rafting is economically valuable, but is also contentious because the reach of Kaituna River flows through important Maori cultural areas, including grave sites. The Maoris have expressed a preference for replacing the Okere Gates with a fixed stepped weir, although the existing gates can be relatively easily operated to reproduce the flows over any of the fixed weir designs so far proposed. HydroLogics created a model of the two-lake system using its OASIS software system. The inflows to the lakes were estimated based on available historical flow and lake level data and on flow estimates derived from Mike-11 modeling of historical lake outlet configurations. A custom genetic algorithm (GA) was created to “wrap” the
Novel hybrid genetic algorithm for progressive multiple sequence alignment.
Afridi, Muhammad Ishaq
2013-01-01
The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution.
A simple genetic algorithm for multiple sequence alignment.
Gondro, C; Kinghorn, B P
2007-10-05
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.
Genetic-based EM algorithm for learning Gaussian mixture models.
Pernkopf, Franz; Bouchaffra, Djamel
2005-08-01
We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning Gaussian mixture models from multivariate data. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of Genetic algorithms (GA) and the EM algorithm by combination of both into a single procedure. The population-based stochastic search of the GA explores the search space more thoroughly than the EM method. Therefore, our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization. The GA-EM algorithm is elitist which maintains the monotonic convergence property of the EM algorithm. The experiments on simulated and real data show that the GA-EM outperforms the EM method since: 1) We have obtained a better MDL score while using exactly the same termination condition for both algorithms. 2) Our approach identifies the number of components which were used to generate the underlying data more often than the EM algorithm.
NASA Astrophysics Data System (ADS)
Picozzi, M.; Oth, A.; Parolai, S.; Bindi, D.; De Landro, G.; Amoroso, O.
2017-05-01
The accurate determination of stress drop, seismic efficiency, and how source parameters scale with earthquake size is an important issue for seismic hazard assessment of induced seismicity. We propose an improved nonparametric, data-driven strategy suitable for monitoring induced seismicity, which combines the generalized inversion technique together with genetic algorithms. In the first step of the analysis the generalized inversion technique allows for an effective correction of waveforms for attenuation and site contributions. Then, the retrieved source spectra are inverted by a nonlinear sensitivity-driven inversion scheme that allows accurate estimation of source parameters. We therefore investigate the earthquake source characteristics of 633 induced earthquakes (Mw 2-3.8) recorded at The Geysers geothermal field (California) by a dense seismic network (i.e., 32 stations, more than 17.000 velocity records). We find a nonself-similar behavior, empirical source spectra that require an ωγ source model with γ > 2 to be well fit and small radiation efficiency ηSW. All these findings suggest different dynamic rupture processes for smaller and larger earthquakes and that the proportion of high-frequency energy radiation and the amount of energy required to overcome the friction or for the creation of new fractures surface changes with earthquake size. Furthermore, we observe also two distinct families of events with peculiar source parameters that in one case suggests the reactivation of deep structures linked to the regional tectonics, while in the other supports the idea of an important role of steeply dipping faults in the fluid pressure diffusion.
NASA Astrophysics Data System (ADS)
Bahrami, Saeed; Doulati Ardejani, Faramarz; Baafi, Ernest
2016-05-01
In this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known analytical solutions. Predicted results obtained by the hybrid methods are closer to the field data compared to the outputs of analytical and simple ANN models. Results show that despite the use of fewer and simpler parameters by the hybrid models, the ANN-GA and to some extent the ANN-SA have the ability to compete with the numerical models.
Moore, J H
1995-06-01
A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.
Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm.
Wen, Xian-Bin; Zhang, Hua; Jiang, Ze-Tao
2008-03-12
A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization(EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive(MMAR) model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL) criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA) exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.
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.
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.
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
An Evaluation of Potentials of Genetic Algorithm in Shortest Path Problem
NASA Astrophysics Data System (ADS)
Hassany Pazooky, S.; Rahmatollahi Namin, Sh; Soleymani, A.; Samadzadegan, F.
2009-04-01
One of the most typical issues considered in combinatorial systems in transportation networks, is the shortest path problem. In such networks, routing has a significant impact on the network's performance. Due to natural complexity in transportation networks and strong impact of routing in different fields of decision making, such as traffic management and vehicle routing problem (VRP), appropriate solutions to solve this problem are crucial to be determined. During last years, in order to solve the shortest path problem, different solutions are proposed. These techniques are divided into two categories of classic and evolutionary approaches. Two well-known classic algorithms are Dijkstra and A*. Dijkstra is known as a robust, but time consuming algorithm in finding the shortest path problem. A* is also another algorithm very similar to Dijkstra, less robust but with a higher performance. On the other hand, Genetic algorithms are introduced as most applicable evolutionary algorithms. Genetic Algorithm uses a parallel search method in several parts of the domain and is not trapped in local optimums. In this paper, the potentiality of Genetic algorithm for finding the shortest path is evaluated by making a comparison between this algorithm and classic algorithms (Dijkstra and A*). Evaluation of the potential of these techniques on a transportation network in an urban area shows that due to the problem of classic methods in their small search space, GA had a better performance in finding the shortest path.
Finding Bent Functions Using Genetic Algorithms
2009-09-01
2009. [20] R. N. Williams , “A Painless Guide to CRC Error Detection Algorithms,” http://www.cs.waikato.ac.nz/~312/crc.txt, last accessed 31AUG09. 179...Naval Postgraduate School Monterey, CA 10. Dr. Herschel Loomis Naval Postgraduate School Monterey, CA 180 11. Mr. Kyprianos Papadimitriou
An enhanced nonparametric streamflow disaggregation model with genetic algorithm
NASA Astrophysics Data System (ADS)
Lee, T.; Salas, J. D.; Prairie, J.
2010-08-01
Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes.
A parallel genetic algorithm for the set partitioning problem
Levine, D.
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.
Selecting Random Distributed Elements for HIFU using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zhou, Yufeng
2011-09-01
As an effective and noninvasive therapeutic modality for tumor treatment, high-intensity focused ultrasound (HIFU) has attracted attention from both physicians and patients. New generations of HIFU systems with the ability to electrically steer the HIFU focus using phased array transducers have been under development. The presence of side and grating lobes may cause undesired thermal accumulation at the interface of the coupling medium (i.e. water) and skin, or in the intervening tissue. Although sparse randomly distributed piston elements could reduce the amplitude of grating lobes, there are theoretically no grating lobes with the use of concave elements in the new phased array HIFU. A new HIFU transmission strategy is proposed in this study, firing a number of but not all elements for a certain period and then changing to another group for the next firing sequence. The advantages are: 1) the asymmetric position of active elements may reduce the side lobes, and 2) each element has some resting time during the entire HIFU ablation (up to several hours for some clinical applications) so that the decreasing efficiency of the transducer due to thermal accumulation is minimized. Genetic algorithm was used for selecting randomly distributed elements in a HIFU array. Amplitudes of the first side lobes at the focal plane were used as the fitness value in the optimization. Overall, it is suggested that the proposed new strategy could reduce the side lobe and the consequent side-effects, and the genetic algorithm is effective in selecting those randomly distributed elements in a HIFU array.
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.
[Image reconstruction in electrical impedance tomography based on genetic algorithm].
Hou, Weidong; Mo, Yulong
2003-03-01
Image reconstruction in electrical impedance tomography (EIT) is a highly ill-posed, non-linear inverse problem. The modified Newton-Raphson (MNR) iteration algorithm is deduced from the strictest theoretic analysis. It is an optimization algorithm based on minimizing the object function. The MNR algorithm with regularization technique is usually not stable, due to the serious image reconstruction model error and measurement noise. So the reconstruction precision is not high when used in static EIT. A new static image reconstruction method for EIT based on genetic algorithm (GA-EIT) is proposed in this paper. The experimental results indicate that the performance (including stability, the precision and space resolution in reconstructing the static EIT image) of the GA-EIT algorithm is better than that of the MNR algorithm.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-01-01
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.
Method of stereo matching based on genetic algorithm
NASA Astrophysics Data System (ADS)
Lu, Chaohui; An, Ping; Zhang, Zhaoyang
2003-09-01
A new stereo matching scheme based on image edge and genetic algorithm (GA) is presented to improve the conventional stereo matching method in this paper. In order to extract robust edge feature for stereo matching, infinite symmetric exponential filter (ISEF) is firstly applied to remove the noise of image, and nonlinear Laplace operator together with local variance of intensity are then used to detect edges. Apart from the detected edge, the polarity of edge pixels is also obtained. As an efficient search method, genetic algorithm is applied to find the best matching pair. For this purpose, some new ideas are developed for applying genetic algorithm to stereo matching. Experimental results show that the proposed methods are effective and can obtain good results.
Protein fold classification with genetic algorithms and feature selection.
Chen, Peng; Liu, Chunmei; Burge, Legand; Mahmood, Mohammad; Southerland, William; Gloster, Clay
2009-10-01
Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.
Genetic algorithms with permutation coding for multiple sequence alignment.
Ben Othman, Mohamed Tahar; Abdel-Azim, Gamil
2013-08-01
Multiple sequence alignment (MSA) is one of the topics of bio informatics that has seriously been researched. It is known as NP-complete problem. It is also considered as one of the most important and daunting tasks in computational biology. Concerning this a wide number of heuristic algorithms have been proposed to find optimal alignment. Among these heuristic algorithms are genetic algorithms (GA). The GA has mainly two major weaknesses: it is time consuming and can cause local minima. One of the significant aspects in the GA process in MSA is to maximize the similarities between sequences by adding and shuffling the gaps of Solution Coding (SC). Several ways for SC have been introduced. One of them is the Permutation Coding (PC). We propose a hybrid algorithm based on genetic algorithms (GAs) with a PC and 2-opt algorithm. The PC helps to code the MSA solution which maximizes the gain of resources, reliability and diversity of GA. The use of the PC opens the area by applying all functions over permutations for MSA. Thus, we suggest an algorithm to calculate the scoring function for multiple alignments based on PC, which is used as fitness function. The time complexity of the GA is reduced by using this algorithm. Our GA is implemented with different selections strategies and different crossovers. The probability of crossover and mutation is set as one strategy. Relevant patents have been probed in the topic.
Combinatorial optimization problem solution based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Peng
2017-08-01
Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.
RNA-RNA interaction prediction using genetic algorithm.
Montaseri, Soheila; Zare-Mirakabad, Fatemeh; Moghadam-Charkari, Nasrollah
2014-01-01
RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time. In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations. This algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches.
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.
Evolving neural networks with genetic algorithms to study the string landscape
NASA Astrophysics Data System (ADS)
Ruehle, Fabian
2017-08-01
We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.
Air data system optimization using a genetic algorithm
NASA Technical Reports Server (NTRS)
Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III
1992-01-01
An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.
Genetic 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.
Algorithmic Trading with Developmental and Linear Genetic Programming
NASA Astrophysics Data System (ADS)
Wilson, Garnett; Banzhaf, Wolfgang
A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.
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.
Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
NASA Astrophysics Data System (ADS)
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
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.
Optimization of multicast optical networks with genetic algorithm
NASA Astrophysics Data System (ADS)
Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng
2007-11-01
In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.
The [Gamma] Algorithm and Some Applications
ERIC Educational Resources Information Center
Castillo, Enrique; Jubete, Francisco
2004-01-01
In this paper the power of the [gamma] algorithm for obtaining the dual of a given cone and some of its multiple applications is discussed. The meaning of each sequential tableau appearing during the process is interpreted. It is shown that each tableau contains the generators of the dual cone of a given cone and that the algorithm updates the…
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.
Comparative Analysis of Rank Aggregation Techniques for Metasearch Using Genetic Algorithm
ERIC Educational Resources Information Center
Kaur, Parneet; Singh, Manpreet; Singh Josan, Gurpreet
2017-01-01
Rank Aggregation techniques have found wide applications for metasearch along with other streams such as Sports, Voting System, Stock Markets, and Reduction in Spam. This paper presents the optimization of rank lists for web queries put by the user on different MetaSearch engines. A metaheuristic approach such as Genetic algorithm based rank…
Gregurick, S. K.
2001-04-20
During the period from September 1, 1998 until September 1, 2000 I was awarded a Sloan/DOE postdoctoral fellowship to work in collaboration with Professor John Moult at the Center for Advanced Research in Biotechnology (CARB). Our research project, ''Ab Initio Protein Tertiary Structure Prediction and a Comparative Genetic algorithm'', yielded promising initial results. In short, the project is designed to predict the native fold, or native tertiary structure, of a given protein by inputting only the primary sequence of the protein (one or three letter code). The algorithm is based on a general learning, or evolutionary algorithm and is called Genetic Algorithm (GAS). In our particular application of GAS, we search for native folds, or lowest energy structures, using two different descriptions for the interactions of the atoms and residues in a given protein sequence. One potential energy function is based on a free energy description, while the other function is a threading potential derived by Moult and Samudrala. This modified genetic algorithm was loosely termed a Comparative Genetic Algorithm and was designed to search for native folded structures on both potential energy surfaces, simultaneously. We tested the algorithm on a series of peptides ranging from 11 to 15 residues in length, which are thought to be independent folding units and thereby will fold to native structures independent of the larger protein environment. Our initial results indicated a modest increase in accuracy, as compared to a standard Genetic Algorithm. We are now in the process of improving the algorithm to increase the sensitivity to other inputs, such as secondary structure requirements. The project did not involve additional students and as of yet, the work has not been published.
Genetic algorithm for flood detection and evacuation route planning
NASA Astrophysics Data System (ADS)
Gomes, Rahul; Straub, Jeremy
2017-05-01
A genetic-type algorithm is presented that uses satellite geospatial data to determine the most probable path to safety for individuals in a disaster area, where a traditional routing system cannot be used. The algorithm uses geological features and disaster information to determine the shortest safe path. It predicts how a flood can change a landform over time and uses this data to predict alternate routes. It also predicts safe routes in rural locations where GPS/map-based routing data is unavailable or inaccurate. Reflectance and a supervised classification algorithm are used and the output is compared with RFPI and PCR-GLOBWB data.
Automated discrete element method calibration using genetic and optimization algorithms
NASA Astrophysics Data System (ADS)
Do, Huy Q.; Aragón, Alejandro M.; Schott, Dingena L.
2017-06-01
This research aims at developing a universal methodology for automated calibration of microscopic properties of modelled granular materials. The proposed calibrator can be applied for different experimental set-ups. Two optimization approaches: (1) a genetic algorithm and (2) DIRECT optimization, are used to identify discrete element method input model parameters, e.g., coefficients of sliding and rolling friction. The algorithms are used to minimize the objective function characterized by the discrepancy between the experimental macroscopic properties and the associated numerical results. Two test cases highlight the robustness, stability, and reliability of the two algorithms used for automated discrete element method calibration with different set-ups.
Digit and command interpretation for electronic book using neural network and genetic algorithm.
Lam, H K; Leung, Frank H F
2004-12-01
This paper presents the interpretation of digits and commands using a modified neural network and the genetic algorithm. The modified neural network exhibits a node-to-node relationship which enhances its learning and generalization abilities. A digit-and-command interpreter constructed by the modified neural networks is proposed to recognize handwritten digits and commands. A genetic algorithm is employed to train the parameters of the modified neural networks of the digit-and-command interpreter. The proposed digit-and-command interpreter is successfully realized in an electronic book. Simulation and experimental results will be presented to show the applicability and merits of the proposed approach.
Advancing x-ray scattering metrology using inverse genetic algorithms
NASA Astrophysics Data System (ADS)
Hannon, Adam F.; Sunday, Daniel F.; Windover, Donald; Joseph Kline, R.
2016-07-01
We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real-space structure in periodic gratings measured using critical dimension small-angle x-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real-space structure of our nanogratings. The study shows that for x-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.
Advancing X-ray scattering metrology using inverse genetic algorithms.
Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph
2016-01-01
We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.
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.
Advancing X-ray scattering metrology using inverse genetic algorithms
Hannon, Adam F.; Sunday, Daniel F.; Windover, Donald; Kline, R. Joseph
2016-01-01
We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting. PMID:27551326
Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.
Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S
2017-02-13
Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.
A boundary finding algorithm and its applications
NASA Technical Reports Server (NTRS)
Gupta, J. N.; Wintz, P. A.
1975-01-01
An algorithm for locating gray level and/or texture edges in digitized pictures is presented. The algorithm is based on the concept of hypothesis testing. The digitized picture is first subdivided into subsets of picture elements, e.g., 2 x 2 arrays. The algorithm then compares the first- and second-order statistics of adjacent subsets; adjacent subsets having similar first- and/or second-order statistics are merged into blobs. By continuing this process, the entire picture is segmented into blobs such that the picture elements within each blob have similar characteristics. The boundaries between the blobs comprise the boundaries. The algorithm always generates closed boundaries. The algorithm was developed for multispectral imagery of the earth's surface. Application of this algorithm to various image processing techniques such as efficient coding, information extraction (terrain classification), and pattern recognition (feature selection) are included.
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.
2001-01-01
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
Real-Time Scheduling in Heterogeneous Systems Considering Cache Reload Time Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Miryani, Mohammad Reza; Naghibzadeh, Mahmoud
Since optimal assignment of tasks in a multiprocessor system is, in almost all practical cases, an NP-hard problem, in recent years some algorithms based on genetic algorithms have been proposed. Some of these algorithms have considered real-time applications with multiple objectives, total tardiness, completion time, etc. Here, we propose a suboptimal static scheduler of nonpreemptable tasks in hard real-time heterogeneous multiprocessor systems considering time constraints and cache reload time. The approach makes use of genetic algorithm to minimize total completion time and number of processors used, simultaneously. One important issue which makes this research different from previous ones is cache reload time. The method is implemented and the results are compared against a similar method.
Optimal design of optical reference signals by use of a genetic algorithm
NASA Astrophysics Data System (ADS)
Saez-Landete, José; Salcedo-Sanz, Sancho; Rosa-Zurera, Manuel; Alonso, José; Bernabeu, Eusebio
2005-10-01
A new technique for the generation of optical reference signals with optimal properties is presented. In grating measurement systems a reference signal is needed to achieve an absolute measurement of the position. The optical signal is the autocorrelation of two codes with binary transmittance. For a long time, the design of this type of code has required great computational effort, which limits the size of the code to ˜30 elements. Recently, the application of the dividing rectangles (DIRECT) algorithm has allowed the automatic design of codes up to 100 elements. Because of the binary nature of the problem and the parallel processing of the genetic algorithms, these algorithms are efficient tools for obtaining codes with particular autocorrelation properties. We design optimum zero reference codes with arbitrary length by means of a genetic algorithm enhanced with a restricted search operator.
A Parallel Genetic Algorithm for Automated Electronic Circuit Design
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)
2000-01-01
We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.
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.
Genetic Algorithms in a Distributed Computing Environment Using PVM
NASA Astrophysics Data System (ADS)
Cronje, G. A.; Steeb, W.-H.
The Parallel Virtual Machine (PVM) is a software system that enables a collection of heterogeneous computer systems to be used as a coherent and flexible concurrent computation resource. We show that genetic algorithms can be implemented using a Parallel Virtual Machine and C++. Problems with constraints are also discussed.
Genetic algorithms in a distributed computing environment using PVM
Cronje, G.A.; Steeb, W.H.
1997-04-01
The Parallel Virtual Machine (PVM) is a software system that enables a collection of heterogeneous computer systems to be used as a coherent and flexible concurrent computation resource. We show that genetic algorithms can be implemented using a Parallel Virtual Machine and C++. Problems with constraints are also discussed.
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
Crossover Improvement for the Genetic Algorithm in Information Retrieval.
ERIC Educational Resources Information Center
Vrajitoru, Dana
1998-01-01
In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…
Some experiments in machine learning using vector evaluated genetic algorithms
Schaffer, J.D.
1985-01-01
This dissertation describes experiments conducted to explore the efficacy of using vector-valued feedback with a class of adaptive procedures called genetic algorithms. The software system developed was called VEGA for Vector Evaluated Genetic Algorithm and was first used on multiple objective optimization problems. The main conclusion of these experiments was that VEGA provided a powerful and robust search technique for complex multiobjective optimization problems of high order when little or no a priori knowledge was available to guide the search. These results were similar to those found by previous researchers using scalar genetic algorithms for scalar optimization problems. The VEGA technique was then applied to multiclass pattern discrimination tasks. The resulting software system was called LS-2 for Learning System-Two, since it followed closely the lead of a scalar-valued learning system called LS-1 developed by Stephen Smith. These experiments revealed that LS-2 was able to evolve high performance production system programs to perform the pattern discrimination tasks it was given. The VEGA approach demonstrates the efficacy of extending the previously demonstrated power of genetic algorithms to vector-valued problems and, thereby, provides a new approach to machine learning.
Constraining compartmental models using multiple voltage recordings and genetic algorithms.
Keren, Naomi; Peled, Noam; Korngreen, Alon
2005-12-01
Compartmental models with many nonlinearly and nonhomogeneous distributions of voltage-gated conductances are routinely used to investigate the physiology of complex neurons. However, the number of loosely constrained parameters makes manually constructing the desired model a daunting if not impossible task. Recently, progress has been made using automated parameter search methods, such as genetic algorithms (GAs). However, these methods have been applied to somatically recorded action potentials using relatively simple target functions. Using a genetic minimization algorithm and a reduced compartmental model based on a previously published model of layer 5 neocortical pyramidal neurons we compared the efficacy of five cost functions (based on the waveform of the membrane potential, the interspike interval, trajectory density, and their combinations) to constrain the model. When the model was constrained using somatic recordings only, a combined cost function was found to be the most effective. This combined cost function was then applied to investigate the contribution of dendritic and axonal recordings to the ability of the GA to constrain the model. The more recording locations from the dendrite and the axon that were added to the data set the better was the genetic minimization algorithm able to constrain the compartmental model. Based on these simulations we propose an experimental scheme that, in combination with a genetic minimization algorithm, may be used to constrain compartmental models of neurons.
USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...
Crossover Improvement for the Genetic Algorithm in Information Retrieval.
ERIC Educational Resources Information Center
Vrajitoru, Dana
1998-01-01
In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…
USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
Economic Dispatch Using Genetic Algorithm Based Hybrid Approach
Tahir Nadeem Malik; Aftab Ahmad; Shahab Khushnood
2006-07-01
Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)
Transcultural Diabetes Nutrition Algorithm: A Malaysian Application
Hamdy, Osama; Chin Chia, Yook; Lin Lim, Shueh; Kumari Natkunam, Santha; Yeong Tan, Ming; Sulaiman, Ridzoni; Nisak, Barakatun; Chee, Winnie Siew Swee; Marchetti, Albert; Hegazi, Refaat A.; Mechanick, Jeffrey I.
2013-01-01
Glycemic control among patients with prediabetes and type 2 diabetes mellitus (T2D) in Malaysia is suboptimal, especially after the continuous worsening over the past decade. Improved glycemic control may be achieved through a comprehensive management strategy that includes medical nutrition therapy (MNT). Evidence-based recommendations for diabetes-specific therapeutic diets are available internationally. However, Asian patients with T2D, including Malaysians, have unique disease characteristics and risk factors, as well as cultural and lifestyle dissimilarities, which may render international guidelines and recommendations less applicable and/or difficult to implement. With these thoughts in mind, a transcultural Diabetes Nutrition Algorithm (tDNA) was developed by an international task force of diabetes and nutrition experts through the restructuring of international guidelines for the nutritional management of prediabetes and T2D to account for cultural differences in lifestyle, diet, and genetic factors. The initial evidence-based global tDNA template was designed for simplicity, flexibility, and cultural modification. This paper reports the Malaysian adaptation of the tDNA, which takes into account the epidemiologic, physiologic, cultural, and lifestyle factors unique to Malaysia, as well as the local guidelines recommendations. PMID:24385984
Genetic applications in avian conservation
Susan M. Haig; Whitcomb M. Bronaugh; Rachel S. Crowhurst; Jesse D' Elia; Collin A. Eagles-Smith; Clinton W. Epps; Brian Knaus; Mark P. Miller; Michael L. Moses; Sara Oyler-McCance; W. Douglas Robinson; Brian. Sidlauskas
2011-01-01
A fundamental need in conserving species and their habitats is defining distinct entities that range from individuals to species to ecosystems and beyond. Rapid progression in this interdisciplinary field continues at an exponential rate; thus, periodic updates on theory, techniques, and applications are important for informing practitioners and consumers of genetic...
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm.
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality.
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
Uplink Scheduling of Navigation Constellation Based on Immune Genetic Algorithm.
Tang, Yinyin; Wang, Yueke; Chen, Jianyun; Li, Xianbin
2016-01-01
The uplink of navigation data as satellite ephemeris is a complex satellite range scheduling problem. Large-scale optimal problems cannot be tackled using traditional heuristic methods, and the efficiency of standard genetic algorithm is unsatisfactory. We propose a multi-objective immune genetic algorithm (IGA) for uplink scheduling of navigation constellation. The method focuses on balance traffic and maximum task objects based on satellite-ground index encoding method, individual diversity evaluation and memory library. Numerical results show that the multi-hierarchical encoding method can improve the computation efficiency, the fuzzy deviation toleration method can speed up convergence, and the method can achieve the balance target with a negligible loss in task number (approximately 2.98%). The proposed algorithm is a general method and thus can be used in similar problems.
Uplink Scheduling of Navigation Constellation Based on Immune Genetic Algorithm
Tang, Yinyin; Wang, Yueke; Chen, Jianyun; Li, Xianbin
2016-01-01
The uplink of navigation data as satellite ephemeris is a complex satellite range scheduling problem. Large–scale optimal problems cannot be tackled using traditional heuristic methods, and the efficiency of standard genetic algorithm is unsatisfactory. We propose a multi-objective immune genetic algorithm (IGA) for uplink scheduling of navigation constellation. The method focuses on balance traffic and maximum task objects based on satellite-ground index encoding method, individual diversity evaluation and memory library. Numerical results show that the multi–hierarchical encoding method can improve the computation efficiency, the fuzzy deviation toleration method can speed up convergence, and the method can achieve the balance target with a negligible loss in task number (approximately 2.98%). The proposed algorithm is a general method and thus can be used in similar problems. PMID:27736986
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
An Adaptive Immune Genetic Algorithm for Edge Detection
NASA Astrophysics Data System (ADS)
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Genetic algorithms for multicriteria shape optimization of induction furnace
NASA Astrophysics Data System (ADS)
Kůs, 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.
Genetic algorithms applied to nonlinear and complex domains
Barash, D; Woodin, A E
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
Genetic algorithms applied to nonlinear and complex domains
Barash, Danny
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
Genetic algorithms applied to nonlinear and complex domains
Barash, Danny
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a final result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
Evaluation of algorithms used to order markers on genetic maps.
Mollinari, M; Margarido, G R A; Vencovsky, R; Garcia, A A F
2009-12-01
When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results.
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.
Drawing inferences from clinical studies with missing values using genetic algorithm.
Priya, R Devi; Kuppuswami, S
2014-01-01
Missing data problem degrades the statistical power of any analysis made in clinical studies. To infer valid results from such studies, suitable method is required to replace the missing values. There is no method which can be universally applicable for handling missing values and the main objective of this paper is to introduce a common method applicable in all cases of missing data. In this paper, Bayesian Genetic Algorithm (BGA) is proposed to effectively impute both missing continuous and discrete values using heuristic search algorithm called genetic algorithm and Bayesian rule. BGA is applied to impute missing values in a real cancer dataset under Missing At Random (MAR) and Missing Completely At Random (MCAR) conditions. For both discrete and continuous attributes, the results show better classification accuracy and RMSE% than many existing methods.
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.
Machine learning applications in genetics and genomics.
Libbrecht, Maxwell W; Noble, William Stafford
2015-06-01
The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
NAS Applications and Advanced Algorithms
NASA Technical Reports Server (NTRS)
Bailey, David H.; Biswas, Rupak; VanDerWijngaart, Rob; Kutler, Paul (Technical Monitor)
1997-01-01
This paper examines the applications most commonly run on the supercomputers at the Numerical Aerospace Simulation (NAS) facility. It analyzes the extent to which such applications are fundamentally oriented to vector computers, and whether or not they can be efficiently implemented on hierarchical memory machines, such as systems with cache memories and highly parallel, distributed memory systems.
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.
Optimization on robot arm machining by using genetic algorithms
NASA Astrophysics Data System (ADS)
Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En
2007-12-01
In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.
Optimum detailed design of reinforced concrete frames using genetic algorithms
NASA Astrophysics Data System (ADS)
Govindaraj, V.; Ramasamy, J. V.
2007-06-01
This article presents the application of the genetic algorithm to the optimum detailed design of reinforced concrete frames based on Indian Standard specifications. The objective function is the total cost of the frame which includes the cost of concrete, formwork and reinforcing steel for individual members of the frame. In order for the optimum design to be directly constructible without any further modifications, aspects such as available standard reinforcement bar diameters, spacing requirements of reinforcing bars, modular sizes of members, architectural requirements on member sizes and other practical requirements in addition to relevant codal provisions are incorporated into the optimum design model. The produced optimum design satisfies the strength, serviceability, ductility, durability and other constraints related to good design and detailing practice. The detailing of reinforcements in the beam members is carried out as a sub-level optimization problem. This strategy helps to reduce the size of the optimization problem and saves computational time. The proposed method is demonstrated through several example problems and the optimum results obtained are compared with those in the available literature. It is concluded that the proposed optimum design model can be adopted in design offices as it yields rational, reliable, economical, time-saving and practical designs.
Optimization of process parameters in stereolithography using genetic algorithm
NASA Astrophysics Data System (ADS)
Chockalingam, K.; Jawahar, N.; Vijaybabu, E. R.
2003-10-01
Stereolithography is the most popular RP process in which intricate models are directly constructed from a CAD package by polymerizing a plastic monomer. The application range is still limited, because dimensional accuracy is still inferior to that of conventional machining process. The ultimate dimensional accuracy of a part built on a layer-by-layer basis depends on shrinkage which depend on many factors such as layer thickness, hatch spacing, hatch style, hatch over cure and fill cure depth. The influence of the above factors on shrinkage in X and Y directions fit to the nonlinear pattern. A particular combination of process variables that would result same shrinkage rate in both directions would enable to predict shrinkage allowance to be provided on a part and hence the CAD model could be constructed including shrinkage allowance. In this concern, the objective of the present work is set as determination of process parameters to have same shrinkage rate in both X and Y directions. A genetic algorithm (GA) is proposed to find optimal process parameters for the above objective. This approach is an analytical approach with experimental sample data and has great potential to predict process parameters for better dimensional accuracy in stereolithography process.
NASA Astrophysics Data System (ADS)
Patel, Sanjaykumar J.; Kheraj, Vipul
2015-07-01
This paper describes a systematic investigation on the use of the genetic algorithm (GA) to accomplish ultra-low reflective multilayer coating designs for optoelectronic device applications. The algorithm is implemented using LabVIEW as a programming tool. The effects of the genetic operators, such as the type of crossover and mutation, as well as algorithm parameters, such as population size and range of search space, on the convergence of design-solution were studied. Finally, the optimal design is obtained in terms of the thickness of each layer for the multilayer AR coating using optimized genetic operators and algorithm parameters. The program is successfully tested to design AR coating in NIR wavelength range to achieve average reflectivity (R) below 10-3 over the spectral bandwidth of 200 nm with different combinations of coating materials in the stack. The random-point crossover operator is found to exhibit a better convergence rate of the solution than single-point and double-point crossover. Periodically re-initializing the thickness value of a randomly selected layer from the stack effectively prevents the solution from becoming trapped in local minima and improves the convergence probability.
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
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.
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.
Distributed query plan generation using multiobjective genetic algorithm.
Panicker, Shina; Kumar, T V Vijay
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.
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.
Ugrinov, Krastyu G; Freed, Stefan D; Thomas, Clayton L; Lee, Shaun W
2015-01-01
Mucopolysaccharidosis type IIIA (MPS-IIIA, Sanfilippo syndrome) is a Lysosomal Storage Disease caused by cellular deficiency of N-sulfoglucosamine sulfohydrolase (SGSH). Given the large heterogeneity of genetic mutations responsible for the disease, a comprehensive understanding of the mechanisms by which these mutations affect enzyme function is needed to guide effective therapies. We developed a multiparametric computational algorithm to assess how patient genetic mutations in SGSH affect overall enzyme biogenesis, stability, and function. 107 patient mutations for the SGSH gene were obtained from the Human Gene Mutation Database representing all of the clinical mutations documented for Sanfilippo syndrome. We assessed each mutation individually using ten distinct parameters to give a comprehensive predictive score of the stability and misfolding capacity of the SGSH enzyme resulting from each of these mutations. The predictive score generated by our multiparametric algorithm yielded a standardized quantitative assessment of the severity of a given SGSH genetic mutation toward overall enzyme activity. Application of our algorithm has identified SGSH mutations in which enzymatic malfunction of the gene product is specifically due to impairments in protein folding. These scores provide an assessment of the degree to which a particular mutation could be treated using approaches such as chaperone therapies. Our multiparametric protein biogenesis algorithm advances a key understanding in the overall biochemical mechanism underlying Sanfilippo syndrome. Importantly, the design of our multiparametric algorithm can be tailored to many other diseases of genetic heterogeneity for which protein misfolding phenotypes may constitute a major component of disease manifestation.
Genetic analysis of growth curves using the SAEM algorithm.
Jaffrézic, Florence; Meza, Cristian; Lavielle, Marc; Foulley, Jean-Louis
2006-01-01
The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.
The genetic algorithm: A robust method for stress inversion
NASA Astrophysics Data System (ADS)
Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.
2017-01-01
The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.
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
Mass spectrometry cancer data classification using wavelets and genetic algorithm.
Nguyen, Thanh; Nahavandi, Saeid; Creighton, Douglas; Khosravi, Abbas
2015-12-21
This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.
Multiple Magnetic Dipole Modeling Coupled with a Genetic Algorithm
NASA Astrophysics Data System (ADS)
Lientschnig, G.
2012-05-01
Magnetic field measurements of scientific spacecraft can be modelled successfully with the multiple magnetic dipole method. The existing GANEW software [1] uses a modified Gauss-Newton algorithm to find good magnetic dipole models. However, this deterministic approach relies on suitable guesses of the initial parameters which require a lot of expertise and time-consuming interaction of the user. Here, the use of probabilistic methods employing genetic algorithms is put forward. Stochastic methods like these are well- suited for providing good initial starting points for GANEW. Furthermore a computer software is reported upon that was successfully tested and used for a Cluster II satellite.
A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects
Alimi, Roger; Weiss, Eyal; Ram-Cohen, Tsuriel; Geron, Nir; Yogev, Idan
2015-01-01
A dedicated Genetic Algorithm (GA) has been developed to localize the trajectory of ferromagnetic moving objects within a bounded perimeter. Localization of moving ferromagnetic objects is an important tool because it can be employed in situations when the object is obscured. This work is innovative for two main reasons: first, the GA has been tuned to provide an accurate and fast solution to the inverse magnetic field equations problem. Second, the algorithm has been successfully tested using real-life experimental data. Very accurate trajectory localization estimations were obtained over a wide range of scenarios. PMID:26393598
Thermoluminescence curves simulation using genetic algorithm with factorial design
NASA Astrophysics Data System (ADS)
Popko, E. A.; Weinstein, I. A.
2016-05-01
The evolutionary approach is an effective optimization tool for numeric analysis of thermoluminescence (TL) processes to assess the microparameters of kinetic models and to determine its effects on the shape of TL peaks. In this paper, the procedure for tuning of genetic algorithm (GA) is presented. This approach is based on multifactorial experiment and allows choosing intrinsic mechanisms of evolutionary operators which provide the most efficient algorithm performance. The proposed method is tested by considering the “one trap-one recombination center” (OTOR) model as an example and advantages for approximation of experimental TL curves are shown.
Diagnosis support using Fuzzy Cognitive Maps combined with Genetic Algorithms.
Georgopoulos, Voula C; Stylios, Chrysotomos D
2009-01-01
A new hybrid modeling methodology to support medical diagnosis decisions is developed here. It extends previous work on Competitive Fuzzy Cognitive Maps for Medical Diagnosis Support Systems by complementing them with Genetic Algorithms Methods for concept interaction. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable Advanced Medical Diagnosis Support Systems that are suitable to handle situations where the decisions are not clearly distinct. The technique developed here is applied successfully to model and test a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments.
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 Cβ) with the φ and ψ dihedral angles as the only degrees of freedom. In addition, φ and ψ 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
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.
Genetic Algorithm for Initial Orbit Determination with Too Short Arc
NASA Astrophysics Data System (ADS)
Xin-ran, Li; Xin, Wang
2017-01-01
A huge quantity of too-short-arc (TSA) observational data have been obtained in sky surveys of space objects. However, reasonable results for the TSAs can hardly be obtained with the classical methods of initial orbit determination (IOD). In this paper, the IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selections of the optimized variables and the corresponding genetic operators for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.
Aligning multiple protein sequences by parallel hybrid genetic algorithm.
Nguyen, Hung Dinh; Yoshihara, Ikuo; Yamamori, Kunihito; Yasunaga, Moritoshi
2002-01-01
This paper presents a parallel hybrid genetic algorithm (GA) for solving the sum-of-pairs multiple protein sequence alignment. A new chromosome representation and its corresponding genetic operators are proposed. A multi-population GENITOR-type GA is combined with local search heuristics. It is then extended to run in parallel on a multiprocessor system for speeding up. Experimental results of benchmarks from the BAliBASE show that the proposed method is superior to MSA, OMA, and SAGA methods with regard to quality of solution and running time. It can be used for finding multiple sequence alignment as well as testing cost functions.
Polyglot programming in applications used for genetic data analysis.
Nowak, Robert M
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.
Polyglot Programming in Applications Used for Genetic Data Analysis
Nowak, Robert M.
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. PMID:25197633
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.
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Genetic Algorithm Design of a 3D Printed Heat Sink
Wu, Tong; Ozpineci, Burak; Ayers, Curtis William
2016-01-01
In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate the performance of the newly designed heat sinkcompared to commercially available heat sinks.
Road detection in spaceborne SAR images using genetic algorithm
NASA Astrophysics Data System (ADS)
Jeon, Byoungki; Jang, JeongHun; Hong, KiSang
2000-08-01
This paper presents a technique for detection of roads in a spaceborne SAR image using a genetic algorithm. Roads in a spaceborne SAR image can be modelled as curvilinear structures with some thickness. Curve segments, which represent candidate positions of roads, are extracted from the image using a curvilinear structure detector, and roads are detected accurately by grouping those curve segments. For this purpose, we designed a grouping method based on a genetic algorithm (GA), which is one of the global optimization methods, combined perceptual grouping factors with it, and tried to reduce its overall computational cost by introducing an operation of thresholding and a concept of region growing. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. We applied our method to ERS-1 SAR images that have a resolution of about 30 meters, and the experimental results show that our method can detect roads accurately, and is much faster than a globally applied GA approach.
Design of PID-type controllers using multiobjective genetic algorithms.
Herreros, Alberto; Baeyens, Enrique; Perán, José R
2002-10-01
The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases.
Supermultiplicative Speedups of Probabilistic Model-Building Genetic Algorithms
2009-02-01
simulations. We (Todd Martinez (2005 MacArthur fellow), Duanc Johnson, Kumara Sastry and David E. Goldberg) have applied inultiobjcctive GAs and model...AUTHOR(S) David E. Goldberg. Kumara Sastry. Martin Pelikan 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S...Speedups of Probabilistic Model-Building Genetic Algorithms AFOSR Grant No. FA9550-06-1-0096 February 1, 2006 to November 30, 2008 David E. Goldberg
Investigation of range extension with a genetic algorithm
Austin, A. S., LLNL
1998-03-04
Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and the simulator`s output (terminal range) was the fitness measure. The parameters of interest were initial launch altitude, initial launch speed, wing angle-of-attack, and engine ignition time. The parameter values the GA produced were validated by Monte Carlo investigations employing a full-scale six-degree-of-freedom (6 DOF) simulation. The best results produced by Monte Carlo processes using values based on the GA derived parameters were within - 1% of the ranges generated by the simplified model using the evolved parameter values. This report has five sections. Section 2 discusses the motivation for the range extension investigation and reviews the surrogate flight model developed as a fitness function for the genetic algorithm tool. Section 3 details the representation and implementation of the task within the genetic algorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.
Identification of the Roessler system: algebraic approach and genetic algorithms
NASA Astrophysics Data System (ADS)
Ibanez, C. A.; Sanchez, J. H.; Suarez, M. S. C.; Flores, F. A.; Garrido, R. M.; Martinez, R. G.
2005-10-01
This article presents a method to determine the parameters of Rossler's attractor in a very approximated way, by means of observations of an available variable. It is shown that the system is observable and identifiable algebraically with respect to the chosen output. This fact allows to construct a differential parametrization of the output and its derivatives. Using this parametrization an identification scheme based on least mean squares is established and the solution is found with a genetic algorithm.
Optical design with the aid of a genetic algorithm.
van Leijenhorst, D C; Lucasius, C B; Thijssen, J M
1996-01-01
Natural evolution is widely accepted as being the process underlying the design and optimization of the sensory functions of biological organisms. Using a genetic algorithm, this process is extended to the automatic optimization and design of optical systems, e.g. as used in astronomical telescopes. The results of this feasibility study indicate that various types of aberrations can be corrected quickly and simultaneously, even on small computers.
OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM
Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam; Ivanovich, Grujica
2010-06-15
This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.
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.
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
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.
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.
Genetic algorithm testbed for expert system testing. Final report
Roache, E.
1996-01-01
In recent years, the electric utility industry has developed advisory and control software that makes use of expert system technology. The validation of the underlying knowledge representation in these expert systems is critical to their success. Most expert systems currently deployed have been validated by certifying that the expert system provides appropriate conclusions for specific test cases. While this type of testing is important, it does not test cases where unexpected inputs are presented to the expert system and potential errors are exposed. Exhaustive testing is not typically an option due to the complexity of the knowledge representation and the combinatorial effects associated with checking all possible inputs through all possible execution paths. Genetic algorithms are general purpose search techniques modeled on natural adaptive systems and selective breeding methods. Genetic algorithms have been used successfully for parameter optimization and efficient search. The goal of this project was to confirm or reject the hypothesis that genetic algorithms (GAs) are useful in expert system validation. The GA system specifically targeted errors in the study`s expert system that would be exposed by unexpected input cases. The GA system found errors in the expert system and the hypothesis was confirmed. This report describes the process and results of the project.
Neural-network-biased genetic algorithms for materials design
NASA Astrophysics Data System (ADS)
Patra, Tarak; Meenakshisundaram, Venkatesh; Simmons, David
Machine learning tools have been progressively adopted by the materials science community to accelerate design of materials with targeted properties. However, in the search for new materials exhibiting properties and performance beyond that previously achieved, machine learning approaches are frequently limited by two major shortcomings. First, they are intrinsically interpolative. They are therefore better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require the availability of large datasets, which in some fields are not available and would be prohibitively expensive to produce. Here we describe a new strategy for combining genetic algorithms, neural networks and other machine learning tools, and molecular simulation to discover materials with extremal properties in the absence of pre-existing data. Predictions from progressively constructed machine learning tools are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct molecular dynamics simulation. We survey several initial materials design problems we have addressed with this framework and compare its performance to that of standard genetic algorithm approaches. We acknowledge the W. M. Keck Foundation for support of this work.
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.
Design of an acoustic metamaterial lens using genetic algorithms.
Li, Dennis; Zigoneanu, Lucian; Popa, Bogdan-Ioan; Cummer, Steven A
2012-10-01
The present work demonstrates a genetic algorithm approach to optimizing the effective material parameters of an acoustic metamaterial. The target device is an acoustic gradient index (GRIN) lens in air, which ideally possesses a maximized index of refraction, minimized frequency dependence of the material properties, and minimized acoustic impedance mismatch. Applying this algorithm results in complex designs with certain common features, and effective material properties that are better than those present in previous designs. After modifying the optimized unit cell designs to make them suitable for fabrication, a two-dimensional lens was built and experimentally tested. Its performance was in good agreement with simulations. Overall, the optimization approach was able to improve the refractive index but at the cost of increased frequency dependence. The optimal solutions found by the algorithm provide a numerical description of how the material parameters compete with one another and thus describes the level of performance achievable in the GRIN lens.
Optimal recombination in genetic algorithms for flowshop scheduling problems
NASA Astrophysics Data System (ADS)
Kovalenko, Julia
2016-10-01
The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.
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.
Feature selection for optimized skin tumor recognition using genetic algorithms.
Handels, H; Ross, T; Kreusch, J; Wolff, H H; Pöppl, S J
1999-07-01
In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.
An Adaptive Hybrid Genetic Algorithm for Improved Groundwater Remediation Design
NASA Astrophysics Data System (ADS)
Espinoza, F. P.; Minsker, B. S.; Goldberg, D. E.
2001-12-01
Identifying optimal designs for a groundwater remediation system is computationally intensive, especially for complex, nonlinear problems such as enhanced in situ bioremediation technology. To improve performance, we apply a hybrid genetic algorithm (HGA), which is a two-step solution method: a genetic algorithm (GA) for global search using the entire population and then a local search (LS) to improve search speed for only a few individuals in the population. We implement two types of HGAs: a non-adaptive HGA (NAHGA), whose operations are invariant throughout the run, and a self-adaptive HGA (SAHGA), whose operations adapt to the performance of the algorithm. The best settings of the two HGAs for optimal performance are then investigated for a groundwater remediation problem. The settings include the frequency of LS with respect to the normal GA evaluation, probability of individual selection for LS, evolution criterion for LS (Lamarckian or Baldwinian), and number of local search iterations. A comparison of the algorithms' performance under different settings will be presented.
High performance genetic algorithm for VLSI circuit partitioning
NASA Astrophysics Data System (ADS)
Dinu, Simona
2016-12-01
Partitioning is one of the biggest challenges in computer-aided design for VLSI circuits (very large-scale integrated circuits). This work address the min-cut balanced circuit partitioning problem- dividing the graph that models the circuit into almost equal sized k sub-graphs while minimizing the number of edges cut i.e. minimizing the number of edges connecting the sub-graphs. The problem may be formulated as a combinatorial optimization problem. Experimental studies in the literature have shown the problem to be NP-hard and thus it is important to design an efficient heuristic algorithm to solve it. The approach proposed in this study is a parallel implementation of a genetic algorithm, namely an island model. The information exchange between the evolving subpopulations is modeled using a fuzzy controller, which determines an optimal balance between exploration and exploitation of the solution space. The results of simulations show that the proposed algorithm outperforms the standard sequential genetic algorithm both in terms of solution quality and convergence speed. As a direction for future study, this research can be further extended to incorporate local search operators which should include problem-specific knowledge. In addition, the adaptive configuration of mutation and crossover rates is another guidance for future research.
A cluster analysis on road traffic accidents using genetic algorithms
NASA Astrophysics Data System (ADS)
Saharan, Sabariah; Baragona, Roberto
2017-04-01
The analysis of traffic road accidents is increasingly important because of the accidents cost and public road safety. The availability or large data sets makes the study of factors that affect the frequency and severity accidents are viable. However, the data are often highly unbalanced and overlapped. We deal with the data set of the road traffic accidents recorded in Christchurch, New Zealand, from 2000-2009 with a total of 26440 accidents. The data is in a binary set and there are 50 factors road traffic accidents with four level of severity. We used genetic algorithm for the analysis because we are in the presence of a large unbalanced data set and standard clustering like k-means algorithm may not be suitable for the task. The genetic algorithm based on clustering for unknown K, (GCUK) has been used to identify the factors associated with accidents of different levels of severity. The results provided us with an interesting insight into the relationship between factors and accidents severity level and suggest that the two main factors that contributes to fatal accidents are "Speed greater than 60 km h" and "Did not see other people until it was too late". A comparison with the k-means algorithm and the independent component analysis is performed to validate the results.
Genetic model compensation: Theory and applications
NASA Astrophysics Data System (ADS)
Cruickshank, David Raymond
1998-12-01
The adaptive filtering algorithm known as Genetic Model Compensation (GMC) was originally presented in the author's Master's Thesis. The current work extends this earlier work. GMC uses a genetic algorithm to optimize filter process noise parameters in parallel with the estimation of the state and based only on the observational information available to the filter. The original stochastic state model underlying GMC was inherited from the antecedent, non-adaptive Dynamic Model Compensation (DMC) algorithm. The current work develops the stochastic state model from a linear system viewpoint, avoiding the simplifications and approximations of the earlier development, and establishes Riemann sums as unbiased estimators of the stochastic integrals which describe the evolution of the random state components. These are significant developments which provide GMC with a solid theoretical foundation. Orbit determination is the area of application in this work, and two types of problems are studied: real-time autonomous filtering using absolute GPS measurements and precise post-processed filtering using differential GPS measurements. The first type is studied in a satellite navigation simulation in which pseudorange and pseudorange rate measurements are processed by an Extended Kalman Filter which incorporates both DMC and GMC. Both estimators are initialized by a geometric point solution algorithm. Using measurements corrupted by simulated Selective Availability errors, GMC reduces mean RSS position error by 6.4 percent, reduces mean clock bias error by 46 percent, and displays a marked improvement in covariance consistency relative to DMC. To study the second type of problem, GMC is integrated with NASA Jet Propulsion Laboratory's Gipsy/Oasis-II (GOA-II) precision orbit determination program creating an adaptive version of GOA-II's Reduced Dynamic Tracking (RDT) process noise formulation. When run as a sequential estimator with GPS measurements from the TOPEX satellite and
An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.
Yoon, Yourim; Kim, Yong-Hyuk
2013-10-01
Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.
Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including 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. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.
Genetic weighted k-means algorithm for clustering large-scale gene expression data.
Wu, Fang-Xiang
2008-05-28
The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It is sensitive to initial partitions, its result is prone to the local minima, and it is only applicable to data with spherical-shape clusters. The last shortcoming means that we must assume that gene expression data at the different conditions follow the independent distribution with the same variances. Nevertheless, this assumption is not true in practice. In this paper, we propose a genetic weighted K-means algorithm (denoted by GWKMA), which solves the first two problems and partially remedies the third one. GWKMA is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). In GWKMA, each individual is encoded by a partitioning table which uniquely determines a clustering, and three genetic operators (selection, crossover, mutation) and a WKM operator derived from WKMA are employed. The superiority of the GWKMA over the k-means is illustrated on a synthetic and two real-life gene expression datasets. The proposed algorithm has general application to clustering large-scale biological data such as gene expression data and peptide mass spectral data.
NASA Astrophysics Data System (ADS)
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-01-01
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_{n} 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 √3 x √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.
Arteaga-Sierra, F R; Milián, C; Torres-Gómez, I; Torres-Cisneros, M; Moltó, G; Ferrando, A
2014-09-22
We present a numerical strategy to design fiber based dual pulse light sources exhibiting two predefined spectral peaks in the anomalous group velocity dispersion regime. The frequency conversion is based on the soliton fission and soliton self-frequency shift occurring during supercontinuum generation. The optimization process is carried out by a genetic algorithm that provides the optimum input pulse parameters: wavelength, temporal width and peak power. This algorithm is implemented in a Grid platform in order to take advantage of distributed computing. These results are useful for optical coherence tomography applications where bell-shaped pulses located in the second near-infrared window are needed.
Genetic demographic networks: Mathematical model and applications.
Kimmel, Marek; Wojdyła, Tomasz
2016-10-01
Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise
An implementation of continuous genetic algorithm in parameter estimation of predator-prey model
NASA Astrophysics Data System (ADS)
Windarto
2016-03-01
Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The main components of this algorithm are chromosomes population (individuals population), parent selection, crossover to produce new offspring, and random mutation. In this paper, continuous genetic algorithm was implemented to estimate parameters in a predator-prey model of Lotka-Volterra type. 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, the algorithms can estimate these parameters well.
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.
Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm
NASA Astrophysics Data System (ADS)
Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui
2017-05-01
The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.
Polyphase orthogonal waveform optimization for MIMO-SAR using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Mehany, Wael; Jiao, Licheng; Hussien, Khaled
2014-04-01
A Multi-Input Multi-Output (MIMO) radar can be used to form a synthetic aperture for high resolution imaging. To successfully utilize the MIMO Synthetic Aperture Radar (SAR) system for practical imaging application, the orthogonal waveform design plays a critical role in image formation. Focusing on the SAR application, a definition for synthetic Integrated Side-Lobe level Ratio (ISLR) is proposed. In this paper a cost function containing ISLR and Peak Side-Lobe level Ratio (PSLR) is presented. A Genetic Algorithm (GA) is presented to numerically optimize orthogonal polyphase code sets design. The obtained waveform can be implemented for MIMO-SAR systems to improve the resolution. The simulation results show that the superiority of the proposed algorithm over other algorithms for the design of polyphase code sets used in MIMO-SAR.
EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME
J. THEILER; ET AL
1999-06-01
The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.
The Genetic Algorithm: A Robust Method for Stress Inversion
NASA Astrophysics Data System (ADS)
Thakur, P.; Srivastava, D. C.; Gupta, P. K.
2016-12-01
The knowledge of stress states in Earth`s crust is a fundamental objective in many tectonic, seismological and engineering geological studies. Geologists and geophysicists routinely practice methods for determination of the stress tensor from inversion of observations on the stress indicators, such as faults, earthquakes and calcite twin lamellae. While the stress inversion is essentially a nonlinear problem, it is commonly solved by linearization, under some assumptions, in most existing methods. These algorithms not only oversimplify the problem but are also vulnerable to entrapment of the solution in a local optimum. We propose a nonlinear heuristic technique, the genetic algorithm method, that searches the global optimum without making any linearizing assumption or simplification. The method mimics the natural evolutionary process of selection, crossover, mutation, and minimises the composite misfit function for searching the global optimum, the fittest stress tensor. The validity of the method is successfully tested on synthetic fault-slip observations in different tectonic settings and also in situations where the observations contain noisy data. These results are compared with those obtained from the other common methods. The genetic algorithm method is superior to other common methods, in particular, in the oblique tectonic settings where none of the principal stresses is directed vertically.
Design of OFDM radar pulses using genetic algorithm based techniques
NASA Astrophysics Data System (ADS)
Lellouch, Gabriel; Mishra, Amit Kumar; Inggs, Michael
2016-08-01
The merit of evolutionary algorithms (EA) to solve convex optimization problems is widely acknowledged. In this paper, a genetic algorithm (GA) optimization based waveform design framework is used to improve the features of radar pulses relying on the orthogonal frequency division multiplexing (OFDM) structure. Our optimization techniques focus on finding optimal phase code sequences for the OFDM signal. Several optimality criteria are used since we consider two different radar processing solutions which call either for single or multiple-objective optimizations. When minimization of the so-called peak-to-mean envelope power ratio (PMEPR) single-objective is tackled, we compare our findings with existing methods and emphasize on the merit of our approach. In the scope of the two-objective optimization, we first address PMEPR and peak-to-sidelobe level ratio (PSLR) and show that our approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) provides design solutions with noticeable improvements as opposed to random sets of phase codes. We then look at another case of interest where the objective functions are two measures of the sidelobe level, namely PSLR and the integrated-sidelobe level ratio (ISLR) and propose to modify the NSGA-II to include a constrain on the PMEPR instead. In the last part, we illustrate via a case study how our encoding solution makes it possible to minimize the single objective PMEPR while enabling a target detection enhancement strategy, when the SNR metric would be chosen for the detection framework.
Library design using genetic algorithms for catalyst discovery and optimization
NASA Astrophysics Data System (ADS)
Clerc, Frederic; Lengliz, Mourad; Farrusseng, David; Mirodatos, Claude; Pereira, Sílvia R. M.; Rakotomalala, Ricco
2005-06-01
This study reports a detailed investigation of catalyst library design by genetic algorithm (GA). A methodology for assessing GA configurations is described. Operators, which promote the optimization speed while being robust to noise and outliers, are revealed through statistical studies. The genetic algorithms were implemented in GA platform software called OptiCat, which enables the construction of custom-made workflows using a tool box of operators. Two separate studies were carried out (i) on a virtual benchmark and (ii) on real surface response which is derived from HT screening. Additionally, we report a methodology to model a complex surface response by binning the search space in small zones that are then independently modeled by linear regression. In contrast to artificial neural networks, this approach allows one to obtain an explicit model in an analogical form that can be further used in Excel or entered in OptiCat to perform simulations. While speeding the implementation of a hybrid algorithm combining a GA with a knowledge-based extraction engine is described, while speeding up the optimization process by means of virtual prescreening the hybrid GA enables one to open the "black-box" by providing knowledge as a set of association rules.
Genetic Applications in Avian Conservation
Haig, Susan M.; Bronaugh, Whitcomb M.; Crowhurst, Rachel S.; D'Elia, Jesse; Eagles-Smith, Collin A.; Epps, Clinton W.; Knaus, Brian; Miller, Mark P.; Moses, Michael L.; Oyler-McCance, Sara; Robinson, W. Douglas; Sidlauskas, Brian
2011-01-01
A fundamental need in conserving species and their habitats is defining distinct entities that range from individuals to species to ecosystems and beyond (Table 1; Ryder 1986, Moritz 1994, Mayden and Wood 1995, Haig and Avise 1996, Hazevoet 1996, Palumbi and Cipriano 1998, Hebert et al. 2004, Mace 2004, Wheeler et al. 2004, Armstrong and Ball 2005, Baker 2008, Ellis et al. 2010, Winker and Haig 2010). Rapid progression in this interdisciplinary field continues at an exponential rate; thus, periodic updates on theory, techniques, and applications are important for informing practitioners and consumers of genetic information. Here, we outline conservation topics for which genetic information can be helpful, provide examples of where genetic techniques have been used best in avian conservation, and point to current technical bottlenecks that prevent better use of genomics to resolve conservation issues related to birds. We hope this review will provide geneticists and avian ecologists with a mutually beneficial dialogue on how this integrated field can solve current and future problems.
Genetic applications in avian conservation
Haig, Susan M.; Bronaugh, Whitcomb M.; Crowhurst, Rachel S.; D'Elia, Jesse; Eagles-Smith, Collin A.; Epps, Clinton W.; Knaus, Brian; Miller, Mark P.; Moses, Michael L.; Oyler-McCance, Sara; Robinson, W. Douglas; Sidlauskas, Brian
2011-01-01
A fundamental need in conserving species and their habitats is defining distinct entities that range from individuals to species to ecosystems and beyond (Table 1; Ryder 1986, Moritz 1994, Mayden and Wood 1995, Haig and Avise 1996, Hazevoet 1996, Palumbi and Cipriano 1998, Hebert et al. 2004, Mace 2004, Wheeler et al. 2004, Armstrong and Ball 2005, Baker 2008, Ellis et al. 2010, Winker and Haig 2010). Rapid progression in this interdisciplinary field continues at an exponential rate; thus, periodic updates on theory, techniques, and applications are important for informing practitioners and consumers of genetic information. Here, we outline conservation topics for which genetic information can be helpful, provide examples of where genetic techniques have been used best in avian conservation, and point to current technical bottlenecks that prevent better use of genomics to resolve conservation issues related to birds. We hope this review will provide geneticists and avian ecologists with a mutually beneficial dialogue on how this integrated field can solve current and future problems.
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.
NASA Astrophysics Data System (ADS)
Zhou, Mandi; Shu, Jiong; Chen, Zhigang; Ji, Minhe
2012-11-01
Hyperspectral imagery has been widely used in terrain classification for its high resolution. Urban vegetation, known as an essential part of the urban ecosystem, can be difficult to discern due to high similarity of spectral signatures among some land-cover classes. In this paper, we investigate a hybrid approach of the genetic-algorithm tuned fuzzy support vector machine (GA-FSVM) technique and apply it to urban vegetation classification from aerial hyperspectral urban imagery. The approach adopts the genetic algorithm to optimize parameters of support vector machine, and employs the K-nearest neighbor algorithm to calculate the membership function for each fuzzy parameter, aiming to reduce the effects of the isolated and noisy samples. Test data come from push-broom hyperspectral imager (PHI) hyperspectral remote sensing image which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. Experimental results show the GA-FSVM model generates overall accuracy of 71.2%, outperforming the maximum likelihood classifier with 49.4% accuracy and the artificial neural network method with 60.8% accuracy. It indicates GA-FSVM is a promising model for vegetation classification from hyperspectral urban data, and has good advantage in the application of classification involving abundant mixed pixels and small samples problem.
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.
Optimization in optical systems revisited: Beyond genetic algorithms
NASA Astrophysics Data System (ADS)
Gagnon, Denis; Dumont, Joey; Dubé, Louis
2013-05-01
Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).
A genetic algorithm based method for docking flexible molecules
Judson, R.S.; Jaeger, E.P.; Treasurywala, A.M.
1993-11-01
The authors describe a computational method for docking flexible molecules into protein binding sites. The method uses a genetic algorithm (GA) to search the combined conformation/orientation space of the molecule to find low energy conformation. Several techniques are described that increase the efficiency of the basic search method. These include the use of several interacting GA subpopulations or niches; the use of a growing algorithm that initially docks only a small part of the molecule; and the use of gradient minimization during the search. To illustrate the method, they dock Cbz-GlyP-Leu-Leu (ZGLL) into thermolysin. This system was chosen because a well refined crystal structure is available and because another docking method had previously been tested on this system. Their method is able to find conformations that lie physically close to and in some cases lower in energy than the crystal conformation in reasonable periods of time on readily available hardware.
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.
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.
Genetic algorithms for the construction of D-optimal designs
Heredia-Langner, Alejandro; Carlyle, W M.; Montgomery, D C.; Borror, Connie M.; Runger, George C.
2003-01-01
Computer-generated designs are useful for situations where standard factorial, fractional factorial or response surface designs cannot be easily employed. Alphabetically-optimal designs are the most widely used type of computer-generated designs, and of these, the D-optimal (or D-efficient) class of designs are extremely popular. D-optimal designs are usually constructed by algorithms that sequentially add and delete points from a potential design based using a candidate set of points spaced over the region of interest. We present a technique to generate D-efficient designs using genetic algorithms (GA). This approach eliminates the need to explicitly consider a candidate set of experimental points and it can handle highly constrained regions while maintaining a level of performance comparable to more traditional design construction techniques.
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
Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment.
Naznin, Farhana; Sarker, Ruhul; Essam, Daryl
2011-08-25
Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research.
Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment
2011-01-01
Background Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. Results In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. Conclusions The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research. PMID:21867510
[Genetic algorithm for fermentation kinetics of submerged fermentation by Morchella].
Wang, Ying; Piao, Meizi; Sun, Yonghai
2008-08-01
Fermentation kinetics is important for optimizing control and up-scaling fermentation process. We studied submerged fermentation kinetics of Morchella. Applying the genetic Algorithm in the Matlab software platform, we compared suitability of the Monod and Logistic models, both are commonly used in process of fungal growth, to describe Morchella growth kinetics. Meanwhile, we evaluated parameters involved in the models for Morchella growth, EPS production and substrate consumption. The results indicated that Logistic model fit better with the experimental data. The average error of this model was 5.8%. This kinetics model can be useful for optimizing and up-scaling fungal fermentation process.
Random search optimization based on genetic algorithm and discriminant function
NASA Technical Reports Server (NTRS)
Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.
1990-01-01
The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.
Optimization of reinforced soil embankments by genetic algorithm
NASA Astrophysics Data System (ADS)
Ponterosso, P.; Fox, D. St. J.
2000-04-01
A Genetic Algorithm (GA) is described, which produces solutions to the cost optimization problem of reinforcement layout for reinforced soil slopes. These solutions incorporate different types of reinforcement within a single slope. The GA described is implemented with the aim of optimizing the cost of materials for the preliminary layout of reinforced soil embankments. The slope design method chosen is the U.K. Department of Transport HA 68/94 Design Methods for the Reinforcement of Highway Slopes by Reinforced Soil and Soil Nailing Techniques. The results confirm that there is a role for the GA in optimization of reinforced soil design.
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.
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.
Random search optimization based on genetic algorithm and discriminant function
NASA Technical Reports Server (NTRS)
Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.
1990-01-01
The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.
Evaluation of Mechanical Losses in Piezoelectric Plates using Genetic algorithm
NASA Astrophysics Data System (ADS)
Arnold, F. J.; Gonçalves, M. S.; Massaro, F. R.; Martins, P. S.
Numerical methods are used for the characterization of piezoelectric ceramics. A procedure based on genetic algorithm is applied to find the physical coefficients and mechanical losses. The coefficients are estimated from a minimum scoring of cost function. Electric impedances are calculated from Mason's model including mechanical losses constant and dependent on frequency as a linear function. The results show that the electric impedance percentage error in the investigated interval of frequencies decreases when mechanical losses depending on frequency are inserted in the model. A more accurate characterization of the piezoelectric ceramics mechanical losses should be considered as frequency dependent.
Genetic Algorithm based Decentralized PI Type Controller: Load Frequency Control
NASA Astrophysics Data System (ADS)
Dwivedi, Atul; Ray, Goshaidas; Sharma, Arun Kumar
2016-12-01
This work presents a design of decentralized PI type Linear Quadratic (LQ) controller based on genetic algorithm (GA). The proposed design technique allows considerable flexibility in defining the control objectives and it does not consider any knowledge of the system matrices and moreover it avoids the solution of algebraic Riccati equation. To illustrate the results of this work, a load-frequency control problem is considered. Simulation results reveal that the proposed scheme based on GA is an alternative and attractive approach to solve load-frequency control problem from both performance and design point of views.
Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.
2017-02-01
Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.
Hydrogenerator system identification using a simple genetic algorithm
Wrate, C.A.; Wozniak, L.
1997-03-01
This paper investigates an identification procedure for a hydrogenerator plant using an adaptive technique. The procedure operates on field data consisting of sampled gate position and electrical frequency. The field data was recorded while the plant was operating under various load conditions. The procedure adapted to ongoing plant changes by continuously updating the identification results. It is shown that the adaptive technique, in this case genetic algorithm based, was capable of identifying the hydrogenerator system and following plant parameter changes while the plant operated under conditions of sufficient frequency excursions. These conditions include off-line and isolated network operation where effective control is critical.
Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Tang, Xinggang; Zhang, Weihong; Zhu, Jihong
A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The genetic algorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.
Genetic algorithm for flaw characterization based on thermographic inspection data
NASA Astrophysics Data System (ADS)
Rajic, Nikolas
2002-03-01
A genetic algorithm is applied to the task of aw characterization based on active thermal inspection data. Experimental observations pertaining to test samples with axisymmetric inclusion aws are used to validate the approach, with encouraging results. Errors in identification are discussed and can, at least in part, be attributed to the use of a relatively primitive fitness function formulation and the assumption in the heat-transfer model of negligible heat-loss through convection and radiation. The general approach however appears sound and can be modified to include important heat-transfer mechanisms and alternative fitness function formulations.
A versatile multi-objective FLUKA optimization using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vlachoudis, Vasilis; Antoniucci, Guido Arnau; Mathot, Serge; Kozlowska, Wioletta Sandra; Vretenar, Maurizio
2017-09-01
Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.
A Genetic Algorithm Approach for Modeling a Grounding Electrode
NASA Astrophysics Data System (ADS)
Mishra, Arbind Kumar; Nagaoka, Naoto; Ametani, Akihiro
This paper has proposed a genetic algorithm based approach to determine a grounding electrode model circuit composed of resistances, inductances and capacitances. The proposed methodology determines the model circuit parameters based on a general ladder circuit directly from a measured result. Transient voltages of some electrodes were measured when applying a step like current. An EMTP simulation of a transient voltage on the grounding electrode has been carried out by adopting the proposed model circuits. The accuracy of the proposed method has been confirmed to be high in comparison with the measured transient voltage.
Genetic algorithms for dipole location of fetal magnetocardiography.
Escalona-Vargas, D; Murphy, P; Lowery, C L; Eswaran, H
2016-08-01
In this paper, we explore the use of Maximum Likelihood (ML) method with Genetic Algorithms (GA) as global optimization procedure for source reconstruction in fetal magnetocardiography (fMCG) data. A multiple equivalent current dipole (ECD) model was used for sources active in different time samples. Inverse solutions across time were obtained for a single-dipole approximation to estimate the trajectory of the dipole position. We compared the GA and SIMPLEX methods in a simulation environment under noise conditions. Methods are applied on a real fMCG data. Results show robust estimators of the cardiac sources when GA is used as optimization technique.
Recognition of digital characteristics based new improved genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Meng; Xu, Guoqiang; Lin, Zihao
2017-08-01
In the field of digital signal processing, Estimating the characteristics of signal modulation parameters is an significant research direction. The paper determines the set of eigenvalue which can show the difference of the digital signal modulation based on the deep research of the new improved genetic algorithm. Firstly take them as the best gene pool; secondly, The best gene pool will be changed in the genetic evolvement by selecting, overlapping and eliminating each other; Finally, Adapting the strategy of futher enhance competition and punishment to more optimizer the gene pool and ensure each generation are of high quality gene. The simulation results show that this method not only has the global convergence, stability and faster convergence speed.
Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm
NASA Astrophysics Data System (ADS)
Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian
2017-03-01
DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.
The mGA1.0: A common LISP implementation of a messy genetic algorithm
NASA Technical Reports Server (NTRS)
Goldberg, David E.; Kerzic, Travis
1990-01-01
Genetic algorithms (GAs) are finding increased application in difficult search, optimization, and machine learning problems in science and engineering. Increasing demands are being placed on algorithm performance, and the remaining challenges of genetic algorithm theory and practice are becoming increasingly unavoidable. Perhaps the most difficult of these challenges is the so-called linkage problem. Messy GAs were created to overcome the linkage problem of simple genetic algorithms by combining variable-length strings, gene expression, messy operators, and a nonhomogeneous phasing of evolutionary processing. Results on a number of difficult deceptive test functions are encouraging with the mGA always finding global optima in a polynomial number of function evaluations. Theoretical and empirical studies are continuing, and a first version of a messy GA is ready for testing by others. A Common LISP implementation called mGA1.0 is documented and related to the basic principles and operators developed by Goldberg et. al. (1989, 1990). Although the code was prepared with care, it is not a general-purpose code, only a research version. Important data structures and global variations are described. Thereafter brief function descriptions are given, and sample input data are presented together with sample program output. A source listing with comments is also included.
Variational algorithms for nonlinear smoothing applications
NASA Technical Reports Server (NTRS)
Bach, R. E., Jr.
1977-01-01
A variational approach is presented for solving a nonlinear, fixed-interval smoothing problem with application to offline processing of noisy data for trajectory reconstruction and parameter estimation. The nonlinear problem is solved as a sequence of linear two-point boundary value problems. Second-order convergence properties are demonstrated. Algorithms for both continuous and discrete versions of the problem are given, and example solutions are provided.
A., Javadpour; A., Mohammadi
2016-01-01
Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629
A, Javadpour; A, Mohammadi
2016-06-01
Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer's disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.
The design and implementation of MPI master-slave parallel genetic algorithm
NASA Astrophysics Data System (ADS)
Liu, Shuping; Cheng, Yanliu
2013-03-01
In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic genetic algorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of maximum value problems (Rosen brocks function) .And we acquire the factors influencing the master-slave parallel genetic algorithm by deriving from the analysis of test data. The experimental data shows that the balanced hardware configuration and software design optimization can improve the performance of system in the complexity of the computing environment using the master-slave parallel genetic algorithms.
Clinical applications of preimplantation genetic testing.
Brezina, Paul R; Kutteh, William H
2015-02-19
Genetic diagnostic technologies are rapidly changing the way medicine is practiced. Preimplantation genetic testing is a well established application of genetic testing within the context of in vitro fertilization cycles. It involves obtaining a cell(s) from a developing embryo in culture, which is then subjected to genetic diagnostic analysis; the resulting information is used to guide which embryos are transferred into the uterus. The potential applications and use of this technology have increased in recent years. Experts agree that preimplantation genetic diagnosis is clinically appropriate for many known genetic disorders. However, some applications of such testing, such as preimplantation genetic screening for aneuploidy, remain controversial. Clinical data suggest that preimplantation genetic screening may be useful, but further studies are needed to quantify the size of the effect and who would benefit most. © BMJ Publishing Group Ltd 2015.
Application of genetic based fuzzy systems to hydroelectric generation scheduling
Huang, S.J.
1999-09-01
An application of genetic based fuzzy systems to hydroelectric generation scheduling is presented in this paper. In the proposed approach, the system was fuzzified with respect to objectives and constraints. A genetic algorithm was included to further enhance the process of tuning membership functions. By this way, membership mappings for those important parameters can be optimally adjusted. The computation performance is thus improved. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results have demonstrated are feasibility and effectiveness of the proposed approach for the applications.
NASA Astrophysics Data System (ADS)
Lin, Jeng-Wen; Shen, Pu Fun; Wen, Hao-Ping
2015-10-01
The application of a repetitive control mechanism for use in a mechanical control system has been a topic of investigation. The fundamental purpose of repetitive control is to eliminate disturbances in a mechanical control system. This paper presents two different repetitive control laws using individual types of basis function feedback and their combinations. These laws adjust the command given to a feedback control system to eliminate tracking errors, generally resulting from periodic disturbance. Periodic errors can be reduced through linear basis functions using regression and a genetic algorithm. The results illustrate that repetitive control is most effective method for eliminating disturbances. When the data are stabilized, the tracking error of the obtained convergence value, 10-14, is the optimal solution, verifying that the proposed regression and genetic algorithm can satisfactorily reduce periodic errors.
Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.
A genetic algorithm approach to optimization for the radiological worker allocation problem.
Chen, Y; Narita, M; Tsuji, M; Sa, S
1996-02-01
This paper describes a new approach to the radiological worker allocation problem using a multiple objective genetic algorithm. The worker allocation problem in radiological facilities involves various types of constraints and even mutually conflicting ones, such as individual dose limits, working time limits, etc. A major difficulty of this highly working time limits, etc. A major difficulty of this highly constrained problem is the way of finding an optimal solution in the huge search space where a large proportion of solutions are not feasible because some of the constraints cannot be satisfied. The paper proposes a model of evolution to establish an optimal assignment efficiently, based on the biological insights into the evolutionary process and heuristic ideas. The experimental results show a very rapid evolution to produce feasible solutions, and the application of multiple evaluation functions converges the feasible solutions to good ones. The genetic algorithm approach was found to be superior to the goal programming and simplex methods.
Robust Flight Path Determination for Mars Precision Landing Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Bayard, David S.; Kohen, Hamid
1997-01-01
This paper documents the application of genetic algorithms (GAs) to the problem of robust flight path determination for Mars precision landing. The robust flight path problem is defined here as the determination of the flight path which delivers a low-lift open-loop controlled vehicle to its desired final landing location while minimizing the effect of perturbations due to uncertainty in the atmospheric model and entry conditions. The genetic algorithm was capable of finding solutions which reduced the landing error from 111 km RMS radial (open-loop optimal) to 43 km RMS radial (optimized with respect to perturbations) using 200 hours of computation on an Ultra-SPARC workstation. Further reduction in the landing error is possible by going to closed-loop control which can utilize the GA optimized paths as nominal trajectories for linearization.
Analytical optimal pulse shapes obtained with the aid of genetic algorithms
Guerrero, Rubén D.; Arango, Carlos A.; Reyes, Andrés
2015-09-28
We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.
Analytical optimal pulse shapes obtained with the aid of genetic algorithms
NASA Astrophysics Data System (ADS)
Guerrero, Rubén D.; Arango, Carlos A.; Reyes, Andrés
2015-09-01
We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.