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.
Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Panicker, Shina; Vijay Kumar, T. V.
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513
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.
Optimizing scheduling problem using an estimation of distribution algorithm and genetic algorithm
NASA Astrophysics Data System (ADS)
Qun, Jiang; Yang, Ou; Dong, Shi-Du
2007-12-01
This paper presents a methodology for using heuristic search methods to optimize scheduling problem. Specifically, an Estimation of Distribution Algorithm (EDA)- Population Based Incremental Learning (PBIL), and Genetic Algorithm (GA) have been applied to finding effective arrangement of curriculum schedule of Universities. To our knowledge, EDAs have been applied to fewer real world problems compared to GAs, and the goal of the present paper is to expand the application domain of this technique. The experimental results indicate a good applicability of PBIL to optimize scheduling problem.
Virus evolutionary genetic algorithm for task collaboration of logistics distribution
NASA Astrophysics Data System (ADS)
Ning, Fanghua; Chen, Zichen; Xiong, Li
2005-12-01
In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.
Implementation of genetic algorithm for distribution systems loss minimum re-configuration
Nara, K.; Shiose, A. ); Kitagawa, M.; Ishihara, T. )
1992-08-01
In this paper, a distribution systems loss minimum reconfiguration method by genetic algorithm is proposed. The problem is a complex mixed integer programming problem and is very difficult to solve by a mathematical programming approach. A genetic algorithm (GA) is a search or optimization algorithm based on the mechanics of natural selection and natural genetics. Since GA is suitable to solve combinatorial optimization problems, it can be successfully applied to problems of loss minimum in distribution systems. Numerical examples demonstrate the validity and effectiveness of the proposed methodology.
NASA Astrophysics Data System (ADS)
Fustes, Diego; Ordóñez, Diego; Dafonte, Carlos; Manteiga, Minia; Arcay, Bernardino
This work presents an algorithm that was developed to select the most relevant areas of a stellar spectrum to extract its basic atmospheric parameters. We consider synthetic spectra obtained from models of stellar atmospheres in the spectral region of the radial velocity spectrograph instrument of the European Space Agency's Gaia space mission. The algorithm that demarcates the areas of the spectra sensitive to each atmospheric parameter (effective temperature and gravity, metallicity, and abundance of alpha elements) is a genetic algorithm, and the parameterization takes place through the learning of artificial neural networks. Due to the high computational cost of processing, we present a distributed implementation in both multiprocessor and multicomputer environments.
Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872
Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872
Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven
2010-05-01
Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.
NASA Astrophysics Data System (ADS)
Castellini, P.; Cecchini, S.; Stroppa, L.; Paone, N.
2015-02-01
The paper presents an adaptive illumination system for image quality enhancement in vision-based quality control systems. In particular, a spatial modulation of illumination intensity is proposed in order to improve image quality, thus compensating for different target scattering properties, local reflections and fluctuations of ambient light. The desired spatial modulation of illumination is obtained by a digital light projector, used to illuminate the scene with an arbitrary spatial distribution of light intensity, designed to improve feature extraction in the region of interest. The spatial distribution of illumination is optimized by running a genetic algorithm. An image quality estimator is used to close the feedback loop and to stop iterations once the desired image quality is reached. The technique proves particularly valuable for optimizing the spatial illumination distribution in the region of interest, with the remarkable capability of the genetic algorithm to adapt the light distribution to very different target reflectivity and ambient conditions. The final objective of the proposed technique is the improvement of the matching score in the recognition of parts through matching algorithms, hence of the diagnosis of machine vision-based quality inspections. The procedure has been validated both by a numerical model and by an experimental test, referring to a significant problem of quality control for the washing machine manufacturing industry: the recognition of a metallic clamp. Its applicability to other domains is also presented, specifically for the visual inspection of shoes with retro-reflective tape and T-shirts with paillettes.
NASA Astrophysics Data System (ADS)
Biswas, S.; Goswami, S. K.
2010-10-01
In the present paper an attempt has been made to place the distributed generation at an optimal location so as to improve the technical as well as economical performance. Among technical issues the sag performance and the loss have been considered. Genetic algorithm method has been used as the optimization technique in this problem. For sag analysis the impact of 3-phase symmetrical short circuit faults is considered. Total load disturbed during the faults is considered as an indicator of sag performance. The solution algorithm is demonstrated on a 34 bus radial distribution system with some lateral branches. For simplicity only one DG of predefined capacity is considered. MATLAB has been used as the programming environment.
Efficient use of hybrid Genetic Algorithms in the gain optimization of distributed Raman amplifiers.
Neto, B; Teixeira, A L J; Wada, N; André, P S
2007-12-24
In this paper, we propose an efficient and accurate method that combines the Genetic Algorithm (GA) with the Nelder-Mead method in order to obtain the gain optimization of distributed Raman amplifiers. By using these two methods together, the advantages of both are combined: the convergence of the GA and the high accuracy of the Nelder-Mead. To enhance the convergence of the GA, several features were examined and correlated with fitting errors. It is also shown that when the right moment to switch between methods is chosen, the computation time can be reduced by a factor of two. PMID:19551045
Probability distribution functions for unit hydrographs with optimization using genetic algorithm
NASA Astrophysics Data System (ADS)
Ghorbani, Mohammad Ali; Singh, Vijay P.; Sivakumar, Bellie; H. Kashani, Mahsa; Atre, Atul Arvind; Asadi, Hakimeh
2015-04-01
A unit hydrograph (UH) of a watershed may be viewed as the unit pulse response function of a linear system. In recent years, the use of probability distribution functions (pdfs) for determining a UH has received much attention. In this study, a nonlinear optimization model is developed to transmute a UH into a pdf. The potential of six popular pdfs, namely two-parameter gamma, two-parameter Gumbel, two-parameter log-normal, two-parameter normal, three-parameter Pearson distribution, and two-parameter Weibull is tested on data from the Lighvan catchment in Iran. The probability distribution parameters are determined using the nonlinear least squares optimization method in two ways: (1) optimization by programming in Mathematica; and (2) optimization by applying genetic algorithm. The results are compared with those obtained by the traditional linear least squares method. The results show comparable capability and performance of two nonlinear methods. The gamma and Pearson distributions are the most successful models in preserving the rising and recession limbs of the unit hydographs. The log-normal distribution has a high ability in predicting both the peak flow and time to peak of the unit hydrograph. The nonlinear optimization method does not outperform the linear least squares method in determining the UH (especially for excess rainfall of one pulse), but is comparable.
NASA Astrophysics Data System (ADS)
Lindsay, A.; McCloskey, J.; Nic Bhloscaidh, M.
2016-03-01
Examining fault activity over several earthquake cycles is necessary for long-term modeling of the fault strain budget and stress state. While this requires knowledge of coseismic slip distributions for successive earthquakes along the fault, these exist only for the most recent events. However, overlying the Sunda Trench, sparsely distributed coral microatolls are sensitive to tectonically induced changes in relative sea levels and provide a century-spanning paleogeodetic and paleoseismic record. Here we present a new technique called the Genetic Algorithm Slip Estimator to constrain slip distributions from observed surface deformations of corals. We identify a suite of models consistent with the observations, and from them we compute an ensemble estimate of the causative slip. We systematically test our technique using synthetic data. Applying the technique to observed coral displacements for the 2005 Nias-Simeulue earthquake and 2007 Mentawai sequence, we reproduce key features of slip present in previously published inversions such as the magnitude and location of slip asperities. From the displacement data available for the 1797 and 1833 Mentawai earthquakes, we present slip estimates reproducing observed displacements. The areas of highest modeled slip in the paleoearthquake are nonoverlapping, and our solutions appear to tile the plate interface, complementing one another. This observation is supported by the complex rupture pattern of the 2007 Mentawai sequence, underlining the need to examine earthquake occurrence through long-term strain budget and stress modeling. Although developed to estimate earthquake slip, the technique is readily adaptable for a wider range of applications.
Using distributed genetic algorithms in three-dimensional bin packing for rapid prototyping machines
NASA Astrophysics Data System (ADS)
Lewis, James E.; Ragade, Rammohan K.; Kumar, Anup; Biles, William E.; Ikonen, Ilkka T.
1998-10-01
Genetic algorithms (GAs) are excellent approaches to solving complex problems in optimization with difficult constraints, and in high state space dimensionality problems. The classic bin-packing optimization problem has been shown to be a NP- complete problem. There are GA applications to variations of the bin-packing problem for stock cutting, vehicle loading, air container loading, scheduling, and the knapsack problem. Mostly, these are based on a 1D or 2D considerations. Ikonen et. al. have developed a GA for rapid prototyping called GARP, which utilizes a 3D chromosome structure for the bin- packing of the Sinterstation 2000's build cylinder. GARP allows the Sinterstation to be used more productively. The GARP application was developed for a single CPU machine. Anticipating greater use of time compression technologies, this paper examines the framework necessary to reduce GARP's execution time. This framework is necessary to speed-up the bin-packing evaluation, by the use of distributed or parallel GAs. In this paper, a framework for distribution techniques to improve the efficiency of GARP, and to improve the quality of GARPis solutions is proposed.
Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms
Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert
2013-01-01
This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099
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.
General cardinality genetic algorithms
Koehler; Bhattacharyya; Vose
1997-01-01
A complete generalization of the Vose genetic algorithm model from the binary to higher cardinality case is provided. Boolean AND and EXCLUSIVE-OR operators are replaced by multiplication and addition over rings of integers. Walsh matrices are generalized with finite Fourier transforms for higher cardinality usage. Comparison of results to the binary case are provided. PMID:10021767
Choi, D.S.; Hasegawa, Jun; Kim, C.S.
1995-12-31
Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches, and is usually done for loss reducing or for load balancing in the system. This paper presents a new method which applies a genetic algorithm for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization reconfiguration problem. In addition, the proposed method introduces a new limited life feature for performing natural selection of individuals. Simulations were carried out in order to verify the effectiveness of the proposed method. These results showed that the proposed method is effective in dealing with the problems of homogeneity and genetic drift associated with the population in the initial state.
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)
Gladwin, D.; Stewart, P.; Stewart, J.
2011-02-01
This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control
NASA Astrophysics Data System (ADS)
Izadi, Arman; Kimiagari, Ali Mohammad
2014-05-01
Distribution network design as a strategic decision has long-term effect on tactical and operational supply chain management. In this research, the location-allocation problem is studied under demand uncertainty. The purposes of this study were to specify the optimal number and location of distribution centers and to determine the allocation of customer demands to distribution centers. The main feature of this research is solving the model with unknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set of possible scenarios for customer demands is created based on the Monte Carlo simulation. The coefficient of variation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network is defined based on the concept of robust optimization. The best structure is identified using genetic algorithms and 14 % reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created by fluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logistical system. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.
NASA Astrophysics Data System (ADS)
Izadi, Arman; Kimiagari, Ali mohammad
2014-01-01
Distribution network design as a strategic decision has long-term effect on tactical and operational supply chain management. In this research, the location-allocation problem is studied under demand uncertainty. The purposes of this study were to specify the optimal number and location of distribution centers and to determine the allocation of customer demands to distribution centers. The main feature of this research is solving the model with unknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set of possible scenarios for customer demands is created based on the Monte Carlo simulation. The coefficient of variation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network is defined based on the concept of robust optimization. The best structure is identified using genetic algorithms and 14% reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created by fluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logistical system. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.
NASA Astrophysics Data System (ADS)
Arpaia, P.; Cimmino, P.; Girone, M.; Commara, G. La; Maisto, D.; Manna, C.; Pezzetti, M.
2014-09-01
Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy.
Arpaia, P; Cimmino, P; Girone, M; La Commara, G; Maisto, D; Manna, C; Pezzetti, M
2014-09-01
Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy. PMID:25273768
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.
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.
NASA Astrophysics Data System (ADS)
Li, Cheng
Wind farms, photovoltaic arrays, fuel cells, and micro-turbines are all considered to be Distributed Generation (DG). DG is defined as the generation of power which is dispersed throughout a utility's service territory and either connected to the utility's distribution system or isolated in a small grid. This thesis addresses modeling and economic issues pertaining to the optimal reactive power planning for distribution system with wind power generation (WPG) units. Wind farms are inclined to cause reverse power flows and voltage variations due to the random-like outputs of wind turbines. To deal with this kind of problem caused by wide spread usage of wind power generation, this thesis investigates voltage and reactive power controls in such a distribution system. Consequently static capacitors (SC) and transformer taps are introduced into the system and treated as controllers. For the purpose of getting optimum voltage and realizing reactive power control, the research proposes a proper coordination among the controllers like on-load tap changer (OLTC), feeder-switched capacitors. What's more, in order to simulate its uncertainty, the wind power generation is modeled by the Markov model. In that way, calculating the probabilities for all the scenarios is possible. Some outputs with consecutive and discrete values have been used for transition between successive time states and within state wind speeds. The thesis will describe the method to generate the wind speed time series from the transition probability matrix. After that, utilizing genetic algorithm, the optimal locations of SCs, the sizes of SCs and transformer taps are determined so as to minimize the cost or minimize the power loss, and more importantly improve voltage profiles. The applicability of the proposed method is verified through simulation on a 9-bus system and a 30-bus system respectively. At last, the simulation results indicate that as long as the available capacitors are able to sufficiently
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.
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.
Scheduling Jobs with Genetic Algorithms
NASA Astrophysics Data System (ADS)
Ferrolho, António; Crisóstomo, Manuel
Most scheduling problems are NP-hard, the time required to solve the problem optimally increases exponentially with the size of the problem. Scheduling problems have important applications, and a number of heuristic algorithms have been proposed to determine relatively good solutions in polynomial time. Recently, genetic algorithms (GA) are successfully used to solve scheduling problems, as shown by the growing numbers of papers. GA are known as one of the most efficient algorithms for solving scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.
Simultaneous stabilization using genetic algorithms
Benson, R.W.; Schmitendorf, W.E. . Dept. of Mechanical Engineering)
1991-01-01
This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.
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.
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.
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…
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.
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
application of hybrid genetic algorithms in seismic tomography is examined and the efficiency of least squares and genetic methods as representative of the local and global optimization, respectively, is presented and evaluated. The robustness of both optimization methods has been tested and compared for the same source-receiver geometry and characteristics of the model structure (anomalies, etc.). A set of seismic refraction synthetic (noise free) data was used for modeling. Specifically, cross-well, down-hole and typical refraction studies using 24 geophones and 5 shoots were used to confirm the applicability of the genetic algorithms in seismic tomography. To solve the forward modeling and estimate the traveltimes, the revisited ray bending method was used supplemented by an approximate computation of the first Fresnel volume. The root mean square (rms) error as the misfit function was used and calculated for the entire random velocity model for each generation. After the end of each generation and based on the misfit of the individuals (velocity models), the selection, crossover and mutation (typical process steps of genetic algorithms) were selected continuing the evolution theory and coding the new generation. To optimize the computation time, since the whole procedure is quite time consuming, the Matlab Distributed Computing Environment (MDCE) was used in a multicore engine. During the tests, we noticed that the fast convergence that the algorithm initially exhibits (first 5 generations) is followed by progressively slower improvements of the reconstructed velocity models. Thus, to improve the final tomographic models, a hybrid genetic algorithm (GA) approach was adopted by combining the GAs with a local optimization method after several generations, on the basis of the convergence of the resulting models. This approach is shown to be efficient, as it directs the solution search towards a model region close to the global minimum solution.
Technology Transfer Automated Retrieval System (TEKTRAN)
This study explored the application of a multi-objective evolutionary algorithm (MOEA) and Pareto ordering in the multiple-objective automatic calibration of the Soil and Water Assessment Tool (SWAT). SWAT was calibrated in the Calapooia watershed, Oregon, USA, with two different pairs of objective ...
Genetic Algorithms with Local Minimum Escaping Technique
NASA Astrophysics Data System (ADS)
Tamura, Hiroki; Sakata, Kenichiro; Tang, Zheng; Ishii, Masahiro
In this paper, we propose a genetic algorithm(GA) with local minimum escaping technique. This proposed method uses the local minimum escaping techique. It can escape from the local minimum by correcting parameters when genetic algorithm falls into a local minimum. Simulations are performed to scheduling problem without buffer capacity using this proposed method, and its validity is shown.
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.
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.
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 supernovae type Ia analysis
Bogdanos, Charalampos; Nesseris, Savvas E-mail: nesseris@nbi.dk
2009-05-15
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) {identical_to} P{sub DE}/{rho}{sub DE}. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Self-adaptive parameters in genetic algorithms
NASA Astrophysics Data System (ADS)
Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain
2004-04-01
Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm"s parameters can be encoded within the chromosome of each individual. The parameters" values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.
Adaptive sensor fusion using genetic algorithms
Fitzgerald, D.S.; Adams, D.G.
1994-08-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.
Genetic Algorithm 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.
Reactive power optimization by genetic algorithm
Iba, Kenji )
1994-05-01
This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most of these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers/banks still remains as a difficult one. The genetic algorithm is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using multiple paths and treat integer problems naturally. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities. Although this method is not as fast as sophisticated traditional methods, the concept is quite promising and useful.
On the scalability of parallel genetic algorithms.
Cantú-Paz, E; Goldberg, D E
1999-01-01
This paper examines the scalability of several types of parallel genetic algorithms (GAs). The objective is to determine the optimal number of processors that can be used by each type to minimize the execution time. The first part of the paper considers algorithms with a single population. The investigation focuses on an implementation where the population is distributed to several processors, but the results are applicable to more common master-slave implementations, where the population is entirely stored in a master processor and multiple slaves are used to evaluate the fitness. The second part of the paper deals with parallel GAs with multiple populations. It first considers a bounding case where the connectivity, the migration rate, and the frequency of migrations are set to their maximal values. Then, arbitrary regular topologies with lower migration rates are considered and the frequency of migrations is set to its lowest value. The investigationis mainly theoretical, but experimental evidence with an additively-decomposable function is included to illustrate the accuracy of the theory. In all cases, the calculations show that the optimal number of processors that minimizes the execution time is directly proportional to the square root of the population size and the fitness evaluation time. Since these two factors usually increase as the domain becomes more difficult, the results of the paper suggest that parallel GAs can integrate large numbers of processors and significantly reduce the execution time of many practical applications. PMID:10578030
Pruning Neural Networks with Distribution Estimation Algorithms
Cantu-Paz, E
2003-01-15
This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.
An Artificial Immune Univariate Marginal Distribution Algorithm
NASA Astrophysics Data System (ADS)
Zhang, Qingbin; Kang, Shuo; Gao, Junxiang; Wu, Song; Tian, Yanping
Hybridization is an extremely effective way of improving the performance of the Univariate Marginal Distribution Algorithm (UMDA). Owing to its diversity and memory mechanisms, artificial immune algorithm has been widely used to construct hybrid algorithms with other optimization algorithms. This paper proposes a hybrid algorithm which combines the UMDA with the principle of general artificial immune algorithm. Experimental results on deceptive function of order 3 show that the proposed hybrid algorithm can get more building blocks (BBs) than the UMDA.
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.
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.
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.
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
Genetic Algorithms for Digital Quantum Simulations
NASA Astrophysics Data System (ADS)
Las Heras, U.; Alvarez-Rodriguez, U.; Solano, E.; Sanz, M.
2016-06-01
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.
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.
Facial Composite System Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zahradníková, Barbora; Duchovičová, Soňa; Schreiber, Peter
2014-12-01
The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.
Logistics distribution centers location problem and algorithm under fuzzy environment
NASA Astrophysics Data System (ADS)
Yang, Lixing; Ji, Xiaoyu; Gao, Ziyou; Li, Keping
2007-11-01
Distribution centers location problem is concerned with how to select distribution centers from the potential set so that the total relevant cost is minimized. This paper mainly investigates this problem under fuzzy environment. Consequentially, chance-constrained programming model for the problem is designed and some properties of the model are investigated. Tabu search algorithm, genetic algorithm and fuzzy simulation algorithm are integrated to seek the approximate best solution of the model. A numerical example is also given to show the application of the algorithm.
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.]. PMID:26676060
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
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.
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. PMID:26441052
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...
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.
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.
An algorithm on distributed mining association rules
NASA Astrophysics Data System (ADS)
Xu, Fan
2005-12-01
With the rapid development of the Internet/Intranet, distributed databases have become a broadly used environment in various areas. It is a critical task to mine association rules in distributed databases. The algorithms of distributed mining association rules can be divided into two classes. One is a DD algorithm, and another is a CD algorithm. A DD algorithm focuses on data partition optimization so as to enhance the efficiency. A CD algorithm, on the other hand, considers a setting where the data is arbitrarily partitioned horizontally among the parties to begin with, and focuses on parallelizing the communication. A DD algorithm is not always applicable, however, at the time the data is generated, it is often already partitioned. In many cases, it cannot be gathered and repartitioned for reasons of security and secrecy, cost transmission, or sheer efficiency. A CD algorithm may be a more appealing solution for systems which are naturally distributed over large expenses, such as stock exchange and credit card systems. An FDM algorithm provides enhancement to CD algorithm. However, CD and FDM algorithms are both based on net-structure and executing in non-shareable resources. In practical applications, however, distributed databases often are star-structured. This paper proposes an algorithm based on star-structure networks, which are more practical in application, have lower maintenance costs and which are more practical in the construction of the networks. In addition, the algorithm provides high efficiency in communication and good extension in parallel computation.
Production scheduling and rescheduling with genetic algorithms.
Bierwirth, C; Mattfeld, D C
1999-01-01
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs. PMID:10199993
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.
The genetic algorithms for trajectory optimization
NASA Astrophysics Data System (ADS)
Janin, G.; Gomez-Tierno, M. A.
1985-10-01
Possible difficulties encountered when solving space flight trajectory optimization problems are recalled. The need of a global optimization scheme is realized. Nondeterministic methods, called here stochastic methods, seem to be good candidates for solving these types of problems. A particular class of such methods, modelled upon search strategies employed in natural adaptation, is proposed here: the genetic algorithms. Two models, the mutation-selection and the crossover-selection, are discussed and remarks resulting from applications to test problems and space flight problems are made. It is concluded that a considerable effort is still needed for developing efficient schemes using genetic algorithms. However, they appear to offer an entirely original way for solving a large class of global optimization problems and they are particularly well-suited for parallel processing to be used in the fifth generation computers.
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.
PSS Parameters Tuning Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Abdulrahim, M.; Almoula, Zakaria Fadl; Al-Hafid, Hafid
2008-10-01
Optimal tuning of power system stabilizer (PSS) parameters using genetic algorithm with single objective function is presented in this paper. A Single Machine Infinite Bus (SMIB) system is considered. The main objective of this research paper is to investigate the suitability of genetic algorithm for effective tuning of parameters of the power system stabilizer in a single machine infinite bus system. A conventional speed based lead-lag PSS is used. A simple and effective method of tuning the parameters of PSS is proposed which is posed as an optimization formulation by maximizing the damping of modes of oscillations of the SMIB system over a wide range of loading conditions and different system configurations. It is found that GA based PSS with single objective design shows improved dynamic performance over Conventional PSS over a wide range of operating conditions and different system parameters.
Efficient genetic algorithms using discretization scheduling.
McLay, Laura A; Goldberg, David E
2005-01-01
In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations use numerical methods with varying discretization. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. This paper examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. There are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one- and two-dimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling. PMID:16156928
Allocating Railway Platforms Using A Genetic Algorithm
NASA Astrophysics Data System (ADS)
Clarke, M.; Hinde, C. J.; Withall, M. S.; Jackson, T. W.; Phillips, I. W.; Brown, S.; Watson, R.
This paper describes an approach to automating railway station platform allocation. The system uses a Genetic Algorithm (GA) to find how a station’s resources should be allocated. Real data is used which needs to be transformed to be suitable for the automated system. Successful or ‘fit’ allocations provide a solution that meets the needs of the station schedule including platform re-occupation and various other constraints. The system associates the train data to derive the station requirements. The Genetic Algorithm is used to derive platform allocations. Finally, the system may be extended to take into account how further parameters that are external to the station have an effect on how an allocation should be applied. The system successfully allocates around 1000 trains to platforms in around 30 seconds requiring a genome of around 1000 genes to achieve this.
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
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. PMID:19272859
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. PMID:27341220
Predicting mining activity with parallel genetic algorithms
Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.
2005-01-01
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.
Modeling a magnetostrictive transducer using genetic algorithm
NASA Astrophysics Data System (ADS)
Almeida, L. A. L.; Deep, G. S.; Lima, A. M. N.; Neff, H.
2001-05-01
This work reports on the applicability of the genetic algorithm (GA) to the problem of parameter determination of magnetostrictive transducers. A combination of the Jiles-Atherton hysteresis model with a quadratic moment rotation model is simulated using known parameters of a sensor. The simulated sensor data are then used as input data for the GA parameter calculation method. Taking the previously known parameters, the accuracy of the GA parameter calculation method can be evaluated.
A review of estimation of distribution algorithms in bioinformatics
Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, Jose Antonio; Peer, Yves Van de; Blanco, Rosa; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro
2008-01-01
Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. PMID:18822112
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 hybrid genetic algorithm for resolving closely spaced objects
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Lillo, W. E.; Schulenburg, N.
1995-01-01
A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.
Genetic 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.
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 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.
Adaptive link selection algorithms for distributed estimation
NASA Astrophysics Data System (ADS)
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
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.
Using a genetic algorithm to solve fluid-flow problems
Pryor, R.J. )
1990-06-01
Genetic algorithms are based on the mechanics of the natural selection and natural genetics processes. These algorithms are finding increasing application to a wide variety of engineering optimization and machine learning problems. In this paper, the authors demonstrate the use of a genetic algorithm to solve fluid flow problems. Specifically, the authors use the algorithm to solve the one-dimensional flow equations for a pipe.
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.
Applying various algorithms for species distribution modelling.
Li, Xinhai; Wang, Yuan
2013-06-01
Species distribution models have been used extensively in many fields, including climate change biology, landscape ecology and conservation biology. In the past 3 decades, a number of new models have been proposed, yet researchers still find it difficult to select appropriate models for data and objectives. In this review, we aim to provide insight into the prevailing species distribution models for newcomers in the field of modelling. We compared 11 popular models, including regression models (the generalized linear model, the generalized additive model, the multivariate adaptive regression splines model and hierarchical modelling), classification models (mixture discriminant analysis, the generalized boosting model, and classification and regression tree analysis) and complex models (artificial neural network, random forest, genetic algorithm for rule set production and maximum entropy approaches). Our objectives are: (i) to compare the strengths and weaknesses of the models, their characteristics and identify suitable situations for their use (in terms of data type and species-environment relationships) and (ii) to provide guidelines for model application, including 3 steps: model selection, model formulation and parameter estimation. PMID:23731809
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.
Fuzzy controller design by parallel genetic algorithms
NASA Astrophysics Data System (ADS)
Mondelli, G.; Castellano, G.; Attolico, Giovanni; Distante, Arcangelo
1998-03-01
Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.
Genetic algorithm for disassembly process planning
NASA Astrophysics Data System (ADS)
Kongar, Elif; Gupta, Surendra M.
2002-02-01
When a product reaches its end of life, there are several options available for processing it including reuse, remanufacturing, recycling, and disposing (the least desirable option). In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with the increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly process planning be developed. In this paper, we present a genetic algorithm for disassembly process planning. A case example is considered to demonstrate the functionality of the algorithm.
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.
Designing conducting polymers using genetic algorithms
NASA Astrophysics Data System (ADS)
Giro, R.; Cyrillo, M.; Galvão, D. S.
2002-11-01
We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. We present the results for a case study of polyanilines, one of the most important families of conducting polymers. The methodology proved to be able of generating automatic solutions for the problem of determining the optimum relative concentration for binary and ternary disordered polyaniline alloys exhibiting metallic properties. The methodology is completely general and can be used to design new classes of materials.
Modeling of Nonlinear Systems using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Hayashi, Kayoko; Yamamoto, Toru; Kawada, Kazuo
In this paper, a newly modeling system by using Genetic Algorithm (GA) is proposed. The GA is an evolutionary computational method that simulates the mechanisms of heredity or evolution of living things, and it is utilized in optimization and in searching for optimized solutions. Most process systems have nonlinearities, so it is necessary to anticipate exactly such systems. However, it is difficult to make a suitable model for nonlinear systems, because most nonlinear systems have a complex structure. Therefore the newly proposed method of modeling for nonlinear systems uses GA. Then, according to the newly proposed scheme, the optimal structure and parameters of the nonlinear model are automatically generated.
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.
Optimization of composite structures by estimation of distribution algorithms
NASA Astrophysics Data System (ADS)
Grosset, Laurent
The design of high performance composite laminates, such as those used in aerospace structures, leads to complex combinatorial optimization problems that cannot be addressed by conventional methods. These problems are typically solved by stochastic algorithms, such as evolutionary algorithms. This dissertation proposes a new evolutionary algorithm for composite laminate optimization, named Double-Distribution Optimization Algorithm (DDOA). DDOA belongs to the family of estimation of distributions algorithms (EDA) that build a statistical model of promising regions of the design space based on sets of good points, and use it to guide the search. A generic framework for introducing statistical variable dependencies by making use of the physics of the problem is proposed. The algorithm uses two distributions simultaneously: the marginal distributions of the design variables, complemented by the distribution of auxiliary variables. The combination of the two generates complex distributions at a low computational cost. The dissertation demonstrates the efficiency of DDOA for several laminate optimization problems where the design variables are the fiber angles and the auxiliary variables are the lamination parameters. The results show that its reliability in finding the optima is greater than that of a simple EDA and of a standard genetic algorithm, and that its advantage increases with the problem dimension. A continuous version of the algorithm is presented and applied to a constrained quadratic problem. Finally, a modification of the algorithm incorporating probabilistic and directional search mechanisms is proposed. The algorithm exhibits a faster convergence to the optimum and opens the way for a unified framework for stochastic and directional optimization.
Adaptive sensor tasking using genetic algorithms
NASA Astrophysics Data System (ADS)
Shea, Peter J.; Kirk, Joe; Welchons, Dave
2007-04-01
Today's battlefield environment contains a large number of sensors, and sensor types, onboard multiple platforms. The set of sensor types includes SAR, EO/IR, GMTI, AMTI, HSI, MSI, and video, and for each sensor type there may be multiple sensing modalities to select from. In an attempt to maximize sensor performance, today's sensors employ either static tasking approaches or require an operator to manually change sensor tasking operations. In a highly dynamic environment this leads to a situation whereby the sensors become less effective as the sensing environments deviates from the assumed conditions. Through a Phase I SBIR effort we developed a system architecture and a common tasking approach for solving the sensor tasking problem for a multiple sensor mix. As part of our sensor tasking effort we developed a genetic algorithm based task scheduling approach and demonstrated the ability to automatically task and schedule sensors in an end-to-end closed loop simulation. Our approach allows for multiple sensors as well as system and sensor constraints. This provides a solid foundation for our future efforts including incorporation of other sensor types. This paper will describe our approach for scheduling using genetic algorithms to solve the sensor tasking problem in the presence of resource constraints and required task linkage. We will conclude with a discussion of results for a sample problem and of the path forward.
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.
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
Multidisciplinary design optimization using genetic algorithms
NASA Astrophysics Data System (ADS)
Unal, Resit
1994-12-01
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
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
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.
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Inversion for seismic anisotropy using genetic algorithms
Horne, S. Univ. of Edinburgh . Dept. of Geology and Geophysics); MacBeth, C. . Dept. of Geology and Geophysics)
1994-11-01
A general inversion scheme based on a genetic algorithm is developed to invert seismic observations for anisotropic parameters. The technique is applied to the inversion of shear-wave observations from two azimuthal VSP data sets from the Conoco test site in Oklahoma. Horizontal polarizations and time-delays are inverted for hexagonal and orthorhombic symmetries. The model solutions are consistent with previous studies using trial and error matching of full waveform synthetics. The shear-wave splitting observations suggest the presence of a shear-wave line singularity and are consistent with a dipping fracture system which is known to exist at the test site. Application of the inversion scheme prior to full waveform modeling demonstrates that a considerable saving in time is possible while retaining the same degree of accuracy.
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.
Birefringent filter design by use of a modified genetic algorithm.
Wen, Mengtao; Yao, Jianping
2006-06-10
A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation angles and the element lengths are determined by the genetic algorithm to minimize the sidelobe levels of the filters. Being different from the normal genetic algorithm, the algorithm proposed reduces the problem space of the birefringent filter design to achieve faster speed and better performance. The design of 4-, 8-, and 14-section birefringent filters with an improved sidelobe suppression ratio is realized. A 4-section birefringent filter designed with the algorithm is experimentally realized. PMID:16761031
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.
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
The genetics of fat distribution.
Schleinitz, Dorit; Böttcher, Yvonne; Blüher, Matthias; Kovacs, Peter
2014-07-01
Fat stored in visceral depots makes obese individuals more prone to complications than subcutaneous fat. There is good evidence that body fat distribution (FD) is controlled by genetic factors. WHR, a surrogate measure of FD, shows significant heritability of up to ∼60%, even after adjusting for BMI. Genetic variants have been linked to various forms of altered FD such as lipodystrophies; however, the polygenic background of visceral obesity has only been sparsely investigated in the past. Recent genome-wide association studies (GWAS) for measures of FD revealed numerous loci harbouring genes potentially regulating FD. In addition, genes with fat depot-specific expression patterns (in particular subcutaneous vs visceral adipose tissue) provide plausible candidate genes involved in the regulation of FD. Many of these genes are differentially expressed in various fat compartments and correlate with obesity-related traits, thus further supporting their role as potential mediators of metabolic alterations associated with a distinct FD. Finally, developmental genes may at a very early stage determine specific FD in later life. Indeed, genes such as TBX15 not only manifest differential expression in various fat depots, but also correlate with obesity and related traits. Moreover, recent GWAS identified several polymorphisms in developmental genes (including TBX15, HOXC13, RSPO3 and CPEB4) strongly associated with FD. More accurate methods, including cardiometabolic imaging, for assessment of FD are needed to promote our understanding in this field, where the main focus is now to unravel the yet unknown biological function of these novel 'fat distribution genes'. PMID:24632736
Experience with a Genetic Algorithm Implemented on a Multiprocessor Computer
NASA Technical Reports Server (NTRS)
Plassman, Gerald E.; Sobieszczanski-Sobieski, Jaroslaw
2000-01-01
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may benefit from a multiprocessor implementation, considering, on one hand, that analyses of individual designs in a population are independent of each other so that they may be executed concurrently on separate processors, and, on the other hand, that there are some operations in a GA that cannot be so distributed. The algorithm experimented with was based on a gaussian distribution rather than bit exchange in the GA reproductive mechanism, and the test case was a hub frame structure of up to 1080 design variables. The experimentation engaging up to 128 processors confirmed expectations of radical elapsed time reductions comparing to a conventional single processor implementation. It also demonstrated that the time spent in the non-distributable parts of the algorithm and the attendant cross-processor communication may have a very detrimental effect on the efficient utilization of the multiprocessor machine and on the number of processors that can be used effectively in a concurrent manner. Three techniques were devised and tested to mitigate that effect, resulting in efficiency increasing to exceed 99 percent.
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…
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.
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…
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.
Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
A 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…
Multiobjective Genetic Algorithm applied to dengue control.
Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F
2014-12-01
Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. PMID:25230238
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.
Random Volumetric MRI Trajectories via Genetic Algorithms
Curtis, Andrew Thomas; Anand, Christopher Kumar
2008-01-01
A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils. PMID:18604305
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.
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 of an antenna array using genetic algorithms
Kiehbadroudinezhad, Shahideh; Noordin, Nor Kamariah; Sali, A.; Abidin, Zamri Zainal
2014-06-01
An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The genetic algorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to –21.75 dB.
NASA Astrophysics Data System (ADS)
Rajalakshmi, N.; Padma Subramanian, D.; Thamizhavel, K.
2015-03-01
The extent of real power loss and voltage deviation associated with overloaded feeders in radial distribution system can be reduced by reconfiguration. Reconfiguration is normally achieved by changing the open/closed state of tie/sectionalizing switches. Finding optimal switch combination is a complicated problem as there are many switching combinations possible in a distribution system. Hence optimization techniques are finding greater importance in reducing the complexity of reconfiguration problem. This paper presents the application of firefly algorithm (FA) for optimal reconfiguration of radial distribution system with distributed generators (DG). The algorithm is tested on IEEE 33 bus system installed with DGs and the results are compared with binary genetic algorithm. It is found that binary FA is more effective than binary genetic algorithm in achieving real power loss reduction and improving voltage profile and hence enhancing the performance of radial distribution system. Results are found to be optimum when DGs are added to the test system, which proved the impact of DGs on distribution system.
Genetic algorithm for bundle adjustment in aerial panoramic stitching
NASA Astrophysics Data System (ADS)
Zhang, Chunxiao; Wen, Gaojin; Wu, Chunnan; Wang, Hongmin; Shang, Zhiming; Zhang, Qian
2015-03-01
This paper presents a genetic algorithm for bundle adjustment in aerial panoramic stitching. Compared with the conventional LM (Levenberg-Marquardt) algorithm for bundle adjustment, the proposed bundle adjustment combining the genetic algorithm optimization eliminates the possibility of sticking into the local minimum, and not requires the initial estimation of desired parameters, naturally avoiding the associated steps, that includes the normalization of matches, the computation of homography transformation, the calculations of rotation transformation and the focal length. Since the proposed bundle adjustment is composed of the directional vectors of matches, taking the advantages of genetic algorithm (GA), the Jacobian matrix and the normalization of residual error are not involved in the searching process. The experiment verifies that the proposed bundle adjustment based on the genetic algorithm can yield the global solution even in the unstable aerial imaging condition.
Genetic Algorithm Based Neural Networks for Nonlinear Optimization
1994-09-28
This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less
Aerodynamic optimum design of transonic turbine cascades using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Li, Jun; Feng, Zhenping; Chang, Jianzhong; Shen, Zuda
1997-06-01
This paper presents an aerodynamic optimum design method for transonic turbine cascades based on the Genetic Algorithms coupled to the inviscid flow Euler solver and the boundary-layer calculation. The Genetic Algorithms control the evolution of a population of cascades towards an optimum design. The fitness value of each string is evaluated using the flow solver. The design procedure has been developed and the behavior of the genetic algorithms has been tested. The objective functions of the design examples are the minimum mean-square deviation between the aimed pressure and computed pressure and the minimum amount of user expertise.
Road Detection by Neural and Genetic Algorithm in Urban Environment
NASA Astrophysics Data System (ADS)
Barsi, A.
2012-07-01
In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.
LYDIAN: An Extensible Educational Animation Environment for Distributed Algorithms
ERIC Educational Resources Information Center
Koldehofe, Boris; Papatriantafilou, Marina; Tsigas, Philippas
2006-01-01
LYDIAN is an environment to support the teaching and learning of distributed algorithms. It provides a collection of distributed algorithms as well as continuous animations. Users can combine algorithms and animations with arbitrary network structures defining the interconnection and behavior of the distributed algorithm. Further, it facilitates…
Mixture distributions in human genetics research.
Schork, N J; Allison, D B; Thiel, B
1996-06-01
The use of mixture distributions in genetics research dates back to at least the late 1800s when Karl Pearson applied them in an analysis of crab morphometry. Pearson's use of normal mixture distributions to model the mixing of different species of crab (or 'families' of crab as he referred to them) within a defined geographic area motivated further use of mixture distributions in genetics research settings, and ultimately led to their development and recognition as intuitive modelling devices for the effects of underlying genes on quantitative phenotypic (i.e. trait) expression. In addition, mixture distributions are now used routinely to model or accommodate the genetic heterogeneity thought to underlie many human diseases. Specific applications of mixture distribution models in contemporary human genetics research are, in fact, too numerous to count. Despite this long, consistent and arguably illustrious history of use, little mention of mixture distributions in genetics research is made in many recent reviews on mixture models. This review attempts to rectify this by providing insight into the role that mixture distributions play in contemporary human genetics research. Tables providing examples from the literature that describe applications of mixture models in human genetics research are offered as a way of acquainting the interested reader with relevant studies. In addition, some of the more problematic aspects of the use of mixture models in genetics research are outlined and addressed. PMID:8817796
Investigation of image feature extraction by a genetic algorithm
NASA Astrophysics Data System (ADS)
Brumby, Steven P.; Theiler, James P.; Perkins, Simon J.; Harvey, Neal R.; Szymanski, John J.; Bloch, Jeffrey J.; Mitchell, Melanie
1999-11-01
We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.
An algorithm for genetic testing of frontotemporal lobar degeneration
Rademakers, R.; Huey, E.D.; Boxer, A.L.; Mayeux, R.; Miller, B.L.; Boeve, B.F.
2011-01-01
Objective: To derive an algorithm for genetic testing of patients with frontotemporal lobar degeneration (FTLD). Methods: A literature search was performed to review the clinical and pathologic phenotypes and family history associated with each FTLD gene. Results: Based on the literature review, an algorithm was developed to allow clinicians to use the clinical and neuroimaging phenotypes of the patient and the family history and autopsy information to decide whether or not genetic testing is warranted, and if so, the order for appropriate tests. Conclusions: Recent findings in genetics, pathology, and imaging allow clinicians to use the clinical presentation of the patient with FTLD to inform genetic testing decisions. PMID:21282594
Optimization of phononic filters via genetic algorithms
NASA Astrophysics Data System (ADS)
Hussein, M. I.; El-Beltagy, M. A.
2007-12-01
A phononic crystal is commonly characterized by its dispersive frequency spectrum. With appropriate spatial distribution of the constituent material phases, spectral stop bands could be generated. Moreover, it is possible to control the number, the width, and the location of these bands within a frequency range of interest. This study aims at exploring the relationship between unit cell configuration and frequency spectrum characteristics. Focusing on 1D layered phononic crystals, and longitudinal wave propagation in the direction normal to the layering, the unit cell features of interest are the number of layers and the material phase and relative thickness of each layer. An evolutionary search for binary- and ternary-phase cell designs exhibiting a series of stop bands at predetermined frequencies is conducted. A specially formulated representation and set of genetic operators that break the symmetries in the problem are developed for this purpose. An array of optimal designs for a range of ratios in Young's modulus and density are obtained and the corresponding objective values (the degrees to which the resulting bands match the predetermined targets) are examined as a function of these ratios. It is shown that a rather complex filtering objective could be met with a high degree of success. Structures composed of the designed phononic crystals are excellent candidates for use in a wide range of applications including sound and vibration filtering.
Improved genetic algorithm for fast path planning of USV
NASA Astrophysics Data System (ADS)
Cao, Lu
2015-12-01
Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for USV(Unmanned Surface Vehicle), an approach of fast path planning based on voronoi diagram and improved Genetic Algorithm is proposed, which makes use of the principle of hierarchical path planning. First the voronoi diagram is utilized to generate the initial paths and then the optimal path is searched by using the improved Genetic Algorithm, which use multiprocessors parallel computing techniques to improve the traditional genetic algorithm. Simulation results verify that the optimal time is greatly reduced and path planning based on voronoi diagram and the improved Genetic Algorithm is more favorable in the real-time operation.
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.
Genetic-algorithm-based tri-state neural networks
NASA Astrophysics Data System (ADS)
Uang, Chii-Maw; Chen, Wen-Gong; Horng, Ji-Bin
2002-09-01
A new method, using genetic algorithms, for constructing a tri-state neural network is presented. The global searching features of the genetic algorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional (binary) genetic algorithms, a two-level chromosome structure is proposed for training the tri-state neural network. A Matlab program is developed for simulating the network performances. The results show that the proposed genetic algorithms method not only has the features of accurate of constructing the interconnection weight matrix, but also has better network performance.
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.
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)
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.
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.
Application of a genetic algorithm to wind turbine design
Selig, M.S.; Coverstone-Carroll, V.L.
1995-09-01
This paper presents an optimization procedure for stall-regulated horizontal-axis wind-turbines. A hybrid approach is used that combines the advantages of a genetic algorithm and an inverse design method. This method is used to determine the optimum blade pitch and blade chord and twist distributions that maximize the annual energy production. To illustrate the method, a family of 25 wind turbines was designed to examine the sensitivity of annual energy production to changes in the rotor blade length and peak rotor power. Trends are revealed that should aid in the design of new rotors for existing turbines. In the second application, a series of five wind turbines was designed to determine the benefits of specifically tailoring wind turbine blades for the average wind speed at a particular site. The results have important practical implications related to rotors designed for the Midwest versus those where the average wind speed may be greater.
a Distributed Polygon Retrieval Algorithm Using Mapreduce
NASA Astrophysics Data System (ADS)
Guo, Q.; Palanisamy, B.; Karimi, H. A.
2015-07-01
The burst of large-scale spatial terrain data due to the proliferation of data acquisition devices like 3D laser scanners poses challenges to spatial data analysis and computation. Among many spatial analyses and computations, polygon retrieval is a fundamental operation which is often performed under real-time constraints. However, existing sequential algorithms fail to meet this demand for larger sizes of terrain data. Motivated by the MapReduce programming model, a well-adopted large-scale parallel data processing technique, we present a MapReduce-based polygon retrieval algorithm designed with the objective of reducing the IO and CPU loads of spatial data processing. By indexing the data based on a quad-tree approach, a significant amount of unneeded data is filtered in the filtering stage and it reduces the IO overhead. The indexed data also facilitates querying the relationship between the terrain data and query area in shorter time. The results of the experiments performed in our Hadoop cluster demonstrate that our algorithm performs significantly better than the existing distributed algorithms.
Genetic optimization of the HSTAMIDS landmine detection algorithm
NASA Astrophysics Data System (ADS)
Konduri, Ravi K.; Solomon, Geoff Z.; DeJong, Keith; Duvoisin, Herbert A.; Bartosz, Elizabeth E.
2004-09-01
CyTerra's dual sensor HSTAMIDS system has demonstrated exceptional landmine detection capabilities in extensive government-run field tests. Further optimization of the highly successful PentAD-class algorithms for Humanitarian Demining (HD) use (to enhance detection (Pd) and to lower the false alarm rate (FAR)) may be possible. PentAD contains several input parameters, making such optimization computationally intensive. Genetic algorithm techniques, which formerly provided substantial improvement in the detection performance of the metal detector sensor algorithm alone, have been applied to optimize the numerical values of the dual-sensor algorithm parameters. Genetic algorithm techniques have also been applied to choose among several sub-models and fusion techniques to potentially train the HSTAMIDS HD system in new ways. In this presentation we discuss the performance of the resulting algorithm as applied to field data.
Application of genetic algorithms to autopiloting in aerial combat simulation
NASA Astrophysics Data System (ADS)
Kim, Dai Hyun; Erwin, Daniel A.; Kostrzewski, Andrew A.; Kim, Jeongdal; Savant, Gajendra D.
1998-10-01
An autopilot algorithm that controls a fighter aircraft in simulated aerial combat is presented. A fitness function, whose arguments are the control settings of the simulated fighter, is continuously maximized by a fuzzied genetic algorithm. Results are presented for one-to-one combat simulated on a personal computer. Generalization to many-to-many combat is discussed.
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…
Optimization of genomic selection training populations with a genetic algorithm
Technology Transfer Automated Retrieval System (TEKTRAN)
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…
Research on Laser Marking Speed Optimization by Using Genetic Algorithm
Wang, Dongyun; Yu, Qiwei; Zhang, Yu
2015-01-01
Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831
Genetic algorithm and the application for job shop group scheduling
NASA Astrophysics Data System (ADS)
Mao, Jianzhong; Wu, Zhiming
1995-08-01
Genetic algorithm (GA) is a heuristic and random search technique mimicking nature. This paper first presents the basic principle of GA, the definition and the function of the genetic operators, and the principal character of GA. On the basis of these, the paper proposes using GA as a new solution method of the job-shop group scheduling problem, discusses the coded representation method of the feasible solution, and the particular limitation to the genetic operators.
NASA Astrophysics Data System (ADS)
Alpman, Emre
2014-06-01
The effect of selecting the twist angle and chord length distributions on the wind turbine blade design was investigated by performing aerodynamic optimization of a two-bladed stall regulated horizontal axis wind turbine. Twist angle and chord length distributions were defined using Bezier curve using 3, 5, 7 and 9 control points uniformly distributed along the span. Optimizations performed using a micro-genetic algorithm with populations composed of 5, 10, 15, 20 individuals showed that, the number of control points clearly affected the outcome of the process; however the effects were different for different population sizes. The results also showed the superiority of micro-genetic algorithm over a standard genetic algorithm, for the selected population sizes. Optimizations were also performed using a macroevolutionary algorithm and the resulting best blade design was compared with that yielded by micro-genetic algorithm.
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.
Cloud identification using genetic algorithms and massively parallel computation
NASA Technical Reports Server (NTRS)
Buckles, Bill P.; Petry, Frederick E.
1996-01-01
As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
Forecasting the solar cycle with genetic algorithms
NASA Astrophysics Data System (ADS)
Orfila, A.; Ballester, J. L.; Oliver, R.; Alvarez, A.; Tintoré, J.
2002-04-01
In the past, it has been postulated that the irregular dynamics of the solar cycle may embed a low order chaotic process (Weiss 1988, 1994; Spiegel 1994) which, if true, implies that the future behaviour of solar activity should be predictable. Here, starting from the historical record of Zürich sunspot numbers, we build a dynamical model of the solar cycle which allows us to make a long-term forecast of its behaviour. Firstly, the deterministic part of the time series has been reconstructed using the Singular Spectrum Analysis and then an evolutionary algorithm (Alvarez et al. 2001), based on Darwinian theories of natural selection and survival and ideally suited for non-linear time series, has been applied. Then, the predictive capability of the algorithm has been tested by comparing the behaviour of solar cycles 19-22 with forecasts made with the algorithm, obtaining results which show reasonable agreement with the known behaviour of those cycles. Next, the forecast of the future behaviour of solar cycle 23 has been performed and the results point out that the level of activity during this cycle will be somewhat smaller than in the two previous ones.
Community detection based on modularity and an improved genetic algorithm
NASA Astrophysics Data System (ADS)
Shang, Ronghua; Bai, Jing; Jiao, Licheng; Jin, Chao
2013-03-01
Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.
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.
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.
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.
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.
Genetic algorithm dose minimization for an operational layout.
McLawhorn, S. L.; Kornreich, D. E.; Dudziak, Donald J.
2002-01-01
In an effort to reduce the dose to operating technicians performing fixed-time procedures on encapsulated source material, a program has been developed to optimize the layout of workstations within a facility by use of a genetic algorithm. Taking into account the sources present at each station and the time required to complete each procedure, the program utilizes a point kernel dose calculation tool for dose estimates. The genetic algorithm driver employs the dose calculation code as a cost function to determine the optimal spatial arrangement of workstations to minimize the total worker dose.
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.
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.
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.
Stochastic search in structural optimization - Genetic algorithms and simulated annealing
NASA Technical Reports Server (NTRS)
Hajela, Prabhat
1993-01-01
An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.
RNA-RNA interaction prediction using genetic algorithm
2014-01-01
Background 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. Results 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. Conclusions 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. PMID:25114714
Structural pattern recognition using genetic algorithms with specialized operators.
Khoo, K G; Suganthan, P N
2003-01-01
This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices. The fitness of each solution string is computed by accumulating the similarity between the unary and binary attributes of the matched vertex pairs. We propose novel crossover and mutation operators, specifically for this problem. With these specialized genetic operators, the proposed algorithm converges to better quality solutions at a faster rate than the standard genetic algorithm (SGA). In addition, the proposed algorithm is also capable of recognizing multiple instances of any model object. An efficient pose-clustering algorithm is used to eliminate occasional wrong mappings and to determine the presence/pose of the model in the scene. We demonstrate the superior performance of our proposed algorithm using extensive experimental results. PMID:18238167
Genetic algorithms, path relinking, and the flowshop sequencing problem.
Reeves, C R; Yamada, T
1998-01-01
In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly. PMID:10021740
Genetic algorithm for chromaticity correction in diffraction limited storage rings
NASA Astrophysics Data System (ADS)
Ehrlichman, M. P.
2016-04-01
A multiobjective genetic algorithm is developed for optimizing nonlinearities in diffraction limited storage rings. This algorithm determines sextupole and octupole strengths for chromaticity correction that deliver optimized dynamic aperture and beam lifetime. The algorithm makes use of dominance constraints to breed desirable properties into the early generations. The momentum aperture is optimized indirectly by constraining the chromatic tune footprint and optimizing the off-energy dynamic aperture. The result is an effective and computationally efficient technique for correcting chromaticity in a storage ring while maintaining optimal dynamic aperture and beam lifetime.
NASA Astrophysics Data System (ADS)
Mao, Jiandong; Li, Jinxuan
2015-10-01
Particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.
Genetic-Annealing Algorithm in Grid Environment for Scheduling Problems
NASA Astrophysics Data System (ADS)
Cruz-Chávez, Marco Antonio; Rodríguez-León, Abelardo; Ávila-Melgar, Erika Yesenia; Juárez-Pérez, Fredy; Cruz-Rosales, Martín H.; Rivera-López, Rafael
This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using simulated annealing within a Genetic Algorithm to solve the job shop scheduling problem. Experimental results of the algorithm obtained in the "Tarantula MiniGrid" are shown. Tarantula was implemented by linking two clusters from different geographic locations in Mexico (Morelos-Veracruz). The technique used to link the two clusters and configure the Tarantula MiniGrid is described. The effects of latency in communication between the two clusters are discussed. It is shown that the evolutionary algorithm presented is more efficient working in Grid environments because it can carry out major exploration and exploitation of the solution space.
The multi-niche crowding genetic algorithm: Analysis and applications
Cedeno, W.
1995-09-01
The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.
Genetic algorithms: What computers can learn from Darwin
Walbridge, C.T. )
1989-01-01
In this article the author posits a field of computing based on the genetic algorithm. This approach to programming mimics evolution by utilizing a computer to solve problems on a trial and error basis and ascertain the best answer through natural selection of the best of the computer's guesses. The author discusses the viability of this system in comparison to that of artificial intelligence.
Hybrid methods using genetic algorithms for global optimization.
Renders, J M; Flasse, S P
1996-01-01
This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and "hill-climbing" methods in order to find a better compromise to the trade-off. Inspired by biology and especially by the manner in which living beings adapt themselves to their environment, these hybrid methods involve two interwoven levels of optimization, namely evolution (genetic algorithms) and individual learning (Quasi-Newton), which cooperate in a global process of optimization. One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter. PMID:18263027
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 ...
Optimization of reliability allocation strategies through use of genetic algorithms
Campbell, J.E.; Painton, L.A.
1996-08-01
This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.
Crossover Improvement for the Genetic Algorithm in Information Retrieval.
ERIC Educational Resources Information Center
Vrajitoru, Dana
1998-01-01
In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
A 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.
Concurrent genetic algorithms for optimization of large structures
Adeli, H.; Cheng, N. )
1994-07-01
In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high-rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high-performance computers such as the Cray Y-MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high-rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35-story tower (with 1,262 elements and 936 degrees of freedom), using eight processors. 9 refs.
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)
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.
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.
Robust Sparse Matching and Motion Estimation Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Shahbazi, M.; Sohn, G.; Théau, J.; Ménard, P.
2015-03-01
In this paper, we propose a robust technique using genetic algorithm for detecting inliers and estimating accurate motion parameters from putative correspondences containing any percentage of outliers. The proposed technique aims to increase computational efficiency and modelling accuracy in comparison with the state-of-the-art via the following contributions: i) guided generation of initial populations for both avoiding degenerate solutions and increasing the rate of useful hypotheses, ii) replacing random search with evolutionary search, iii) possibility of evaluating the individuals of every population by parallel computation, iv) being performable on images with unknown internal orientation parameters, iv) estimating the motion model via detecting a minimum, however more than enough, set of inliers, v) ensuring the robustness of the motion model against outliers, degeneracy and poorperspective camera models, vi) making no assumptions about the probability distribution of inliers and/or outliers residuals from the estimated motion model, vii) detecting all the inliers by setting the threshold on their residuals adaptively with regard to the uncertainty of the estimated motion model and the position of the matches. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC, MSAC, MLESAC, Least Trimmed Squares and Least Median of Squares. Experimental results proved that the proposed approach perform better than others in terms of accuracy of motion estimation, accuracy of inlier detection and the computational efficiency.
Inner Random Restart Genetic Algorithm for Practical Delivery Schedule Optimization
NASA Astrophysics Data System (ADS)
Sakurai, Yoshitaka; Takada, Kouhei; Onoyama, Takashi; Tsukamoto, Natsuki; Tsuruta, Setsuo
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires solving several tens to hundreds but less than 2 thousands cities Traveling Salesman Problems (TSP) within interactive response time (less than about 3 second), with expert-level accuracy (less than about 3% of error rate). Further, to make things more difficult, the optimization is subjects to special requirements or preferences of each various delivery sites, persons, or societies. To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) is proposed and developed. This method combines meta-heuristics such as random restart and GA having different types of simple heuristics. Such simple heuristics are 2-opt and NI (Nearest Insertion) methods, each applied for gene operations. The proposed method is hierarchical structured, integrating meta-heuristics and heuristics both of which are multiple but simple. This method is elaborated so that field experts as well as field engineers can easily understand to make the solution or method easily customized and extended according to customers' needs or taste. Comparison based on the experimental results and consideration proved that the method meets the above requirements more than other methods judging from not only optimality but also simplicity, flexibility, and expandability in order for this method to be practically used.
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-05-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al{sub n} (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of {radical}3 x {radical}3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
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.
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
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
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. PMID:19639011
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
Rogers, Adam; Fiege, Jason D.
2011-02-01
Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image {chi}{sup 2} and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest {chi}{sup 2} is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.
Packing Boxes into Multiple Containers Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Menghani, Deepak; Guha, Anirban
2016-07-01
Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.
Selection Intensity in Genetic Algorithms with Generation Gaps
Cantu-Paz, E.
2000-01-19
This paper presents calculations of the selection intensity of common selection and replacement methods used in genetic algorithms (GAs) with generation gaps. The selection intensity measures the increase of the average fitness of the population after selection, and it can be used to predict the average fitness of the population at each iteration as well as the number of steps until the population converges to a unique solution. In addition, the theory explains the fast convergence of some algorithms with small generation gaps. The accuracy of the calculations was verified experimentally with a simple test function. The results of this study facilitate comparisons between different algorithms, and provide a tool to adjust the selection pressure, which is indispensable to obtain robust algorithms.
The ordered clustered travelling salesman problem: a hybrid genetic algorithm.
Ahmed, Zakir Hussain
2014-01-01
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148
A novel pipeline based FPGA implementation of a genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2014-05-01
To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.
Strain gage selection in loads equations using a genetic algorithm
NASA Technical Reports Server (NTRS)
1994-01-01
Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.
Using genetic algorithms to construct a network for financial prediction
NASA Astrophysics Data System (ADS)
Patel, Devesh
1996-03-01
Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.
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. *
Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm
NASA Astrophysics Data System (ADS)
Şencan Şahin, Arzu
2012-11-01
Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.
NASA Astrophysics Data System (ADS)
Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.
2013-12-01
A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.
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
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
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
Detection of parametric curves based on genetic algorithm
NASA Astrophysics Data System (ADS)
Li, Haimin; Wu, Chengke
1998-09-01
Detection of curves with special shapes has been put on great interest in the fields of image processing and recognition. Some commonly used algorithms such as Hough Transform and Generalized Radon Transform are global search methods. When the number of parameters increases, their efficiencies decrease rapidly because of the expansion of parameter space. To solve this problem, a new method based on Genetic Algorithm is presented which combines a local search procedure to improve its performance. Experimental results show that the proposed method improves search efficiency greatly.
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. PMID:26611346
a Genetic Algorithm for Urban Transit Routing Problem
NASA Astrophysics Data System (ADS)
Chew, Joanne Suk Chun; Lee, Lai Soon
The Urban Transit Routing Problem (UTRP) involves solving a set of transit route networks, which proved to be a highly complex multi-constrained problem. In this study, a bus route network to find an efficient network to meet customer demands given information on link travel times is considered. An evolutionary optimization technique, called Genetic Algorithm is proposed to solve the UTRP. The main objective is to minimize the passenger costs where the quality of the route sets is evaluated by a set of parameters. Initial computational experiments show that the proposed algorithm performs better than the benchmark results for Mandl's problems.
Optimization of multilayer cylindrical cloaks using genetic algorithms and NEWUOA
NASA Astrophysics Data System (ADS)
Sakr, Ahmed A.; Abdelmageed, Alaa K.
2016-06-01
The problem of minimizing the scattering from a multilayer cylindrical cloak is studied. Both TM and TE polarizations are considered. A two-stage optimization procedure using genetic algorithms and NEWUOA (new unconstrained optimization algorithm) is adopted for realizing the cloak using homogeneous isotropic layers. The layers are arranged such that they follow a repeated pattern of alternating DPS and DNG materials. The results show that a good level of invisibility can be realized using a reasonable number of layers. Maintaining the cloak performance over a finite range of frequencies without sacrificing the level of invisibility is achieved.
Genetic Algorithm for Initial Orbit Determination with Too Short Arc
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-01-01
The sky surveys of space objects have obtained a huge quantity of too-short-arc (TSA) observation data. However, the classical method of initial orbit determination (IOD) can hardly get reasonable results for the TSAs. 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 selection of optimizing variables as well as the corresponding genetic operator for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.
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.
Efficient algorithms for distributed simulation and related problems
Kumar, D.
1987-01-01
This thesis presents efficient algorithms for distributed simulation, and for the related problems of termination detection and sequential simulation. Distributed simulation algorithms applicable to the simulation of special classes of systems, such that almost no overhead messages are required are presented. By contrast, previous distributed simulation algorithms, although applicable to the general class of any discrete-event system, usually require too many overhead messages. First, a simple distributed simulation algorithm is defined with nearly zero overhead messages for simulating feedforward systems. An approximate method is developed to predict its performance in simulating a class of feedforward-queuing networks. Performance of the scheme is evaluated in simulating specific subclasses of these queuing networks. It is shown that the scheme offers a high performance for serial-parallel networks. Next, another distributed simulation scheme is defined for a class of distributed systems whose topologies may have cycles. One important problem in devising distributed simulation algorithms is that of efficient detection of termination. With this in mind, a class of termination-detection algorithms using markers is devised. Finally, a new sequential simulation algorithm is developed, based on a distributed one. This algorithm often reduces the event-list manipulations of traditional-event list-driven simulation.
A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2013-05-01
With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.
A genetic algorithm solution to the unit commitment problem
Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V.
1996-02-01
This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 units and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.
Genetic algorithm application in optimization of wireless sensor networks.
Norouzi, Ali; Zaim, A Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235
Adaptive process control 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.
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.
Designing a competent simple genetic algorithm for search and optimization
NASA Astrophysics Data System (ADS)
Reed, Patrick; Minsker, Barbara; Goldberg, David E.
2000-12-01
Simple genetic algorithms have been used to solve many water resources problems, but specifying the parameters that control how adaptive search is performed can be a difficult and time-consuming trial-and-error process. However, theoretical relationships for population sizing and timescale analysis have been developed that can provide pragmatic tools for vastly limiting the number of parameter combinations that must be considered. The purpose of this technical note is to summarize these relationships for the water resources community and to illustrate their practical utility in a long-term groundwater monitoring design application. These relationships, which model the effects of the primary operators of a simple genetic algorithm (selection, recombination, and mutation), provide a highly efficient method for ensuring convergence to near-optimal or optimal solutions. Application of the method to a monitoring design test case identified robust parameter values using only three trial runs.
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
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. PMID:12398277
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.
GAz: a genetic algorithm for photometric redshift estimation
NASA Astrophysics Data System (ADS)
Hogan, Robert; Fairbairn, Malcolm; Seeburn, Navin
2015-05-01
We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The genetic algorithm explores the very large space of high order polynomials while only requiring optimization of a small number of terms. We find a σrms = 0.0408 ± 0.0006 for redshifts in the range 0.4 < z < 0.7. These results are competitive with the current state-of-the-art but can be presented simply as a polynomial which does not require the user to run any code. We demonstrate that the method generalizes well to other data sets and redshift ranges by testing it on SDSS DR11 and on simulated data. For other data sets or applications the code has been made available at https://github.com/rbrthogan/GAz.
Calibration of FRESIM for Singapore expressway using genetic algorithm
Cheu, R.L.; Jin, X.; Srinivasa, D.; Ng, K.C.; Ng, Y.L.
1998-11-01
FRESIM is a microscopic time-stepping simulation model for freeway corridor traffic operations. To enable FRESIM to realistically simulate expressway traffic flow in Singapore, parameters that govern the movement of vehicles needed to be recalibrated for local traffic conditions. This paper presents the application of a genetic algorithm as an optimization method for finding a suitable combination of FRESIM parameter values. The calibration is based on field data collected on weekdays over a 5.8 km segment of the Ayer Rajar Expressway. Independent calibrations have been made for evening peak and midday off-peak traffic. The results show that the genetic algorithm is able to search for two sets of parameter values that enable FRESIM to produce 30-s loop-detector volume and speed (averaged across all lanes) closely matching the field data under two different traffic conditions. The two sets of parameter values are found to produce a consistently good match for data collected in different days.
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. PMID:8924643
Solving a multistage partial inspection problem using genetic algorithms
Heredia-Langner, Alejandro ); Montgomery, D C.; Carlyle, W M.
2002-01-01
Traditionally, the multistage inspection problem has been formulated as consisting of a decision schedule where some manufacturing stages receive full inspection and the rest none. Dynamic programming and heuristic methods (like local search) are the most commonly used solution techniques. A highly constrained multistage inspection problem is presented where all stages must receive partial rectifying inspection and it is solved using a real-valued genetic algorithm. This solution technique can handle multiple objectives and quality constraints effectively.
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.
Optimal design of plasmonic waveguide using multiobjective genetic algorithm
NASA Astrophysics Data System (ADS)
Jung, Jaehoon
2016-01-01
An approach for multiobjective optimal design of a plasmonic waveguide is presented. We use a multiobjective extension of a genetic algorithm to find the Pareto-optimal geometries. The design variables are the geometrical parameters of the waveguide. The objective functions are chosen as the figure of merit defined as the ratio between the propagation distance and effective mode size and the normalized coupling length between adjacent waveguides at the telecom wavelength of 1550 nm.
A genetic algorithm for ground-based telescope observation scheduling
NASA Astrophysics Data System (ADS)
Mahoney, William; Veillet, Christian; Thanjavur, Karun
2012-09-01
A prototype genetic algorithm (GA) is being developed to provide assisted and ultimately automated observation scheduling functionality. Harnessing the logic developed for manual queue preparation, the GA can build suitable sets of queues for the potential combinations of environmental and atmospheric conditions. Evolving one step further, the GA can select the most suitable observation for any moment in time, based on allocated priorities, agency balances, and realtime availability of the skies' condition.
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.
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; Reyna, Alberto
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339
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
Edge detection in medical images using a genetic algorithm.
Gudmundsson, M; El-Kwae, E A; Kabuka, M R
1998-06-01
An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then optimized in parallel using incorporated genetic operators that perform transforms on edge structures. Adaptation is used to control operator probabilities based on their participation. The GA was compared to the simulated annealing (SA) approach using ideal and actual medical images from different modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. Quantitative comparisons were provided based on the Pratt figure of merit and on the cost-function minimization. The detected edges were thin, continuous, and well localized. Most of the basic edge features were detected. Results for different medical image modalities are promising and encourage further investigation to improve the accuracy and experiment with different cost functions and genetic operators. PMID:9735910
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.
The use of genetic algorithms to model protoplanetary discs
NASA Astrophysics Data System (ADS)
Hetem, Annibal; Gregorio-Hetem, Jane
2007-12-01
The protoplanetary discs of T Tauri and Herbig Ae/Be stars have previously been studied using geometric disc models to fit their spectral energy distribution (SED). The simulations provide a means to reproduce the signatures of various circumstellar structures, which are related to different levels of infrared excess. With the aim of improving our previous model, which assumed a simple flat-disc configuration, we adopt here a reprocessing flared-disc model that assumes hydrostatic, radiative equilibrium. We have developed a method to optimize the parameter estimation based on genetic algorithms (GAs). This paper describes the implementation of the new code, which has been applied to Herbig stars from the Pico dos Dias Survey catalogue, in order to illustrate the quality of the fitting for a variety of SED shapes. The star AB Aur was used as a test of the GA parameter estimation, and demonstrates that the new code reproduces successfully a canonical example of the flared-disc model. The GA method gives a good quality of fit, but the range of input parameters must be chosen with caution, as unrealistic disc parameters can be derived. It is confirmed that the flared-disc model fits the flattened SEDs typical of Herbig stars; however, embedded objects (increasing SED slope) and debris discs (steeply decreasing SED slope) are not well fitted with this configuration. Even considering the limitation of the derived parameters, the automatic process of SED fitting provides an interesting tool for the statistical analysis of the circumstellar luminosity of large samples of young stars.
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.
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.
In-Space Radiator Shape Optimization using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Hull, Patrick V.; Kittredge, Ken; Tinker, Michael; SanSoucie, Michael
2006-01-01
Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in
An efficient algorithm for estimating noise covariances in distributed systems
NASA Technical Reports Server (NTRS)
Dee, D. P.; Cohn, S. E.; Ghil, M.; Dalcher, A.
1985-01-01
An efficient computational algorithm for estimating the noise covariance matrices of large linear discrete stochatic-dynamic systems is presented. Such systems arise typically by discretizing distributed-parameter systems, and their size renders computational efficiency a major consideration. The proposed adaptive filtering algorithm is based on the ideas of Belanger, and is algebraically equivalent to his algorithm. The earlier algorithm, however, has computational complexity proportional to p to the 6th, where p is the number of observations of the system state, while the new algorithm has complexity proportional to only p-cubed. Further, the formulation of noise covariance estimation as a secondary filter, analogous to state estimation as a primary filter, suggests several generalizations of the earlier algorithm. The performance of the proposed algorithm is demonstrated for a distributed system arising in numerical weather prediction.
NASA Astrophysics Data System (ADS)
Jiang, Tianzi; Cui, Qinghua; Shi, Guihua; Ma, Songde
2003-08-01
In this paper, a novel hybrid algorithm combining genetic algorithms and tabu search is presented. In the proposed hybrid algorithm, the idea of tabu search is applied to the crossover operator. We demonstrate that the hybrid algorithm can be applied successfully to the protein folding problem based on a hydrophobic-hydrophilic lattice model. The results show that in all cases the hybrid algorithm works better than a genetic algorithm alone. A comparison with other methods is also made.
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
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. PMID:26553402
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.
Modeling and convergence analysis of distributed coevolutionary algorithms.
Subbu, Raj; Sanderson, Arthur C
2004-04-01
A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyzes are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior. PMID:15376831
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.
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.
Evolving retrieval algorithms with a genetic programming scheme
NASA Astrophysics Data System (ADS)
Theiler, James P.; Harvey, Neal R.; Brumby, Steven P.; Szymanski, John J.; Alferink, Steve; Perkins, Simon J.; Porter, Reid B.; Bloch, Jeffrey J.
1999-10-01
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 reflected 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.
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. PMID:26241984
Parallel matrix transpose algorithms on distributed memory concurrent computers
Choi, Jaeyoung; Dongarra, J. |; Walker, D.W.
1994-12-31
This paper describes parallel matrix transpose algorithms on distributed memory concurrent processors. We assume that the matrix is distributed over a P {times} Q processor template with a block scattered data distribution. P, Q, and the block size can be arbitrary, so the algorithms have wide applicability. The algorithms make use of non-blocking, point-to-point communication between processors. The use of nonblocking communication allows a processor to overlap the messages that it sends to different processors, thereby avoiding unnecessary synchronization. Combined with the matrix multiplication routine, C = A {center_dot} B, the algorithms are used to compute parallel multiplications of transposed matrices, C = A{sup T} {center_dot} B{sup T}, in the PUMMA package. Details of the parallel implementation of the algorithms are given, and results are presented for runs on the Intel Touchstone Delta computer.
Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2011-12-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2012-01-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
NASA Astrophysics Data System (ADS)
Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.
Ebtehaj, Isa; Bonakdari, Hossein
2014-01-01
The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations. PMID:25429460
Fringe Pattern Demodulation by Independent Windows Fitting Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Toledo, L. E.; Cuevas, F. J.
2008-04-01
It is presented a new method to retrieve the phase map from a fringe pattern with closed and sub-sampled fringes. The Fringe Processing on Independent Windows method (FPIW) find a parametric function that estimate the phase of a given segmented region that comes from the fringe pattern. FPIW method is a modification of the Window Fringe Pattern Demodulation technique (WFPD), that uses a genetic algorithm to find the parametric function. A population of randomly generated chromosomes, that codifies different parametric functions, is used by the genetic algorithm to simulate natural selection. A fitness value is associated to all chromosomes by a function that uses two criterion in FPIW method: fringe similarity between the segmented interferogram and the fringe pattern generated by the cosine of the phase given by the parametric function, and the smoothness of these function. The best chromosome produced by the evolution is decoded to obtain the parametric function that estimates the phase in a given region. The genetic algorithm is applied on a set of partially overlapped windows extracted from the original fringe pattern. The independent phases obtained by the GA's, are used to reconstruct the whole phase field. A given window is chosen to be the reference. Phase in adjacent windows is spliced with the phase in the reference window to form a phase map of the joined regions. The RMS value between reference phase and adjacent phase is minimized in the overlapped area to find the DC bias and the correct concavity of the adjacent phase, so continuity between reference and adjacent spliced phase is assured. The new phase map is used as the new reference. This process is repeated until the whole phase map is reconstructed.
Self-adaptive genetic algorithms with simulated binary crossover.
Deb, K; Beyer, H G
2001-01-01
Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs. PMID:11382356
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 Algorithms: A New Method for Neutron Beam Spectral Characterization
David W. Freeman
2000-06-04
A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron beams associated with boron neutron capture therapy (BNCT). The GA unfolding technique has been successfully applied to problems with moderate energy resolution (up to 47 energy groups). Initial research indicates that the GA unfolding technique may well be superior to popular unfolding methods in common use. Research now under way at Kansas State University is focused on optimizing the unfolding algorithm and expanding its energy resolution to unfold detailed beam spectra based on multiple foil measurements. Indications are that the final code will significantly outperform current, state-of-the-art codes in use by the scientific community.
A genetic algorithm 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.
Genetic algorithm for multiple bus line coordination on urban arterial.
Yang, Zhen; Wang, Wei; Chen, Shuyan; Ding, Haoyang; Li, Xiaowei
2015-01-01
Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority. PMID:25663837
An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems
Salcedo-Sanz, S.; Del Ser, J.; Geem, Z. W.
2014-01-01
This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases. PMID:24977235
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.
Autosegmentation of ultrasonic images by the genetic algorithm
NASA Astrophysics Data System (ADS)
Jiang, Ching-Fen
2001-07-01
The textural-feature-based segmentation methods were widely applied to the segmentation problems of ultrasonic images. However the manual selection of textural features in the previous approaches not only makes these segmentation methods inadaptable but could lead to the results with bias. Herein we propose an auto-feature-selection algorithm to solve the problems. This algorithm includes three steps: The feature library composed of 32 textural features was established at first. The genetic algorithm was then used to auto-select the features and give each of them different weight according to their importance. The fitness of each gene was evaluated by five factors including region dissimilarity, number of edge points, edge fragmentation, edge thickness, and curvature. Finally, K-means process classified the image into 3 different tissues using the selected features with different weights. The segmentation outcomes of various ultrasonic images by this auto-feature selection algorithm have shown better correspondence with human comprehension in comparison with the results of previous works. In addition, it provides a more adaptive way to adjust the weight of the features used for clustering process and therefore to avoid takeover by the big-value features. This problem has been paid little attention in the traditional K-means process in which all the features have the same weight.
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).
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.
Genetic Algorithm based Decentralized PI Type Controller: Load Frequency Control
NASA Astrophysics Data System (ADS)
Dwivedi, Atul; Ray, Goshaidas; Sharma, Arun Kumar
2016-05-01
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.
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.
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.
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.
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.
Applications of genetic algorithms and neural networks to interatomic potentials
NASA Astrophysics Data System (ADS)
Hobday, Steven; Smith, Roger; BelBruno, Joe
1999-06-01
Applications of two modern artificial intelligence (AI) techniques, genetic algorithms (GA) and neural networks (NN) to computer simulations are reported. It is shown that the GA are very useful tools for determining the minimum energy structures of clusters of atoms described by interatomic potential functions and generally outperform other optimisation methods for this task. A number of applications are given including covalent, and close packed structures of single or multi-component atomic species. It is also shown that (many body) interatomic potential functions for multi-component systems can be derived by training a specially constructed NN on a variety of structural data.
A swarm intelligence based memetic algorithm for task allocation in distributed systems
NASA Astrophysics Data System (ADS)
Sarvizadeh, Raheleh; Haghi Kashani, Mostafa
2011-12-01
This paper proposes a Swarm Intelligence based Memetic algorithm for Task Allocation and scheduling in distributed systems. The tasks scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperformed the existing GA-based method in terms of CPU utilization.
A swarm intelligence based memetic algorithm for task allocation in distributed systems
NASA Astrophysics Data System (ADS)
Sarvizadeh, Raheleh; Haghi Kashani, Mostafa
2012-01-01
This paper proposes a Swarm Intelligence based Memetic algorithm for Task Allocation and scheduling in distributed systems. The tasks scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperformed the existing GA-based method in terms of CPU utilization.
Modelling Paleoearthquake Slip Distributions using a Gentic Algorithm
NASA Astrophysics Data System (ADS)
Lindsay, Anthony; Simão, Nuno; McCloskey, John; Nalbant, Suleyman; Murphy, Shane; Bhloscaidh, Mairead Nic
2013-04-01
Along the Sunda trench, the annual growth rings of coral microatolls store long term records of tectonic deformation. Spread over large areas of an active megathrust fault, they offer the possibility of high resolution reconstructions of slip for a number of paleo-earthquakes. These data are complex with spatial and temporal variations in uncertainty. Rather than assuming that any one model will uniquely fit the data, Monte Carlo Slip Estimation (MCSE) modelling produces a catalogue of possible models for each event. From each earthquake's catalogue, a model is selected and a possible history of slip along the fault reconstructed. By generating multiple histories, then finding the average slip during each earthquake, a probabilistic history of slip along the fault can be generated and areas that may have a large slip deficit identified. However, the MCSE technique requires the production of many hundreds of billions of models to yield the few models that fit the observed coral data. In an attempt to accelerate this process, we have designed a Genetic Algorithm (GA). The GA uses evolutionary operators to recombine the information held by a population of possible slip models to produce a set of new models, based on how well they reproduce a set of coral deformation data. Repeated iterations of the algorithm produce populations of improved models, each generation better satisfying the coral data. Preliminary results have shown the GA to be capable of recovering synthetically generated slip distributions based their displacements of sets of corals faster than the MCSE technique. The results of the systematic testing of the GA technique and its performance using both synthetic and observed coral displacement data will be presented.
Distributed concurrency control performance: A study of algorithms, distribution, and replication
Carey, M.J.; Livny, M.
1988-01-01
Many concurrency control algorithms have been proposed for use in distributed database systems. Despite the large number of available algorithms, and the fact that distributed database systems are becoming a commercial reality, distributed concurrency control performance tradeoffs are still not well understood. In this paper the authors attempt to shed light on some of the important issues by studying the performance of four representative algorithms - distributed 2PL, wound-wait, basic timestamp ordering, and a distributed optimistic algorithm - using a detailed simulation model of a distributed DBMS. The authors examine the performance of these algorithms for various levels of contention, ''distributedness'' of the workload, and data replication. The results should prove useful to designers of future distributed database systems.
Echoed time series predictions, neural networks and genetic algorithms
NASA Astrophysics Data System (ADS)
Conway, A.
This work aims to illustrate a potentially serious and previously unrecognised problem in using Neural Networks (NNs), and possibly other techniques, to predict Time Series (TS). It also demonstrates how a new training scheme using a genetic algorithm can alleviate this problem. Although it is already established that NNs can predict TS such as Sunspot Number (SSN) with reasonable success, the accuracy of these predictions is often judged solely by an RMS or related error. The use of this type of error overlooks the presence of what we have termed echoing, where the NN outputs its most recent input as its prediction. Therefore, a method of detecting echoed predictions is introduced, called time-shifting. Reasons for the presence of echo are discussed and then related to the choice of TS sampling. Finally, a new specially designed training scheme is described, which is a hybrid of a genetic algorithm search and back propagation. With this method we have successfully trained NNs to predict without any echo.
Internal Lattice Reconfiguration for Diversity Tuning in Cellular Genetic Algorithms
Morales-Reyes, Alicia; Erdogan, Ahmet T.
2012-01-01
Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover, internal reconfiguration allows the adjacency of individuals to be maintained. Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy. PMID:22859973
Generation of Compliant Mechanisms using Hybrid Genetic Algorithm
NASA Astrophysics Data System (ADS)
Sharma, D.; Deb, K.
2014-10-01
Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( η). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for η value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.
A genetic algorithm approach to recognition and data mining
Punch, W.F.; Goodman, E.D.; Min, Pei
1996-12-31
We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.
Feature Subset Selection, Class Separability, and Genetic Algorithms
Cantu-Paz, E
2004-01-21
The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be unpractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. The experiments suggest that the hybrid usually finds compact feature subsets that give the most accurate results, while beating the execution time of the other wrappers.
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.
An Introduction to Genetic Algorithms and to Their Use in Information Retrieval.
ERIC Educational Resources Information Center
Jones, Gareth; And Others
1994-01-01
Genetic algorithms, a class of nondeterministic algorithms in which the role of chance makes the precise nature of a solution impossible to guarantee, seem to be well suited to combinatorial-optimization problems in information retrieval. Provides an introduction to techniques and characteristics of genetic algorithms and illustrates their…
Combining neural networks and genetic algorithms for hydrological flow forecasting
NASA Astrophysics Data System (ADS)
Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr
2010-05-01
We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and
An Evolved Wavelet Library Based on Genetic Algorithm
Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.
2014-01-01
As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR. PMID:25405225
Integrating GIS and genetic algorithms for automating land partitioning
NASA Astrophysics Data System (ADS)
Demetriou, Demetris; See, Linda; Stillwell, John
2014-08-01
Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.
Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi; Mousavi, Reza
2015-01-01
In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy. PMID:25786796
Parallel matrix transpose algorithms on distributed memory concurrent computers
Choi, J.; Walker, D.W.; Dongarra, J.J. |
1993-10-01
This paper describes parallel matrix transpose algorithms on distributed memory concurrent processors. It is assumed that the matrix is distributed over a P x Q processor template with a block scattered data distribution. P, Q, and the block size can be arbitrary, so the algorithms have wide applicability. The communication schemes of the algorithms are determined by the greatest common divisor (GCD) of P and Q. If P and Q are relatively prime, the matrix transpose algorithm involves complete exchange communication. If P and Q are not relatively prime, processors are divided into GCD groups and the communication operations are overlapped for different groups of processors. Processors transpose GCD wrapped diagonal blocks simultaneously, and the matrix can be transposed with LCM/GCD steps, where LCM is the least common multiple of P and Q. The algorithms make use of non-blocking, point-to-point communication between processors. The use of nonblocking communication allows a processor to overlap the messages that it sends to different processors, thereby avoiding unnecessary synchronization. Combined with the matrix multiplication routine, C = A{center_dot}B, the algorithms are used to compute parallel multiplications of transposed matrices, C = A{sup T}{center_dot}B{sup T}, in the PUMMA package. Details of the parallel implementation of the algorithms are given, and results are presented for runs on the Intel Touchstone Delta computer.
Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.
Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi
2014-09-01
Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter. PMID:25190869
Ancestral genome inference using a genetic algorithm approach.
Gao, Nan; Yang, Ning; Tang, Jijun
2013-01-01
Recent advancement of technologies has now made it routine to obtain and compare gene orders within genomes. Rearrangements of gene orders by operations such as reversal and transposition are rare events that enable researchers to reconstruct deep evolutionary histories. An important application of genome rearrangement analysis is to infer gene orders of ancestral genomes, which is valuable for identifying patterns of evolution and for modeling the evolutionary processes. Among various available methods, parsimony-based methods (including GRAPPA and MGR) are the most widely used. Since the core algorithms of these methods are solvers for the so called median problem, providing efficient and accurate median solver has attracted lots of attention in this field. The "double-cut-and-join" (DCJ) model uses the single DCJ operation to account for all genome rearrangement events. Because mathematically it is much simpler than handling events directly, parsimony methods using DCJ median solvers has better speed and accuracy. However, the DCJ median problem is NP-hard and although several exact algorithms are available, they all have great difficulties when given genomes are distant. In this paper, we present a new algorithm that combines genetic algorithm (GA) with genomic sorting to produce a new method which can solve the DCJ median problem in limited time and space, especially in large and distant datasets. Our experimental results show that this new GA-based method can find optimal or near optimal results for problems ranging from easy to very difficult. Compared to existing parsimony methods which may severely underestimate the true number of evolutionary events, the sorting-based approach can infer ancestral genomes which are much closer to their true ancestors. The code is available at http://phylo.cse.sc.edu. PMID:23658708
NASA Astrophysics Data System (ADS)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Huang, Chien-Feng; Wolpert, David H.; Bieniawski, Stefan; Strauss, Charles E. M.
2005-01-01
We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems. It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.
Bornholdt, S.; Graudenz, D.
1993-07-01
A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.
Feature Subset Selection by Estimation of Distribution Algorithms
Cantu-Paz, E
2002-01-17
This paper describes the application of four evolutionary algorithms to the identification of feature subsets for classification problems. Besides a simple GA, the paper considers three estimation of distribution algorithms (EDAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the EDAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. In contrast with previous studies, we did not find evidence to support or reject the use of EDAs for this problem.
A scalable parallel graph coloring algorithm for distributed memory computers.
Bozdag, Doruk; Manne, Fredrik; Gebremedhin, Assefaw H.; Catalyurek, Umit; Boman, Erik Gunnar
2005-02-01
In large-scale parallel applications a graph coloring is often carried out to schedule computational tasks. In this paper, we describe a new distributed memory algorithm for doing the coloring itself in parallel. The algorithm operates in an iterative fashion; in each round vertices are speculatively colored based on limited information, and then a set of incorrectly colored vertices, to be recolored in the next round, is identified. Parallel speedup is achieved in part by reducing the frequency of communication among processors. Experimental results on a PC cluster using up to 16 processors show that the algorithm is scalable.
Impacts of Time Delays on Distributed Algorithms for Economic Dispatch
Yang, Tao; Wu, Di; Sun, Yannan; Lian, Jianming
2015-07-26
Economic dispatch problem (EDP) is an important problem in power systems. It can be formulated as an optimization problem with the objective to minimize the total generation cost subject to the power balance constraint and generator capacity limits. Recently, several consensus-based algorithms have been proposed to solve EDP in a distributed manner. However, impacts of communication time delays on these distributed algorithms are not fully understood, especially for the case where the communication network is directed, i.e., the information exchange is unidirectional. This paper investigates communication time delay effects on a distributed algorithm for directed communication networks. The algorithm has been tested by applying time delays to different types of information exchange. Several case studies are carried out to evaluate the effectiveness and performance of the algorithm in the presence of time delays in communication networks. It is found that time delay effects have negative effects on the convergence rate, and can even result in an incorrect converge value or fail the algorithm to converge.
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
Improving ecological forecasts of copepod community dynamics using genetic algorithms
NASA Astrophysics Data System (ADS)
Record, N. R.; Pershing, A. J.; Runge, J. A.; Mayo, C. A.; Monger, B. C.; Chen, C.
2010-08-01
The validity of computational models is always in doubt. Skill assessment and validation are typically done by demonstrating that output is in agreement with empirical data. We test this approach by using a genetic algorithm to parameterize a biological-physical coupled copepod population dynamics computation. The model is applied to Cape Cod Bay, Massachusetts, and is designed for operational forecasting. By running twin experiments on terms in this dynamical system, we demonstrate that a good fit to data does not necessarily imply a valid parameterization. An ensemble of good fits, however, provides information on the accuracy of parameter values, on the functional importance of parameters, and on the ability to forecast accurately with an incorrect set of parameters. Additionally, we demonstrate that the technique is a useful tool for operational forecasting.
An adaptive genetic algorithm for crystal structure prediction
Wu, Shunqing; Ji, Min; Wang, Cai-Zhuang; Nguyen, Manh Cuong; Zhao, Xin; Umemoto, K.; Wentzcovitch, R. M.; Ho, Kai-Ming
2013-12-18
We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.
Merging of synchrotron serial crystallographic data by a genetic algorithm.
Zander, Ulrich; Cianci, Michele; Foos, Nicolas; Silva, Catarina S; Mazzei, Luca; Zubieta, Chloe; de Maria, Alejandro; Nanao, Max H
2016-09-01
Recent advances in macromolecular crystallography have made it practical to rapidly collect hundreds of sub-data sets consisting of small oscillations of incomplete data. This approach, generally referred to as serial crystallography, has many uses, including an increased effective dose per data set, the collection of data from crystals without harvesting (in situ data collection) and studies of dynamic events such as catalytic reactions. However, selecting which data sets from this type of experiment should be merged can be challenging and new methods are required. Here, it is shown that a genetic algorithm can be used for this purpose, and five case studies are presented in which the merging statistics are significantly improved compared with conventional merging of all data. PMID:27599735
Optimization of Power Coefficient of Wind Turbine Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Rajakumar, Sappani; Ravindran, Durairaj; Sivakumar, Mahalingam; Venkatachalam, Gopalan; Muthukumar, Shunmugavelu
2016-06-01
In the design of a wind turbine, the goal is to attain the highest possible power output under specified atmospheric conditions. The optimization of power coefficient of horizontal axis wind turbine has been carried out by integration of blade element momentum method and genetic algorithm (GA). The design variables considered are wind velocity, angle of attack and tip speed ratio. The objective function is power coefficient of wind turbine. The different combination of design variables are optimized using GA and then the Power coefficient is optimized. The optimized design variables are validated with the experimental results available in the literature. By this optimization work the optimum design variables of wind turbine can be found economically than experimental work. NACA44XX series airfoils are considered for this optimization work.
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
Alien Genetic Algorithm for Exploration of Search Space
NASA Astrophysics Data System (ADS)
Patel, Narendra; Padhiyar, Nitin
2010-10-01
Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.
Evolutionary Design of Rule Changing Artificial Society Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Wu, Yun; Kanoh, Hitoshi
Socioeconomic phenomena, cultural progress and political organization have recently been studied by creating artificial societies consisting of simulated agents. In this paper we propose a new method to design action rules of agents in artificial society that can realize given requests using genetic algorithms (GAs). In this paper we propose an efficient method for designing the action rules of agents that will constitute an artificial society that meets a specified demand by using a GAs. In the proposed method, each chromosome in the GA population represents a candidate set of action rules and the number of rule iterations. While a conventional method applies distinct rules in order of precedence, the present method applies a set of rules repeatedly for a certain period. The present method is aiming at both firm evolution of agent population and continuous action by that. Experimental results using the artificial society proved that the present method can generate artificial society which fills a demand in high probability.
Genetic Algorithms and Nucleation in VIH-AIDS transition.
NASA Astrophysics Data System (ADS)
Barranon, Armando
2003-03-01
VIH to AIDS transition has been modeled via a genetic algorithm that uses boom-boom principle and where population evolution is simulated with a cellular automaton based on SIR model. VIH to AIDS transition is signed by nucleation of infected cells and low probability of infection are obtained for different mutation rates in agreement with clinical results. A power law is obtained with a critical exponent close to the critical exponent of cubic, spherical percolation, colossal magnetic resonance, Ising Model and liquid-gas phase transition in heavy ion collisions. Computations were carried out at UAM-A Supercomputing Lab and author acknowledges financial support from Division of CBI at UAM-A.
Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm
NASA Astrophysics Data System (ADS)
Xu, Liming
The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.
Tuning of active vibration controllers for ACTEX by genetic algorithm
NASA Astrophysics Data System (ADS)
Kwak, Moon K.; Denoyer, Keith K.
1999-06-01
This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.
Optimizing the controllability of arbitrary networks with genetic algorithm
NASA Astrophysics Data System (ADS)
Li, Xin-Feng; Lu, Zhe-Ming
2016-04-01
Recently, as the controllability of complex networks attracts much attention, how to optimize networks' controllability has become a common and urgent problem. In this paper, we develop an efficient genetic algorithm oriented optimization tool to optimize the controllability of arbitrary networks consisting of both state nodes and control nodes under Popov-Belevitch-Hautus rank condition. The experimental results on a number of benchmark networks show the effectiveness of this method and the evolution of network topology is captured. Furthermore, we explore how network structure affects its controllability and find that the sparser a network is, the more control nodes are needed to control it and the larger the differences between node degrees, the more control nodes are needed to achieve the full control. Our framework provides an alternative to controllability optimization and can be applied to arbitrary networks without any limitations.
Optimal Design of RF Energy Harvesting Device Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Mori, T.; Sato, Y.; Adriano, R.; Igarashi, H.
2015-11-01
This paper presents optimal design of an RF energy harvesting device using genetic algorithm (GA). In the present RF harvester, a planar spiral antenna (PSA) is loaded with matching and rectifying circuits. On the first stage of the optimal design, the shape parameters of PSA are optimized using . Then, the equivalent circuit of the optimized PSA is derived for optimization of the circuits. Finally, the parameters of RF energy harvesting circuit are optimized to maximize the output power using GA. It is shown that the present optimization increases the output power by a factor of five. The manufactured energy harvester starts working when the input electric field is greater than 0.5 V/m.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2014-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and later on solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. A remaining issue is the cost of hybrids vs the existing launch propulsion systems. This paper will review the known state of the art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2015-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
A new perspective on dark energy modeling via genetic algorithms
Nesseris, Savvas; García-Bellido, Juan E-mail: juan.garciabellido@uam.es
2012-11-01
We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity d{sub L}(z) and the angular diameter distance d{sub A}(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ω{sub m}(a) in the growth rate data, fσ{sub 8}(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function Ω{sub M}(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡d{sub L}(z)/(1+z){sup 2}d{sub A}(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ∼ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.
Mesh Algorithms for PDE with Sieve I: Mesh Distribution
Knepley, Matthew G.; Karpeev, Dmitry A.
2009-01-01
We have developed a new programming framework, called Sieve, to support parallel numerical partial differential equation(s) (PDE) algorithms operating over distributed meshes. We have also developed a reference implementation of Sieve in C++ as a library of generic algorithms operating on distributed containers conforming to the Sieve interface. Sieve makes instances of the incidence relation, or arrows, the conceptual first-class objects represented in the containers. Further, generic algorithms acting on this arrow container are systematically used to provide natural geometric operations on the topology and also, through duality, on the data. Finally, coverings and duality are used to encode notmore » only individual meshes, but all types of hierarchies underlying PDE data structures, including multigrid and mesh partitions. In order to demonstrate the usefulness of the framework, we show how the mesh partition data can be represented and manipulated using the same fundamental mechanisms used to represent meshes. We present the complete description of an algorithm to encode a mesh partition and then distribute a mesh, which is independent of the mesh dimension, element shape, or embedding. Moreover, data associated with the mesh can be similarly distributed with exactly the same algorithm. The use of a high level of abstraction within the Sieve leads to several benefits in terms of code reuse, simplicity, and extensibility. We discuss these benefits and compare our approach to other existing mesh libraries.« less
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2013-03-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2012-04-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
Use of genetic algorithm for the selection of EEG features
NASA Astrophysics Data System (ADS)
Asvestas, P.; Korda, A.; Kostopoulos, S.; Karanasiou, I.; Ouzounoglou, A.; Sidiropoulos, K.; Ventouras, E.; Matsopoulos, G.
2015-09-01
Genetic Algorithm (GA) is a popular optimization technique that can detect the global optimum of a multivariable function containing several local optima. GA has been widely used in the field of biomedical informatics, especially in the context of designing decision support systems that classify biomedical signals or images into classes of interest. The aim of this paper is to present a methodology, based on GA, for the selection of the optimal subset of features that can be used for the efficient classification of Event Related Potentials (ERPs), which are recorded during the observation of correct or incorrect actions. In our experiment, ERP recordings were acquired from sixteen (16) healthy volunteers who observed correct or incorrect actions of other subjects. The brain electrical activity was recorded at 47 locations on the scalp. The GA was formulated as a combinatorial optimizer for the selection of the combination of electrodes that maximizes the performance of the Fuzzy C Means (FCM) classification algorithm. In particular, during the evolution of the GA, for each candidate combination of electrodes, the well-known (Σ, Φ, Ω) features were calculated and were evaluated by means of the FCM method. The proposed methodology provided a combination of 8 electrodes, with classification accuracy 93.8%. Thus, GA can be the basis for the selection of features that discriminate ERP recordings of observations of correct or incorrect actions.
South American foF2 database using genetic algorithms
NASA Astrophysics Data System (ADS)
Gularte, Erika; Bilitza, Dieter; Carpintero, Daniel; Jaen, Juliana
2016-07-01
We present the first step towards a new database of the ionospheric parameter foF2 for the South American region. The foF2 parameter, being the maximum of the ionospheric electronic density profile and its main sculptor, is of great interest not only in atmospheric studies but also in the realm of radio propagation. Due to its importance, its large variability and the difficulty to model it in time and space, it was the subject of an intense study since decades ago. The current databases, used by the IRI (International Reference Ionosphere) model, and based on Fourier expansions, has been built in the 60s from the available ionosondes at that time; therefore, it is still short of South American data. The main goal of this work is to upgrade the database, incorporating the now available data compiled by the RAPEAS (Red Argentina para el Estudio de la Atmósfera Superior, Argentine Network for the Study of the Upper Atmosphere) network. Also, we developed an algorithm to study the foF2 variability, based on the modern technique of genetic algorithms, which has been successfully applied on other disciplines. One of the main advantages of this technique is its ability in working with many variables and with unfavorable samples. The results are compared with the IRI databases, and improvements to the latter are suggested. Finally, it is important to notice that the new database is designed so that new available data can be easily incorporated.
Human emotion detector based on genetic algorithm using lip features
NASA Astrophysics Data System (ADS)
Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga
2010-04-01
We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Svečko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
Genetic algorithm parameter optimization: applied to sensor coverage
NASA Astrophysics Data System (ADS)
Sahin, Ferat; Abbate, Giuseppe
2004-08-01
Genetic Algorithms are powerful tools, which when set upon a solution space will search for the optimal answer. These algorithms though have some associated problems, which are inherent to the method such as pre-mature convergence and lack of population diversity. These problems can be controlled with changes to certain parameters such as crossover, selection, and mutation. This paper attempts to tackle these problems in GA by having another GA controlling these parameters. The values for crossover parameter are: one point, two point, and uniform. The values for selection parameters are: best, worst, roulette wheel, inside 50%, outside 50%. The values for the mutation parameter are: random and swap. The system will include a control GA whose population will consist of different parameters settings. While this GA is attempting to find the best parameters it will be advancing into the search space of the problem and refining the population. As the population changes due to the search so will the optimal parameters. For every control GA generation each of the individuals in the population will be tested for fitness by being run through the problem GA with the assigned parameters. During these runs the population used in the next control generation is compiled. Thus, both the issue of finding the best parameters and the solution to the problem are attacked at the same time. The goal is to optimize the sensor coverage in a square field. The test case used was a 30 by 30 unit field with 100 sensor nodes. Each sensor node had a coverage area of 3 by 3 units. The algorithm attempts to optimize the sensor coverage in the field by moving the nodes. The results show that the control GA will provide better results when compared to a system with no parameter changes.
NASA Astrophysics Data System (ADS)
Xu, Dexiang
This dissertation presents a novel method of designing finite word length Finite Impulse Response (FIR) digital filters using a Real Parameter Parallel Genetic Algorithm (RPPGA). This algorithm is derived from basic Genetic Algorithms which are inspired by natural genetics principles. Both experimental results and theoretical studies in this work reveal that the RPPGA is a suitable method for determining the optimal or near optimal discrete coefficients of finite word length FIR digital filters. Performance of RPPGA is evaluated by comparing specifications of filters designed by other methods with filters designed by RPPGA. The parallel and spatial structures of the algorithm result in faster and more robust optimization than basic genetic algorithms. A filter designed by RPPGA is implemented in hardware to attenuate high frequency noise in a data acquisition system for collecting seismic signals. These studies may lead to more applications of the Real Parameter Parallel Genetic Algorithms in Electrical Engineering.
A genetic-based algorithm for personalized resistance training
Kiely, J; Suraci, B; Collins, DJ; de Lorenzo, D; Pickering, C; Grimaldi, KA
2016-01-01
Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective
A genetic-based algorithm for personalized resistance training.
Jones, N; Kiely, J; Suraci, B; Collins, D J; de Lorenzo, D; Pickering, C; Grimaldi, K A
2016-06-01
Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective
Orbit design and estimation for surveillance missions using genetic algorithms
NASA Astrophysics Data System (ADS)
Abdelkhalik, Osama Mohamed Omar
2005-11-01
The problem of observing a given set of Earth target sites within an assigned time frame is examined. Attention is given mainly to visiting these sites as sub-satellite nadir points. Solutions to this problem in the literature require thrusters to continuously maneuver the satellite from one site to another. A natural solution is proposed. A natural solution is a gravitational orbit that enables the spacecraft to satisfy the mission requirements without maneuvering. Optimization of a penalty function is performed to find natural solutions for satellite orbit configurations. This penalty function depends on the mission objectives. Two mission objectives are considered: maximum observation time and maximum resolution. The penalty function poses multi minima and a genetic algorithm technique is used to solve this problem. In the case that there is no one orbit satisfying the mission requirements, a multi-orbit solution is proposed. In a multi-orbit solution, the set of target sites is split into two groups. Then the developed algorithm is used to search for a natural solution for each group. The satellite has to be maneuvered between the two solution orbits. Genetic algorithms are used to find the optimal orbit transfer between the two orbits using impulsive thrusters. A new formulation for solving the orbit maneuver problem using genetic algorithms is developed. The developed formulation searches for a minimum fuel consumption maneuver and guarantees that the satellite will be transferred exactly to the final orbit even if the solution is non-optimal. The results obtained demonstrate the feasibility of finding natural solutions for many case studies. The problem of the design of suitable satellite constellation for Earth observing applications is addressed. Two cases are considered. The first is the remote sensing missions for a particular region with high frequency and small swath width. The second is the interferometry radar Earth observation missions. In satellite
SPLICER - A GENETIC ALGORITHM TOOL FOR SEARCH AND OPTIMIZATION, VERSION 1.0 (MACINTOSH VERSION)
NASA Technical Reports Server (NTRS)
Wang, L.
1994-01-01
SPLICER is a genetic algorithm tool which can be used to solve search and optimization problems. Genetic algorithms are adaptive search procedures (i.e. problem solving methods) based loosely on the processes of natural selection and Darwinian "survival of the fittest." SPLICER provides the underlying framework and structure for building a genetic algorithm application. These algorithms apply genetically-inspired operators to populations of potential solutions in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. SPLICER 1.0 was created using a modular architecture that includes a Genetic Algorithm Kernel, interchangeable Representation Libraries, Fitness Modules and User Interface Libraries, and well-defined interfaces between these components. The architecture supports portability, flexibility, and extensibility. SPLICER comes with all source code and several examples. For instance, a "traveling salesperson" example searches for the minimum distance through a number of cities visiting each city only once. Stand-alone SPLICER applications can be used without any programming knowledge. However, to fully utilize SPLICER within new problem domains, familiarity with C language programming is essential. SPLICER's genetic algorithm (GA) kernel was developed independent of representation (i.e. problem encoding), fitness function or user interface type. The GA kernel comprises all functions necessary for the manipulation of populations. These functions include the creation of populations and population members, the iterative population model, fitness scaling, parent selection and sampling, and the generation of population statistics. In addition, miscellaneous functions are included in the kernel (e.g., random number generators). Different problem-encoding schemes and functions are defined and stored in interchangeable representation libraries. This allows the GA kernel to be used with any
Intelligent decision support algorithm for distribution system restoration.
Singh, Reetu; Mehfuz, Shabana; Kumar, Parmod
2016-01-01
Distribution system is the means of revenue for electric utility. It needs to be restored at the earliest if any feeder or complete system is tripped out due to fault or any other cause. Further, uncertainty of the loads, result in variations in the distribution network's parameters. Thus, an intelligent algorithm incorporating hybrid fuzzy-grey relation, which can take into account the uncertainties and compare the sequences is discussed to analyse and restore the distribution system. The simulation studies are carried out to show the utility of the method by ranking the restoration plans for a typical distribution system. This algorithm also meets the smart grid requirements in terms of an automated restoration plan for the partial/full blackout of network. PMID:27512634
Optimization of energy saving device combined with a propeller using real-coded genetic algorithm
NASA Astrophysics Data System (ADS)
Ryu, Tomohiro; Kanemaru, Takashi; Kataoka, Shiro; Arihama, Kiyoshi; Yoshitake, Akira; Arakawa, Daijiro; Ando, Jun
2014-06-01
This paper presents a numerical optimization method to improve the performance of the propeller with Turbo-Ring using real-coded genetic algorithm. In the presented method, Unimodal Normal Distribution Crossover (UNDX) and Minimal Generation Gap (MGG) model are used as crossover operator and generation-alternation model, respectively. Propeller characteristics are evaluated by a simple surface panel method "SQCM" in the optimization process. Blade sections of the original Turbo-Ring and propeller are replaced by the NACA66 a = 0.8 section. However, original chord, skew, rake and maximum blade thickness distributions in the radial direction are unchanged. Pitch and maximum camber distributions in the radial direction are selected as the design variables. Optimization is conducted to maximize the efficiency of the propeller with Turbo-Ring. The experimental result shows that the efficiency of the optimized propeller with Turbo-Ring is higher than that of the original propeller with Turbo-Ring.
Mokeddem, Diab; Khellaf, Abdelhafid
2009-01-01
Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples. PMID:19543537
Empirical study of self-configuring genetic programming algorithm performance and behaviour
NASA Astrophysics Data System (ADS)
Semenkin, E.; Semenkina, M.
2015-01-01
The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems.
Comparing Different Fault Identification Algorithms in Distributed Power System
NASA Astrophysics Data System (ADS)
Alkaabi, Salim
A power system is a huge complex system that delivers the electrical power from the generation units to the consumers. As the demand for electrical power increases, distributed power generation was introduced to the power system. Faults may occur in the power system at any time in different locations. These faults cause a huge damage to the system as they might lead to full failure of the power system. Using distributed generation in the power system made it even harder to identify the location of the faults in the system. The main objective of this work is to test the different fault location identification algorithms while tested on a power system with the different amount of power injected using distributed generators. As faults may lead the system to full failure, this is an important area for research. In this thesis different fault location identification algorithms have been tested and compared while the different amount of power is injected from distributed generators. The algorithms were tested on IEEE 34 node test feeder using MATLAB and the results were compared to find when these algorithms might fail and the reliability of these methods.
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.
User-Based Document Clustering by Redescribing Subject Descriptions with a Genetic Algorithm.
ERIC Educational Resources Information Center
Gordon, Michael D.
1991-01-01
Discussion of clustering of documents and queries in information retrieval systems focuses on the use of a genetic algorithm to adapt subject descriptions so that documents become more effective in matching relevant queries. Various types of clustering are explained, and simulation experiments used to test the genetic algorithm are described. (27…
Order-Based Fitness Functions for Genetic Algorithms Applied to Relevance Feedback.
ERIC Educational Resources Information Center
Lopez-Pujalte, Cristina; Guerrero-Bote, Vicente P.; de Moya-Anegon, Felix
2003-01-01
Discusses genetic algorithms in information retrieval, especially for relevance feedback, and evaluates the efficacy of a genetic algorithm with various order-based fitness functions for relevance feedback in a test database. Compares results with the Ide dec-hi method, one of the best traditional methods. (Contains 56 references.) (Author/LRW)
Using genetic algorithms to select and create features for pattern classification. Technical report
Chang, E.I.; Lippmann, R.P.
1991-03-11
Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech pattern classification tasks. On a 15-feature machine-vision inspection task, it was found that genetic algorithms performed no better than conventional approaches to feature selection but required much more computation. For a speech recognition task, genetic algorithms required no more computation time than traditional approaches but reduced the number of features required by a factor of five (from 153 to 33 features). On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) that reduced classification error rates from 10 to almost 0 percent. Neural net and nearest-neighbor classifiers were unable to provide such low error rates using only the original features. Genetic algorithms were also used to reduce the number of reference exemplar patterns and to select the value of k for a k-nearest-neighbor classifier. On a .338 training pattern vowel recognition problem with 10 classes, genetic algorithms simultaneously reduced the number of stored exemplars from 338 to 63 and selected k without significantly decreasing classification accuracy. In all applications, genetic algorithms were easy to apply and found good solutions in many fewer trials than would be required by an exhaustive search. Run times were long but not unreasonable. These results suggest that genetic algorithms may soon be practical for pattern classification problems as faster serial and parallel computers are developed.
Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo
2016-01-01
Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. PMID:26764911
Distributed autonomous systems: resource management, planning, and control algorithms
NASA Astrophysics Data System (ADS)
Smith, James F., III; Nguyen, ThanhVu H.
2005-05-01
Distributed autonomous systems, i.e., systems that have separated distributed components, each of which, exhibit some degree of autonomy are increasingly providing solutions to naval and other DoD problems. Recently developed control, planning and resource allocation algorithms for two types of distributed autonomous systems will be discussed. The first distributed autonomous system (DAS) to be discussed consists of a collection of unmanned aerial vehicles (UAVs) that are under fuzzy logic control. The UAVs fly and conduct meteorological sampling in a coordinated fashion determined by their fuzzy logic controllers to determine the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy planning algorithm determines the optimal trajectory, sampling rate and pattern for the UAVs and an interferometer platform while taking into account risk, reliability, priority for sampling in certain regions, fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV will give the UAV limited autonomy allowing it to change course immediately without consulting with any commander, request other UAVs to help it, alter its sampling pattern and rate when observing interesting phenomena, or to terminate the mission and return to base. The algorithms developed will be compared to a resource manager (RM) developed for another DAS problem related to electronic attack (EA). This RM is based on fuzzy logic and optimized by evolutionary algorithms. It allows a group of dissimilar platforms to use EA resources distributed throughout the group. For both DAS types significant theoretical and simulation results will be presented.
A distributed Canny edge detector: algorithm and FPGA implementation.
Xu, Qian; Varadarajan, Srenivas; Chakrabarti, Chaitali; Karam, Lina J
2014-07-01
The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM READ/WRITE time and the computation time) to detect edges of 512 × 512 images in the USC SIPI database when clocked at 100
Efficient Improvement of Silage Additives by Using Genetic Algorithms
Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.
2000-01-01
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage
Modelling and genetic algorithm based optimisation of inverse supply chain
NASA Astrophysics Data System (ADS)
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
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, 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.
A new distributed systems scheduling algorithm: a swarm intelligence approach
NASA Astrophysics Data System (ADS)
Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi
2011-12-01
The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.
A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems
NASA Astrophysics Data System (ADS)
Thammano, Arit; Teekeng, Wannaporn
2015-05-01
The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.
Genetic algorithms approach for the extraction of the polygonal approximation of planar objects
NASA Astrophysics Data System (ADS)
Erives, Hector; Parra-Loera, Ramon
1996-06-01
A new approach to the extraction of the polygonal approximation is presented. The method obtains a smaller set of the important features by means of an evolutionary algorithm. A genetic approach with some heuristics, improves contour approximation search by starting with a parallel search at various points in the contour. The algorithm uses genetic algorithms to encode a polygonal approximation as a chromosome and evolve it to provide a polygonal approximation. Experimental results are provided.
Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2001-01-01
A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.
NASA Astrophysics Data System (ADS)
Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie
2015-12-01
The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.
User Profile Creation Using Genetic Algorithm with Kullback Leibler Divergence
NASA Astrophysics Data System (ADS)
Hidekazu, Yanagimoto; Sigeru, Omatu
In this paper we propose a user profile creation method using the Kullback Leibler divergence. To cope with information flood, many information filtering systems have been developed up to now. In the information filtering systems it is important to create a user profile which represents user's interests correctly. Since almost all information filtering systems are developed with techniques of information retrieval, machine learning, and pattern recognition, they often use a linear function as a discriminant function. To classify information in the field of document classification more precisely, the systems have been reported which use a non-linear function as a discriminant function. The proposed method is to use the Kullback Leibler divergence as a discriminant function which denotes to user's interest in the information filtering system. To identify an optimal discriminat function with documents which a user evaluates, we use the real-coded genetic algorithm. We compare the present method with the other one using a linear discriminant function and confirm the effectiveness of the proposing method.
Genetic algorithm optimized triply compensated pulses in NMR spectroscopy
NASA Astrophysics Data System (ADS)
Manu, V. S.; Veglia, Gianluigi
2015-11-01
Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature's evolutionary processes. The newly designed π and π / 2 pulses belong to the 'type A' (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U-13C, 15N NAVL peptide as well as U-13C, 15N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences.
Reducing aerodynamic vibration with piezoelectric actuators: a genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Hu, Zhenning; Jakiela, Mark; Pitt, Dale M.; Burnham, Jay K.
2004-07-01
Modern high performance aircraft fly at high speeds and high angles of attack. This can result in "buffet" aerodynamics, an unsteady turbulent flow that causes vibrations of the wings, tails, and body of the aircraft. This can result in decreased performance and ride quality, and fatigue failures. We are experimenting with controlling these vibrations by using piezoceramic actuators attached to the inner and outer skin of the aircraft. In this project, a tail or wing is investigated. A "generic" tail finite element model is studied in which individual actuators are assumed to exactly cover individual finite elements. Various optimizations of the orientations and power consumed by these actuators are then performed. Real coded genetic algorithms are used to perform the optimizations and a design space approximation technique is used to minimize costly finite element runs. An important result is the identification of a power consumption threshold for the entire system. Below the threshold, vibration control performance of optimized systems decreases with decreasing values of power supplied to the entire system.
Constrained genetic algorithms for optimizing multi-use reservoir operation
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Chang, Fi-John; Wang, Kuo-Wei; Dai, Shin-Yi
2010-08-01
To derive an optimal strategy for reservoir operations to assist the decision-making process, we propose a methodology that incorporates the constrained genetic algorithm (CGA) where the ecological base flow requirements are considered as constraints to water release of reservoir operation when optimizing the 10-day reservoir storage. Furthermore, a number of penalty functions designed for different types of constraints are integrated into reservoir operational objectives to form the fitness function. To validate the applicability of this proposed methodology for reservoir operations, the Shih-Men Reservoir and its downstream water demands are used as a case study. By implementing the proposed CGA in optimizing the operational performance of the Shih-Men Reservoir for the last 20 years, we find this method provides much better performance in terms of a small generalized shortage index (GSI) for human water demands and greater ecological base flows for most of the years than historical operations do. We demonstrate the CGA approach can significantly improve the efficiency and effectiveness of water supply capability to both human and ecological base flow requirements and thus optimize reservoir operations for multiple water users. The CGA can be a powerful tool in searching for the optimal strategy for multi-use reservoir operations in water resources management.
Sequence-Specific Copolymer Compatibilizers designed via a Genetic Algorithm
NASA Astrophysics Data System (ADS)
Meenakshisundaram, Venkatesh; Patra, Tarak; Hung, Jui-Hsiang; Simmons, David
For several decades, block copolymers have been employed as surfactants to reduce interfacial energy for applications from emulsification to surface adhesion. While the simplest approach employs symmetric diblocks, studies have examined asymmetric diblocks, multiblock copolymers, gradient copolymers, and copolymer-grafted nanoparticles. However, there exists no established approach to determining the optimal copolymer compatibilizer sequence for a given application. Here we employ molecular dynamics simulations within a genetic algorithm to identify copolymer surfactant sequences yielding maximum reductions the interfacial energy of model immiscible polymers. The optimal copolymer sequence depends significantly on surfactant concentration. Most surprisingly, at high surface concentrations, where the surfactant achieves the greatest interfacial energy reduction, specific non-periodic sequences are found to significantly outperform any regularly blocky sequence. This emergence of polymer sequence-specificity within a non-sequenced environment adds to a recent body of work suggesting that specific sequence may have the potential to play a greater role in polymer properties than previously understood. We acknowledge the W. M. Keck Foundation for financial support of this research.
A Moving Target Environment for Computer Configurations Using Genetic Algorithms
Crouse, Michael; Fulp, Errin W.
2011-10-31
Moving Target (MT) environments for computer systems provide security through diversity by changing various system properties that are explicitly defined in the computer configuration. Temporal diversity can be achieved by making periodic configuration changes; however in an infrastructure of multiple similarly purposed computers diversity must also be spatial, ensuring multiple computers do not simultaneously share the same configuration and potential vulnerabilities. Given the number of possible changes and their potential interdependencies discovering computer configurations that are secure, functional, and diverse is challenging. This paper describes how a Genetic Algorithm (GA) can be employed to find temporally and spatially diverse secure computer configurations. In the proposed approach a computer configuration is modeled as a chromosome, where an individual configuration setting is a trait or allele. The GA operates by combining multiple chromosomes (configurations) which are tested for feasibility and ranked based on performance which will be measured as resistance to attack. The result of successive iterations of the GA are secure configurations that are diverse due to the crossover and mutation processes. Simulations results will demonstrate this approach can provide at MT environment for a large infrastructure of similarly purposed computers by discovering temporally and spatially diverse secure configurations.
Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling
Not Available
2007-05-01
Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.
Shape: automatic conformation prediction of carbohydrates using a genetic algorithm
2009-01-01
Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses. PMID:20298520
Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm
NASA Astrophysics Data System (ADS)
Gen, Mitsuo; Lin, Lin; Cheng, Runwei
Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.
Scheduling trucks in container terminals using a genetic algorithm
NASA Astrophysics Data System (ADS)
Ng, W. C.; Mak, K. L.; Zhang, Y. X.
2007-01-01
Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
Improvement of unsupervised texture classification based on genetic algorithms
NASA Astrophysics Data System (ADS)
Okumura, Hiroshi; Togami, Yuuki; Arai, Kohei
2004-11-01
At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed; 6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. However, this method has not been automated because it requires not only target image but also the number of categories for classification. In this paper, we describe some improvement for implementation of automated texture classification. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.
Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework
Alicia Hofler, Pavel Evtushenko, Frank Marhauser
2009-09-01
Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.
Application of genetic algorithm on optimization of laser beam shaping.
Tsai, Cheng-Mu; Fang, Yi-Chin; Lin, Chia-Te
2015-06-15
This study proposes a newly developed optimization method for an aspherical lens system employed in a refractive laser beam shaping system, which performs transformations on laser spots such that they are transformed into flat-tops of any size. In this paper, a genetic algorithm (GA) with multipoint search is proposed as the optimization method, together with macro language in optical simulation software, in order to search for ideal and optimized parameters. In comparison to a traditional two-dimensional (2D) computational method, using the one-dimensional (1D) computation for laser beam shaping can search for the optimal solution approximately twice as fast (after experiments). The optimal results show that when the laser spot shrinks from 3 mm to 1.07 mm, 88% uniformity is achieved, and when the laser spot increases from 3 mm to 5.273 mm, 90% uniformity is achieved. The distances between the lenses for both systems described above are even smaller than the thickness for the first lens, enabling us to conclude that our design objectives of extra light and slimness in the system are achieved. PMID:26193566
Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics
Hofler, Alicia; Terzic, Balsa; Kramer, Matthew; Zvezdin, Anton; Morozov, Vasiliy; Roblin, Yves; Lin, Fanglei; Jarvis, Colin
2013-01-01
The genetic algorithm (GA) is a relatively new technique that implements the principles nature uses in biological evolution in order to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing CEBAF facility, the proposed MEIC at Jefferson Lab, and a radio frequency (RF) gun based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, including a newly devised enhancement, which leads to improved convergence to the optimum and make recommendations for future GA developments and accelerator applications.
Applying fuzzy theory and genetic algorithm to interpolate precipitation
NASA Astrophysics Data System (ADS)
Chang, C. L.; Lo, S. L.; Yu, S. L.
2005-11-01
A watershed management program is usually based on the results of watershed modeling. Accurate modeling results are decided by the appropriate parameters and input data. Rainfall is the most important input for watershed modeling. Precipitation characteristics, such as rainfall intensity and duration, usually exhibit significant spatial variation, even within small watersheds. Therefore, properly describing the spatial variation of rainfall is essential for predicting the water movement in a watershed. Varied circumstances require a variety of suitable methods for interpolating and estimating precipitation. In this study, a modified method, combining the inverse distance method and fuzzy theory, was applied to precipitation interpolation. Meanwhile, genetic algorithm (GA) was used to determine the parameters of fuzzy membership functions, which represent the relationship between the location without rainfall records and its surrounding rainfall gauges. The objective in the optimization process is to minimize the estimated error of precipitation. The results show that the estimated error is usually reduced by this method. Particularly, when there are large and irregular elevation differences between the interpolated area and its vicinal rainfall gauging stations, it is important to consider the effect of elevation differences, in addition to the effect of horizontal distances. Reliable modeling results can substantially lower the cost for the watershed management strategy.
Genetic algorithm optimized triply compensated pulses in NMR spectroscopy.
Manu, V S; Veglia, Gianluigi
2015-11-01
Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature's evolutionary processes. The newly designed π and π/2 pulses belong to the 'type A' (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U-(13)C, (15)N NAVL peptide as well as U-(13)C, (15)N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences. PMID:26473327
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.
Optimal vaccination schedule search using genetic algorithm over MPI technology
2012-01-01
Background Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. Methods To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. Results & Conclusions The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented. PMID:23148787
Coil optimization for electromagnetic levitation using a genetic like algorithm
NASA Astrophysics Data System (ADS)
Royer, Z. L.; Tackes, C.; LeSar, R.; Napolitano, R. E.
2013-06-01
The technique of electromagnetic levitation (EML) provides a means for thermally processing an electrically conductive specimen in a containerless manner. For the investigation of metallic liquids and related melting or freezing transformations, the elimination of substrate-induced nucleation affords access to much higher undercooling than otherwise attainable. With heating and levitation both arising from the currents induced by the coil, the performance of any EML system depends on controlling the balance between lifting forces and heating effects, as influenced by the levitation coil geometry. In this work, a genetic algorithm is developed and utilized to optimize the design of electromagnetic levitation coils. The optimization is targeted specifically to reduce the steady-state temperature of the stably levitated metallic specimen. Reductions in temperature of nominally 70 K relative to that obtained with the initial design are achieved through coil optimization, and the results are compared with experiments for aluminum. Additionally, the optimization method is shown to be robust, generating a small range of converged results from a variety of initial starting conditions. While our optimization criterion was set to achieve the lowest possible sample temperature, the method is general and can be used to optimize for other criteria as well.
Orbit determination by genetic algorithm and application to GEO observation
NASA Astrophysics Data System (ADS)
Hinagawa, Hideaki; Yamaoka, Hitoshi; Hanada, Toshiya
2014-02-01
This paper demonstrates an initial orbit determination method that solves the problem by a genetic algorithm using two well-known solutions for the Lambert's problem: universal variable method and Battin method. This paper also suggests an intuitive error evaluation method in terms of rotational angle and orbit shape by separating orbit elements into two groups. As reference orbit, mean orbit elements (original two-lines elements) and osculating orbit elements considering the J2 effect are adopted and compared. Our proposed orbit determination method has been tested with actual optical observations of a geosynchronous spacecraft. It should be noted that this demonstration of the orbit determination is limited to one test case. This observation was conducted during approximately 70 min on 2013/05/15 UT. Our method was compared with the orbit elements propagated by SGP4 using the TLE of the spacecraft. The result indicates that our proposed method had a slightly better performance on estimating orbit shape than Gauss's methods and Escobal's method by 120 km. In addition, the result of the rotational angle is closer to the osculating orbit elements than the mean orbit elements by 0.02°, which supports that the estimated orbit is valid.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
Improved interpretation of satellite altimeter data using genetic algorithms
NASA Technical Reports Server (NTRS)
Messa, Kenneth; Lybanon, Matthew
1992-01-01
Genetic algorithms (GA) are optimization techniques that are based on the mechanics of evolution and natural selection. They take advantage of the power of cumulative selection, in which successive incremental improvements in a solution structure become the basis for continued development. A GA is an iterative procedure that maintains a 'population' of 'organisms' (candidate solutions). Through successive 'generations' (iterations) the population as a whole improves in simulation of Darwin's 'survival of the fittest'. GA's have been shown to be successful where noise significantly reduces the ability of other search techniques to work effectively. Satellite altimetry provides useful information about oceanographic phenomena. It provides rapid global coverage of the oceans and is not as severely hampered by cloud cover as infrared imagery. Despite these and other benefits, several factors lead to significant difficulty in interpretation. The GA approach to the improved interpretation of satellite data involves the representation of the ocean surface model as a string of parameters or coefficients from the model. The GA searches in parallel, a population of such representations (organisms) to obtain the individual that is best suited to 'survive', that is, the fittest as measured with respect to some 'fitness' function. The fittest organism is the one that best represents the ocean surface model with respect to the altimeter data.
Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging
NASA Astrophysics Data System (ADS)
Apolinar Muñoz Rodríguez, J.; Mejía Alanís, Francisco Carlos
2016-07-01
An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.
Chen, Ying-ping; Chen, Chao-Hong
2010-01-01
An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence. PMID:20210600
Du, Tingsong; Hu, Yang; Ke, Xianting
2015-01-01
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA. PMID:26447713
Du, Tingsong; Hu, Yang; Ke, Xianting
2015-01-01
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA. PMID:26447713
Distributed-memory Parallel Algorithms for Matching and Coloring
Catalyurek, Umit; Dobrian, Florin; Gebremedhin, Assefaw H.; Halappanavar, Mahantesh; Pothen, Alex
2011-05-31
Graph matching and coloring constitute two fundamental classes of combinatorial problems having numerous established as well as emerging applications in computational science and engineering, high-performance computing, and informatics. We provide a snapshot of an on-going work on the design and implementation of new highly-scalable distributed-memory parallel algorithms for two prototypical problems from these classes, edge-weighted matching and distance-1 vertex coloring. Graph algorithms in general have low concurrency and poor data locality, making it challenging to achieve scalability on massively parallel machines. We overcome this challenge by employing a variety of techniques, including approximation, speculation and iteration, optimized communication, and randomization, in concert. We present preliminary results on weak and strong scalability studies conducted on an IBM Blue Gene/P machine employing up to tens of thousands of processors. The results show that the algorithms hold strong potential for computing at petascale.
Yu, Bo; Jin, Chunshui; Yao, Shun; Li, Chun; Wang, Hui; Zhou, Feng; Guo, Benyin; Xie, Yao; Liu, Yu; Wang, Liping
2015-09-01
An inversion method based on a genetic algorithm has been developed to control the lateral thickness gradients of a Mo-Si multilayer deposited on curved substrates by planar magnetron sputtering. At first, the sputtering distribution of the target is inversed from coating thickness profiles of flat substrates at different heights. Then, the speed profiles of substrates sweeping across the target are optimized according to the desired coating thickness profiles of the primary and secondary mirrors in a two-bounce projection system. The measured coating thickness profiles show that the non-compensable added figure error is below 0.1 nm rms, and the wavelength uniformity across each mirror surface is within ±0.2% P-V. The inversion method introduced here exhibits its convenience in obtaining the sputtering distribution of the target and efficiency in coating iterations during process development. PMID:26368686
Improving permafrost distribution modelling using feature selection algorithms
NASA Astrophysics Data System (ADS)
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its
A distributed geo-routing algorithm for wireless sensor networks.
Joshi, Gyanendra Prasad; Kim, Sung Won
2009-01-01
Geographic wireless sensor networks use position information for greedy routing. Greedy routing works well in dense networks, whereas in sparse networks it may fail and require a recovery algorithm. Recovery algorithms help the packet to get out of the communication void. However, these algorithms are generally costly for resource constrained position-based wireless sensor networks (WSNs). In this paper, we propose a void avoidance algorithm (VAA), a novel idea based on upgrading virtual distance. VAA allows wireless sensor nodes to remove all stuck nodes by transforming the routing graph and forwarding packets using only greedy routing. In VAA, the stuck node upgrades distance unless it finds a next hop node that is closer to the destination than it is. VAA guarantees packet delivery if there is a topologically valid path. Further, it is completely distributed, immediately responds to node failure or topology changes and does not require planarization of the network. NS-2 is used to evaluate the performance and correctness of VAA and we compare its performance to other protocols. Simulations show our proposed algorithm consumes less energy, has an efficient path and substantially less control overheads. PMID:22408514
Algorithm for systematic peak extraction from atomic pair distribution functions.
Granlund, L; Billinge, S J L; Duxbury, P M
2015-07-01
The study presents an algorithm, ParSCAPE, for model-independent extraction of peak positions and intensities from atomic pair distribution functions (PDFs). It provides a statistically motivated method for determining parsimony of extracted peak models using the information-theoretic Akaike information criterion (AIC) applied to plausible models generated within an iterative framework of clustering and chi-square fitting. All parameters the algorithm uses are in principle known or estimable from experiment, though careful judgment must be applied when estimating the PDF baseline of nanostructured materials. ParSCAPE has been implemented in the Python program SrMise. Algorithm performance is examined on synchrotron X-ray PDFs of 16 bulk crystals and two nanoparticles using AIC-based multimodeling techniques, and particularly the impact of experimental uncertainties on extracted models. It is quite resistant to misidentification of spurious peaks coming from noise and termination effects, even in the absence of a constraining structural model. Structure solution from automatically extracted peaks using the Liga algorithm is demonstrated for 14 crystals and for C60. Special attention is given to the information content of the PDF, theory and practice of the AIC, as well as the algorithm's limitations. PMID:26131896
A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms
Kanwal, Maxinder S; Ramesh, Avinash S; Huang, Lauren A
2013-01-01
Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates. PMID:24627784
A matching algorithm for the distribution of human pancreatic islets
Qian, Dajun; Kaddis, John; Niland, Joyce C.
2011-01-01
The success of human pancreatic islet transplantation in a subset of type 1 diabetic patients has led to an increased demand for this tissue in both clinical and basic research, yet the availability of such preparations is limited and the quality highly variable. Under the current process of islet distribution for basic science experimentation nationwide, specialized laboratories attempt to distribute islets to one or more scientists based on a list of known investigators. This Local Decision Making (LDM) process has been found to be ineffective and suboptimal. To alleviate these problems, a computerized Matching Algorithm for Islet Distribution (MAID) was developed to better match the functional, morphological, and quality characteristics of islet preparations to the criteria desired by basic research laboratories, i.e. requesters. The algorithm searches for an optimal combination of requesters using detailed screening, sorting, and search procedures. When applied to a data set of 68 human islet preparations distributed by the Islet Cell Resource (ICR) Center Consortium, MAID reduced the number of requesters that a) did not receive any islets, and b) received mis-matched shipments. These results suggest that MAID is an improved more efficient approach to the centralized distribution of human islets within a consortium setting. PMID:22199413
A weight based genetic algorithm for selecting views
NASA Astrophysics Data System (ADS)
Talebian, Seyed H.; Kareem, Sameem A.
2013-03-01
Data warehouse is a technology designed for supporting decision making. Data warehouse is made by extracting large amount of data from different operational systems; transforming it to a consistent form and loading it to the central repository. The type of queries in data warehouse environment differs from those in operational systems. In contrast to operational systems, the analytical queries that are issued in data warehouses involve summarization of large volume of data and therefore in normal circumstance take a long time to be answered. On the other hand, the result of these queries must be answered in a short time to enable managers to make decisions as short time as possible. As a result, an essential need in this environment is in improving the performances of queries. One of the most popular methods to do this task is utilizing pre-computed result of queries. In this method, whenever a new query is submitted by the user instead of calculating the query on the fly through a large underlying database, the pre-computed result or views are used to answer the queries. Although, the ideal option would be pre-computing and saving all possible views, but, in practice due to disk space constraint and overhead due to view updates it is not considered as a feasible choice. Therefore, we need to select a subset of possible views to save on disk. The problem of selecting the right subset of views is considered as an important challenge in data warehousing. In this paper we suggest a Weighted Based Genetic Algorithm (WBGA) for solving the view selection problem with two objectives.
Yoshimaru, Eriko S.; Randtke, Edward A.; Pagel, Mark D.; Cárdenas-Rodríguez, Julio
2016-01-01
Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners. PMID:26778301
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.
The potential of genetic algorithms for conceptual design of rotor systems
NASA Technical Reports Server (NTRS)
Crossley, William A.; Wells, Valana L.; Laananen, David H.
1993-01-01
The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.
NASA Astrophysics Data System (ADS)
Yoshimaru, Eriko S.; Randtke, Edward A.; Pagel, Mark D.; Cárdenas-Rodríguez, Julio
2016-02-01
Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners.
Liu, Chun; Kroll, Andreas
2016-01-01
Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems. PMID:27588254
Optimal well locations using genetic algorithm for Tushki Project, Western Desert, Egypt
NASA Astrophysics Data System (ADS)
Khalaf, S.; Gad, M. I.
2014-10-01
Groundwater depletion is one of the most important problems threaten the national projects in Egypt. The optimal distribution of well locations and pumping rates mitigate this problem. In this paper, a trial to mitigate this problem in Tushki National Project, south western desert, Egypt was carried out via delineating the optimal well locations and optimal pumping rates. The methodology of combination between simulation and optimization techniques was applied. A linked simulation-optimization model for obtaining the optimum management of groundwater flow is used in this research. MODFLOW packages are used to simulate the groundwater flow system. This model is integrated with an optimization model OLGA (Optimal well Location using Genetic Algorithm technique) which is based on the genetic algorithm (GA). Two management cases were considered by running the model in Abu Simbel-Tushki area with adopted steady and transit calibrated parameters. The first case (fixed well location) is found that the optimum value of the objective function (maximum pumping rate). In the second case (flexible well location with the moving well option) locations of wells are to be decided by the OLGA model itself within a user defined region of the model grid until the optimal location is reached. Also, the prediction of the future changes in both head and flow were made in steady and transient states.
Double Motor Coordinated Control Based on Hybrid Genetic Algorithm and CMAC
NASA Astrophysics Data System (ADS)
Cao, Shaozhong; Tu, Ji
A novel hybrid cerebellar model articulation controller (CMAC) and online adaptive genetic algorithm (GA) controller is introduced to control two Brushless DC motor (BLDCM) which applied in a biped robot. Genetic Algorithm simulates the random learning among the individuals of a group, and CMAC simulates the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments have been done in MATLAB/SIMULINK. Analysis among GA, hybrid GA-CMAC and CMAC feed-forward control is also given. The results prove that the torque ripple of the coordinated control system is eliminated by using the hybrid GA-CMAC algorithm.
A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database
Mubarak, Misbah; Seol, Seegyoung; Lu, Qiukai; Shephard, Mark S.
2013-01-01
Critical to the scalability of parallel adaptive simulations are parallel control functions including load balancing, reduced inter-process communication and optimal data decomposition. In distributed meshes, many mesh-based applications frequently access neighborhood information for computational purposes which must be transmitted efficiently to avoid parallel performance degradation when the neighbors are on different processors. This article presents a parallel algorithm of creating and deleting data copies, referred to as ghost copies, which localize neighborhood data for computation purposes while minimizing inter-process communication. The key characteristics of the algorithm are: (1) It can create ghost copies of any permissible topological order inmore » a 1D, 2D or 3D mesh based on selected adjacencies. (2) It exploits neighborhood communication patterns during the ghost creation process thus eliminating all-to-all communication. (3) For applications that need neighbors of neighbors, the algorithm can create n number of ghost layers up to a point where the whole partitioned mesh can be ghosted. Strong and weak scaling results are presented for the IBM BG/P and Cray XE6 architectures up to a core count of 32,768 processors. The algorithm also leads to scalable results when used in a parallel super-convergent patch recovery error estimator, an application that frequently accesses neighborhood data to carry out computation.« less
NASA Astrophysics Data System (ADS)
Kitagawa, Wataru; Kimura, Yoshihiro; Takeshita, Takaharu
This paper presents one of the embedding methods for a genetic algorithm (GA) in the three-dimensional finite element method (3-D FEM). We use a shell script to automate the preprocesses of the 3-D FEM and to perform the genetic operation for the GA. In this paper, a surface permanent-magnet synchronous motor (SPMSM) was selected as a simple model for optimizing the shape. The capability of this method was confirmed by decreasing the cogging torque. Moreover, the evaluation of GA was performed by distributing the analytical model to several PCs for parallel processing, and the computing time was thus shortened.
Focusing through a turbid medium by amplitude modulation with genetic algorithm
NASA Astrophysics Data System (ADS)
Dai, Weijia; Peng, Ligen; Shao, Xiaopeng
2014-05-01
Multiple scattering of light in opaque materials such as white paint and human tissue forms a volume speckle field, will greatly reduce the imaging depth and degrade the imaging quality. A novel approach is proposed to focus light through a turbid medium using amplitude modulation with genetic algorithm (GA) from speckle patterns. Compared with phase modulation method, amplitude modulation approach, in which the each element of spatial light modulator (SLM) is either zero or one, is much easier to achieve. Theoretical and experimental results show that, the advantage of GA is more suitable for low the signal to noise ratio (SNR) environments in comparison to the existing amplitude control algorithms such as binary amplitude modulation. The circular Gaussian distribution model and Rayleigh Sommerfeld diffraction theory are employed in our simulations to describe the turbid medium and light propagation between optical devices, respectively. It is demonstrated that the GA technique can achieve a higher overall enhancement, and converge much faster than others, and outperform all algorithms at high noise. Focusing through a turbid medium has potential in the observation of cells and protein molecules in biological tissues and other structures in micro/nano scale.
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.
Genetic algorithms - A new technique for solving the neutron spectrum unfolding problem
NASA Astrophysics Data System (ADS)
Freeman, David W.; Ray Edwards, D.; Bolon, Albert E.
1999-04-01
A new technique utilizing genetic algorithms has been applied to the Bonner sphere neutron spectrum unfolding problem. Genetic algorithms are part of a relatively new field of "evolutionary" solution techniques that mimic living systems with computer-simulated "chromosome" solutions. Solutions mate and mutate to create better solutions. Several benchmark problems, considered representative of radiation protection environments, have been evaluated using the newly developed UMRGA code which implements the genetic algorithm unfolding technique. The results are compared with results from other well-established unfolding codes. The genetic algorithm technique works remarkably well and produces solutions with relatively high spectral qualities. UMRGA appears to be a superior technique in the absence of a priori data - it does not rely on "lucky" guesses of input spectra. Calculated personnel doses associated with the unfolded spectra match benchmark values within a few percent.
NASA Technical Reports Server (NTRS)
Wang, Lui; Valenzuela-Rendon, Manuel
1993-01-01
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.
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.
Distributed Minimal Residual (DMR) method for acceleration of iterative algorithms
NASA Technical Reports Server (NTRS)
Lee, Seungsoo; Dulikravich, George S.
1991-01-01
A new method for enhancing the convergence rate of iterative algorithms for the numerical integration of systems of partial differential equations was developed. It is termed the Distributed Minimal Residual (DMR) method and it is based on general Krylov subspace methods. The DMR method differs from the Krylov subspace methods by the fact that the iterative acceleration factors are different from equation to equation in the system. At the same time, the DMR method can be viewed as an incomplete Newton iteration method. The DMR method was applied to Euler equations of gas dynamics and incompressible Navier-Stokes equations. All numerical test cases were obtained using either explicit four stage Runge-Kutta or Euler implicit time integration. The formulation for the DMR method is general in nature and can be applied to explicit and implicit iterative algorithms for arbitrary systems of partial differential equations.
NASA Astrophysics Data System (ADS)
Que, Dashun; Li, Gang; Yue, Peng
2007-12-01
An adaptive optimization watermarking algorithm based on Genetic Algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital watermarking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.
EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking.
Guan, Boxin; Zhang, Changsheng; Ning, Jiaxu
2016-07-01
Protein-ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein-ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein-ligand docking. We consider four search methods-a tradition genetic algorithm, LGA, SODOCK, and EDGA-and compare their performance in docking of six protein-ligand docking problems. The results show that EDGA is the most stable, reliable, and successful. PMID:26895461
Landscape genetics and the spatial distribution of chronic wasting disease
Blanchong, Julie A.; Samuel, M.D.; Scribner, K.T.; Weckworth, B.V.; Langenberg, J.A.; Filcek, K.B.
2008-01-01
Predicting the spread of wildlife disease is critical for identifying populations at risk, targeting surveillance and designing proactive management programmes. We used a landscape genetics approach to identify landscape features that influenced gene flow and the distribution of chronic wasting disease (CWD) in Wisconsin white-tailed deer. CWD prevalence was negatively correlated with genetic differentiation of study area deer from deer in the area of disease origin (core-area). Genetic differentiation was greatest, and CWD prevalence lowest, in areas separated from the core-area by the Wisconsin River, indicating that this river reduced deer gene flow and probably disease spread. Features of the landscape that influence host dispersal and spatial patterns of disease can be identified based on host spatial genetic structure. Landscape genetics may be used to predict high-risk populations based on their genetic connection to infected populations and to target disease surveillance, control and preventative activities. ?? 2007 The Royal Society.
Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.
2013-01-01
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933
NASA Astrophysics Data System (ADS)
Spinella-Mamo, V.; Paranjape, M.
2009-02-01
Both ferrofluidics and genetic algorithms are relatively new fields. Due to complex physical interactions, ferrofluidic topographies and assemblies have only been solved using finite time step, Lattice Boltzmann, and finite-element methods in very simple magnetic field configurations. In this paper, we show that it is possible (and highly advantageous) to employ genetic algorithms to solve for the fluid topographies, which can be extended to include more complex magnetic fields.
Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Benford, Andrew; Tinker, Michael L.
2004-01-01
The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.
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
Raindrop Size Distribution Observation for GPM/DPR algorithm development
NASA Astrophysics Data System (ADS)
Nakagawa, Katsuhiro; Hanado, Hiroshi; Nishikawa, Masanori; Nakamura, Kenji; Kaneko, Yuki; Kawamura, Seiji; Iwai, Hironori; Minda, Haruya; Oki, Riko
2013-04-01
In order to evaluate and improve the accuracy of rainfall intensity from space-borne radars (TRMM/PR and GPM/DPR), it is important to estimate the rain attenuation, namely the k-Z relationship (k is the specific attenuation, Z is the radar reflectivity) correctly. National Institute of Information and Communications Technology (NICT) developed the mobile precipitation observation system for the dual Ka-band radar field campaign for GPM/DPR algorithm development. The precipitation measurement instruments are installed on the roof of container. The installed instruments for raindrop size distribution (DSD) measurements are 2-dimensional Video disdtrometer (2DVD), Joss-type disdrometer, and Laser Optical disdrometr (Parsival). 2DVD and Persival can measure not only raindrop size distribution but also ice and snow size distribution. Observations using the mobile precipitation observation system were performed in Okinawa Island, in Tsukuba, over the slope of Mt. Fuji, in Nagaoka, and in Sapporo Japan. Using these observed DSD data in the different provinces, the characteristics of DSD itself are analyzed and the k-Z relationship is estimated for evaluation and improvement of the TRMM/PR and GPM/DPR algorithm.
Unfolding neutron spectra obtained from BS-TLD system using genetic algorithm.
Santos, J A L; Silva, E R; Ferreira, T A E; Vilela, E C
2012-12-01
Due to the variability of neutron spectrum within the same environment, it is essential that the spectral distribution as a function of energy should be characterized. The precise information allows radiological quantities establishment related to that spectrum, but it is necessary that a spectrometric system covers a large interval of energy and an unfolding process is appropriate. This paper proposes use of a technique of Artificial Intelligence (AI) called genetic algorithm (GA), which uses bio-inspired mathematical models with the implementation of a specific matrix to unfolding data obtained from a combination of TLDs embedded in a BS system to characterize the neutron spectrum as a function of energy. The results obtained with this method were in accordance with reference spectra, thus enabling this technique to unfold neutron spectra with the BS-TLD system. PMID:22963980
Optimization of high speed pipelining in FPGA-based FIR filter design using genetic algorithm
NASA Astrophysics Data System (ADS)
Meyer-Baese, Uwe; Botella, Guillermo; Romero, David E. T.; Kumm, Martin
2012-06-01
This paper compares FPGA-based full pipelined multiplierless FIR filter design options. Comparison of Distributed Arithmetic (DA), Common Sub-Expression (CSE) sharing and n-dimensional Reduced Adder Graph (RAG-n) multiplierless filter design methods in term of size, speed, and A*T product are provided. Since DA designs are table-based and CSE/RAG-n designs are adder-based, FPGA synthesis design data are used for a realistic comparison. Superior results of a genetic algorithm based optimization of pipeline registers and non-output fundamental coefficients are shown. FIR filters (posted as open source by Kastner et al.) for filters in the length from 6 to 151 coefficients are used.
Designing Integrated Fuzzy Guidance Law for Aerodynamic Homing Missiles Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Omar, Hanafy M.
The Fuzzy logic controller (FLC) is well-known for robustness to parameter variations and ability to reject noise. However, its design requires definition of many parameters. This work proposes a systematic and simple procedure to develop an integrated fuzzy-based guidance law which consists of three FLC. Each is activated in a region of the interception. Another fuzzy-based switching system is introduced to allow smooth transition between these controllers. The parameters of all the fuzzy controllers, which include the distribution of the membership functions and the rules, are obtained simply by observing the function of each controller. Furthermore, these parameters are tuned by genetic algorithms by solving an optimization problem to minimize the interception time, missile acceleration commands, and miss distance. The simulation results show that the proposed procedure can generate a guidance law with satisfactory performance.
A new neutron energy spectrum unfolding code using a two steps genetic algorithm
NASA Astrophysics Data System (ADS)
Shahabinejad, H.; Hosseini, S. A.; Sohrabpour, M.
2016-03-01
A new neutron spectrum unfolding code TGASU (Two-steps Genetic Algorithm Spectrum Unfolding) has been developed to unfold the neutron spectrum from a pulse height distribution which was calculated using the MCNPX-ESUT computational Monte Carlo code. To perform the unfolding process, the response matrices were generated using the MCNPX-ESUT computational code. Both one step (common GA) and two steps GAs have been implemented to unfold the neutron spectra. According to the obtained results, the new two steps GA code results has shown closer match in all energy regions and particularly in the high energy regions. The results of the TGASU code have been compared with those of the standard spectra, LSQR method and GAMCD code. The results of the TGASU code have been demonstrated to be more accurate than that of the existing computational codes for both under-determined and over-determined problems.
Searching good strategies in evolutionary minority game using variable length genetic algorithm
NASA Astrophysics Data System (ADS)
Yang, Wei-Song; Wang, Bing-Hong; Wu, Yi-Lin; Xie, Yan-Bo
2004-08-01
We propose and study a new adaptation minority game for understanding the complex dynamical behavior characterized by agent interactions competing limited resource in many natural and social systems. We compare the strategy of agents in the model to chromosome in biology. In our model, the agents with poor performance during certain time period may modify their strategies via variable length genetic algorithm which consists of cut and splice operator, imitating similar processes in biology. The performances of the agents in our model are calculated for different parameter conditions and different evolution mechanism. It is found that the system may evolve into a much more ideal equilibrium state, which implies much stronger cooperation among agents and much more effective utilization of the social resources. It is also found that the distribution of the strategies held by agents will tend towards a state concentrating upon small m region.
A Multi-Inner-World Genetic Algorithm Using Multiple Heuristics to Optimize Delivery Schedule
NASA Astrophysics Data System (ADS)
Sakurai, Yoshitaka; Onoyama, Takashi; Tsukamoto, Natsuki; Takada, Kouhei; Tsuruta, Setsuo
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires to solve several tens to hundreds cities Traveling Salesman Problems (TSP) (1)(2) within interactive response time, with expert-level accuracy (less than about 3% of error rate). To meet these requirements, a multi-inner-world Genetic Algorithm (Miw-GA) method is developed. This method combines several types of GA's inner worlds. Each world of this method uses a different type of heuristics such as a 2-opt type mutation world and a block (Nearest Insertion) type mutation world. Comparison based on the results of experiments proved the method is superior to others and our previously proposed method.
NASA Astrophysics Data System (ADS)
Zhu, Jiulong; Wang, Shijun
Presently water resource in most watersheds in China is distributed in terms of administrative instructions. This kind of allocation method has many disadvantages and hampers the instructional effect of market mechanism on water allocation. The paper studies South-to-North Water Transfer Project and discusses water allocation of the node lakes along the Project. Firstly, it advanced four assumptions. Secondly, it analyzed constraint conditions of water allocation in terms of present state of water allocation in China. Thirdly, it established a goal model of water allocation and set up a systematic model from the angle of comprehensive profits of water utilization and profits of the node lakes. Fourthly, it discussed calculation method of the model by means of Simulated Annealing Hybrid Genetic Algorithm (SHGA). Finally, it validated the rationality and validity of the model by a simulation testing.
Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed
NASA Technical Reports Server (NTRS)
Taylor, Jaime; Rakoczy, John; Steincamp, James
2003-01-01
Phase retrieval requires calculation of the real-valued phase of the pupil fimction from the image intensity distribution and characteristics of an optical system. Genetic 'algorithms were used to solve two one-dimensional phase retrieval problem. A GA successfully estimated the coefficients of a polynomial expansion of the phase when the number of coefficients was correctly specified. A GA also successfully estimated the multiple p h e s of a segmented optical system analogous to the seven-mirror Systematic Image-Based Optical Alignment (SIBOA) testbed located at NASA s Marshall Space Flight Center. The SIBOA testbed was developed to investigate phase retrieval techniques. Tiphilt and piston motions of the mirrors accomplish phase corrections. A constant phase over each mirror can be achieved by an independent tip/tilt correction: the phase Conection term can then be factored out of the Discrete Fourier Tranform (DFT), greatly reducing computations.
The anatomical distribution of genetic associations.
Wells, Alan; Kopp, Nathan; Xu, Xiaoxiao; O'Brien, David R; Yang, Wei; Nehorai, Arye; Adair-Kirk, Tracy L; Kopan, Raphael; Dougherty, J D
2015-12-15
Deeper understanding of the anatomical intermediaries for disease and other complex genetic traits is essential to understanding mechanisms and developing new interventions. Existing ontology tools provide functional, curated annotations for many genes and can be used to develop mechanistic hypotheses; yet information about the spatial expression of genes may be equally useful in interpreting results and forming novel hypotheses for a trait. Therefore, we developed an approach for statistically testing the relationship between gene expression across the body and sets of candidate genes from across the genome. We validated this tool and tested its utility on three applications. First, we show that the expression of genes in associated loci from GWA studies implicates specific tissues for 57 out of 98 traits. Second, we tested the ability of the tool to identify novel relationships between gene expression and phenotypes. Specifically, we experimentally confirmed an underappreciated prediction highlighted by our tool: that white blood cell count--a quantitative trait of the immune system--is genetically modulated by genes expressed in the skin. Finally, using gene lists derived from exome sequencing data, we show that human genes under selective constraint are disproportionately expressed in nervous system tissues. PMID:26586807
The anatomical distribution of genetic associations
Wells, Alan; Kopp, Nathan; Xu, Xiaoxiao; O'Brien, David R.; Yang, Wei; Nehorai, Arye; Adair-Kirk, Tracy L.; Kopan, Raphael; Dougherty, J. D.
2015-01-01
Deeper understanding of the anatomical intermediaries for disease and other complex genetic traits is essential to understanding mechanisms and developing new interventions. Existing ontology tools provide functional, curated annotations for many genes and can be used to develop mechanistic hypotheses; yet information about the spatial expression of genes may be equally useful in interpreting results and forming novel hypotheses for a trait. Therefore, we developed an approach for statistically testing the relationship between gene expression across the body and sets of candidate genes from across the genome. We validated this tool and tested its utility on three applications. First, we show that the expression of genes in associated loci from GWA studies implicates specific tissues for 57 out of 98 traits. Second, we tested the ability of the tool to identify novel relationships between gene expression and phenotypes. Specifically, we experimentally confirmed an underappreciated prediction highlighted by our tool: that white blood cell count – a quantitative trait of the immune system – is genetically modulated by genes expressed in the skin. Finally, using gene lists derived from exome sequencing data, we show that human genes under selective constraint are disproportionately expressed in nervous system tissues. PMID:26586807
Spatiotemporal dynamics of distributed synthetic genetic circuits
NASA Astrophysics Data System (ADS)
Kanakov, Oleg; Laptyeva, Tetyana; Tsimring, Lev; Ivanchenko, Mikhail
2016-04-01
We propose and study models of two distributed synthetic gene circuits, toggle-switch and oscillator, each split between two cell strains and coupled via quorum-sensing signals. The distributed toggle switch relies on mutual repression of the two strains, and oscillator is comprised of two strains, one of which acts as an activator for another that in turn acts as a repressor. Distributed toggle switch can exhibit mobile fronts, switching the system from the weaker to the stronger spatially homogeneous state. The circuit can also act as a biosensor, with the switching front dynamics determined by the properties of an external signal. Distributed oscillator system displays another biosensor functionality: oscillations emerge once a small amount of one cell strain appears amid the other, present in abundance. Distribution of synthetic gene circuits among multiple strains allows one to reduce crosstalk among different parts of the overall system and also decrease the energetic burden of the synthetic circuit per cell, which may allow for enhanced functionality and viability of engineered cells.
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.
Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping
NASA Astrophysics Data System (ADS)
Balakrishnan, D.; Quan, C.; Tay, C. J.
2013-06-01
The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.
3D Hail Size Distribution Interpolation/Extrapolation Algorithm
NASA Technical Reports Server (NTRS)
Lane, John
2013-01-01
Radar data can usually detect hail; however, it is difficult for present day radar to accurately discriminate between hail and rain. Local ground-based hail sensors are much better at detecting hail against a rain background, and when incorporated with radar data, provide a much better local picture of a severe rain or hail event. The previous disdrometer interpolation/ extrapolation algorithm described a method to interpolate horizontally between multiple ground sensors (a minimum of three) and extrapolate vertically. This work is a modification to that approach that generates a purely extrapolated 3D spatial distribution when using a single sensor.
ERIC Educational Resources Information Center
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm
ERIC Educational Resources Information Center
Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.
2009-01-01
Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…
ERIC Educational Resources Information Center
Chen, Hsinchun
1995-01-01
Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…
A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.
ERIC Educational Resources Information Center
Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind
1999-01-01
Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)
NASA Astrophysics Data System (ADS)
Kanagaraj, G.; Ponnambalam, S. G.; Jawahar, N.; Mukund Nilakantan, J.
2014-10-01
This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.
Combustion distribution control using the extremum seeking algorithm
NASA Astrophysics Data System (ADS)
Marjanovic, A.; Krstic, M.; Djurovic, Z.; Kvascev, G.; Papic, V.
2014-12-01
Quality regulation of the combustion process inside the furnace is the basis of high demands for increasing robustness, safety and efficiency of thermal power plants. The paper considers the possibility of spatial temperature distribution control inside the boiler, based on the correction of distribution of coal over the mills. Such control system ensures the maintenance of the flame focus away from the walls of the boiler, and thus preserves the equipment and reduces the possibility of ash slugging. At the same time, uniform heat dissipation over mills enhances the energy efficiency of the boiler, while reducing the pollution of the system. A constrained multivariable extremum seeking algorithm is proposed as a tool for combustion process optimization with the main objective of centralizing the flame in the furnace. Simulations are conducted on a model corresponding to the 350MW boiler of the Nikola Tesla Power Plant, in Obrenovac, Serbia.
An efficient distributed navigation algorithm for mobile robot
NASA Astrophysics Data System (ADS)
Yang, Xiuping; Liu, Songyan; Liu, Zhen
2008-10-01
An efficient distributed algorithm for mobile nodes of wireless sensor network (WSN) is proposed. A smart mobile robot is designed as the special node of WSN, and the nodes of WSN are deployed in the environment as signposts for the robot to follow. We use the RSSI value between the robot and other static nodes as the input of the navigation control system. The mobile robot state and navigation space are denoted by the RSSI potential field. Navigation directions are computed by using fuzzy logic method. To reduce the communication expense, each node within one hop communication range of robot is a distributed navigation unites. Then the fuzzy logic control centre will collect the control outputs from every beacon nodes and calculate the final outputs for mobile robot based on data fusion. The experimental results confirm that the navigation system based on WSN successfully achieved their assigned tasks.
NASA Astrophysics Data System (ADS)
Wu, Q. H.; Ma, J. T.
1993-09-01
A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.
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.
GASAT: a genetic local search algorithm for the satisfiability problem.
Lardeux, Frédéric; Saubion, Frédéric; Hao, Jin-Kao
2006-01-01
This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results. PMID:16831107
Spread of pedigree versus genetic ancestry in spatially distributed populations.
Kelleher, J; Etheridge, A M; Véber, A; Barton, N H
2016-04-01
Ancestral processes are fundamental to modern population genetics and spatial structure has been the subject of intense interest for many years. Despite this interest, almost nothing is known about the distribution of the locations of pedigree or genetic ancestors. Using both spatially continuous and stepping-stone models, we show that the distribution of pedigree ancestors approaches a travelling wave, for which we develop two alternative approximations. The speed and width of the wave are sensitive to the local details of the model. After a short time, genetic ancestors spread far more slowly than pedigree ancestors, ultimately diffusing out with radius ∼ t rather than spreading at constant speed. In contrast to the wave of pedigree ancestors, the spread of genetic ancestry is insensitive to the local details of the models. PMID:26546979
Modelling and genetic algorithm based optimisation of inverse supply chain
NASA Astrophysics Data System (ADS)
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
Chen, C.; Xia, J.; Liu, J.; Feng, G.
2006-01-01
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant
Optimization of simulation models with GADELO: a multi-population genetic algorithm.
Elketroussi, M; Fan, D P
1994-02-01
In this paper, a new Genetic Algorithm based on the Dynamic Exploration of Local Optima (GADELO) was used to estimate the parameters of the MRD (Micro-population model of Risk-group Dynamics) micro-population model for smoking cessation by minimizing a deviation function between the model's predictions and the smoking cessation data of the Multiple Risk Factor Intervention Trial (MRFIT). The efficiency and accuracy of the GADELO estimations were consistently superior to those obtained using the standard genetic algorithm or the simplex algorithm of Nelder-Mead. PMID:8175209
NASA Astrophysics Data System (ADS)
Bigdeli, Kasra; Hare, Warren; Tesfamariam, Solomon
2012-04-01
Passive dampers can be used to connect two adjacent structures in order to mitigate earthquakes induced pounding damages. Theoretical and experimental studies have confirmed efficiency and applicability of various connecting devices, such as viscous damper, MR damper, etc. However, few papers employed optimization methods to find the optimal mechanical properties of the dampers, and in most papers, dampers are assumed to be uniform. In this study, we optimized the optimal damping coefficients of viscous dampers considering a general case of non-uniform damping coefficients. Since the derivatives of objective function to damping coefficients are not known, to optimize damping coefficients, a heuristic search method, i.e. the genetic algorithm, is employed. Each structure is modeled as a multi degree of freedom dynamic system consisting of lumped-masses, linear springs and dampers. In order to examine dynamic behavior of the structures, simulations in frequency domain are carried out. A pseudo-excitation based on Kanai-Tajimi spectrum is used as ground acceleration. The optimization results show that relaxing the uniform dampers coefficient assumption generates significant improvement in coupling effectiveness. To investigate efficiency of genetic algorithm, solution quality and solution time of genetic algorithm are compared with those of Nelder-Mead algorithm.
Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating
Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.; Taylor, A.J.
1999-04-12
The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a general parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.
NASA Astrophysics Data System (ADS)
Sharifi, Mani; Rezaei Moayed, Reza; Haratizadeh, Sara
2011-09-01
This paper presents two models for redundancy allocation problem (RAP) with cold standby redundancy policy subject to weight and cost constraints. Also, each element of the system can be damaged exponentially. And, damaged elements can be repaired exponentially by hiring some repairmen. The problem is to determine: (1) element type used in the system, (2) number of elements, and (3) number of repairmen. As the models are not solvable by exact solution methods in reasonable CPU time, an efficient genetic algorithm is developed for it. The genetic algorithm (GA) is hybridized with a local search procedure. Also, the algorithm accepts infeasible solutions after penalizing them based on their amounts of infeasibilities. Thereby, by using these two features, an efficient genetic algorithm is obtained.
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.
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.
Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A
2015-06-01
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. PMID:25880524
Wayne F. Boyer; Gurdeep S. Hura
2005-09-01
The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized task orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|Cmax.,
An estimation of distribution algorithm (EDA) variant with QGA for Flowshop scheduling problem
NASA Astrophysics Data System (ADS)
Latif, Muhammad Shahid; Hong, Zhou; Ali, Amir
2014-04-01
In this research article, a hybrid approach is presented which based on well-known meta-heuristics algorithms. This study based on integration of Quantum Genetic Algorithm (QGA) and Estimation of Distribution Algorithm, EDA, (for simplicity we use Q-EDA) for flowshop scheduling, a well-known NP hard Problem, while focusing on the total flow time minimization criterion. A relatively new method has been adopted for the encoding of jobs sequence in flowshop known as angel rotations instead of random keys, so QGA become more efficient. Further, EDA has been integrated to update the population of QGA by making a probability model. This probabilistic model is built and used to generate new candidate solutions which comprised on best individuals, obtained after several repetitions of proposed (Q-EDA) approach. As both heuristics based on probabilistic characteristics, so exhibits excellent learning capability and have minimum chances of being trapped in local optima. The results obtained during this study are presented and compared with contemporary approaches in literature. The current hybrid Q-EDA has implemented on different benchmark problems. The experiments has showed better convergence and results. It is concluded that hybrid Q-EDA algorithm can generally produce better results while implemented for Flowshop Scheduling Problem (FSSP).
Optimization of the transition path of the head hardening with using the genetic algorithms
NASA Astrophysics Data System (ADS)
Wróbel, Joanna; Kulawik, Adam
2016-06-01
An automated method of choice of the transition path of the head hardening in heat treatment process for the plane steel element is proposed in this communication. This method determines the points on the path of moving heat source using the genetic algorithms. The fitness function of the used algorithm is determined on the basis of effective stresses and yield point depending on the phase composition. The path of the hardening tool and also the area of the heat affected zone is determined on the basis of obtained points. A numerical model of thermal phenomena, phase transformations in the solid state and mechanical phenomena for the hardening process is implemented in order to verify the presented method. A finite element method (FEM) was used for solving the heat transfer equation and getting required temperature fields. The moving heat source is modeled with a Gaussian distribution and the water cooling is also included. The macroscopic model based on the analysis of the CCT and CHT diagrams of the medium-carbon steel is used to determine the phase transformations in the solid state. A finite element method is also used for solving the equilibrium equations giving us the stress field. The thermal and structural strains are taken into account in the constitutive relations.
Applying genetic algorithms to the state assignment problem: a case study
NASA Astrophysics Data System (ADS)
Amaral, Jose N.; Tumer, Kagan; Ghosh, Joydeep
1992-08-01
Finding the best state assignment for implementing a synchronous sequential circuit is important for reducing silicon area or chip count in many digital designs. This State Assignment Problem (SAP) belongs to a broader class of combinatorial optimization problems than the well studied traveling salesman problem, which can be formulated as a special case of SAP. The search for a good solution is considerably more involved for the SAP than it is for the traveling salesman problem due to a much larger number of equivalent solutions, and no effective heuristic has been found so far to cater to all types of circuits. In this paper, a matrix representation is used as the genotype for a Generic Algorithm (GA) approach to this problem. A novel selection mechanism is introduced, and suitable genetic operators for crossover and mutation, are constructed. The properties of each of these elements of the GA are discussed and an analysis of parameters that influence the algorithm is given. A canonical form for a solution is defined to significantly reduce the search space and number of local minima. Simulation results for scalable examples show that the GA approach yields results that are comparable to those obtained using competing heuristics. Although a GA does not seem to be the tool of choice for use in a sequential Von-Neumann machine, the results obtained are good enough to encourage further research on distributed processing GA machines that can exploit its intrinsic parallelism.
Calibration of the analogue method for precipitation forecasting by means of genetic algorithms
NASA Astrophysics Data System (ADS)
Horton, P.; Jaboyedoff, M.; Obled, C.
2012-04-01
The analogue downscaling technique allows precipitation forecasting on the basis of the synoptic circulation and humidity variables resulting from a global circulation model (GCM). The method identifies analog days in a long archive of past situations and uses their observed precipitation amount to build the empirical conditional distribution considered as the probabilistic forecast for the target day. The Atmoswing model (Analog Technique MOdel for Statistical Weather forecastING) was developed to calibrate the method and to process real-time forecasting in the Swiss Alps. It is part of the MINERVE project, which aims at reducing the flood peaks of the Rhône River by means of water retention in dams. Such a method is highly non-linear, works with both discrete and continuous variables and has a complex cost surface. Calibration with linear methods such as a simplex concept has been tried, but has led to unsatisfying results. The complexity of the analogue technique gives the user no choice but to use either a step-by-step manual calibration or a global optimizer. While the first option is the commonly used approach, a global optimizer has never been used to fulfill that goal. The classic calibration's main issue is that parameters are not independent and the choices made in the beginning of the calibration procedure have an impact on the final set. We decided to implement the genetic algorithms to achieve the model calibration, and so to avoid subjective choices of initial parameters. Genetic algorithms exist with multiple operators (natural selection, mating selection, chromosomes crossover, and mutation) variations and specific parameterizations. Most used implementations were compared to choose the version resulting in the best calibration with the minimum processing time. Afterwards, the optimizer is used to explore new variable spaces and even to choose the best atmospheric variables, what was not possible in the traditional calibration procedure.
A Single-Lap Joint Adhesive Bonding Optimization Method Using Gradient and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Smeltzer, Stanley S., III; Finckenor, Jeffrey L.
1999-01-01
A natural process for any engineer, scientist, educator, etc. is to seek the most efficient method for accomplishing a given task. In the case of structural design, an area that has a significant impact on the structural efficiency is joint design. Unless the structure is machined from a solid block of material, the individual components which compose the overall structure must be joined together. The method for joining a structure varies depending on the applied loads, material, assembly and disassembly requirements, service life, environment, etc. Using both metallic and fiber reinforced plastic materials limits the user to two methods or a combination of these methods for joining the components into one structure. The first is mechanical fastening and the second is adhesive bonding. Mechanical fastening is by far the most popular joining technique; however, in terms of structural efficiency, adhesive bonding provides a superior joint since the load is distributed uniformly across the joint. The purpose of this paper is to develop a method for optimizing single-lap joint adhesive bonded structures using both gradient and genetic algorithms and comparing the solution process for each method. The goal of the single-lap joint optimization is to find the most efficient structure that meets the imposed requirements while still remaining as lightweight, economical, and reliable as possible. For the single-lap joint, an optimum joint is determined by minimizing the weight of the overall joint based on constraints from adhesive strengths as well as empirically derived rules. The analytical solution of the sin-le-lap joint is determined using the classical Goland-Reissner technique for case 2 type adhesive joints. Joint weight minimization is achieved using a commercially available routine, Design Optimization Tool (DOT), for the gradient solution while an author developed method is used for the genetic algorithm solution. Results illustrate the critical design variables
A Genetic Algorithm Approach to Nonlinear Least Squares Estimation
ERIC Educational Resources Information Center
Olinsky, Alan D.; Quinn, John T.; Mangiameli, Paul M.; Chen, Shaw K.
2004-01-01
A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than…
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.
Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
Jevtić, Aleksandar; Gutiérrez, Álvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. PMID:22346677
Jevtić, Aleksandar; Gutiérrez, Alvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. PMID:22346677
Distribution and Location of Genetic Effects for Dairy Traits
Technology Transfer Automated Retrieval System (TEKTRAN)
Genetic effects for many dairy traits and for total economic merit are fairly evenly distributed across all chromosomes. A high-density scan using 38,416 SNP markers for 5,285 bulls confirmed two previously-known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects...
Distribution and Location of Genetic Effects for Dairy Traits
Technology Transfer Automated Retrieval System (TEKTRAN)
Genetic effects for many dairy traits and for total economic merit are fairly evenly distributed across all chromosomes. A high-density scan using 39,314 SNP markers for 5,285 bulls confirmed two previously-known major genes on BTA 6 and 14 but revealed few other large effects. Markers on BTA 18 had...
A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case
Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing
2014-01-01
This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038
A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
Song, Jiancai; Xue, Guixiang; Kang, Yanan
2016-01-01
In this paper, a novel method for selecting a navigation satellite subset for a global positioning system (GPS) based on a genetic algorithm is presented. This approach is based on minimizing the factors in the geometric dilution of precision (GDOP) using a modified genetic algorithm (MGA) with an elite conservation strategy, adaptive selection, adaptive mutation, and a hybrid genetic algorithm that can select a subset of the satellites represented by specific numbers in the interval (4 ∼ n) while maintaining position accuracy. A comprehensive simulation demonstrates that the MGA-based satellite selection method effectively selects the correct number of optimal satellite subsets using receiver autonomous integrity monitoring (RAIM) or fault detection and exclusion (FDE). This method is more adaptable and flexible for GPS receivers, particularly for those used in handset equipment and mobile phones. PMID:26943638
A high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithm.
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
Song, Jiancai; Xue, Guixiang; Kang, Yanan
2016-01-01
In this paper, a novel method for selecting a navigation satellite subset for a global positioning system (GPS) based on a genetic algorithm is presented. This approach is based on minimizing the factors in the geometric dilution of precision (GDOP) using a modified genetic algorithm (MGA) with an elite conservation strategy, adaptive selection, adaptive mutation, and a hybrid genetic algorithm that can select a subset of the satellites represented by specific numbers in the interval (4 ∼ n) while maintaining position accuracy. A comprehensive simulation demonstrates that the MGA-based satellite selection method effectively selects the correct number of optimal satellite subsets using receiver autonomous integrity monitoring (RAIM) or fault detection and exclusion (FDE). This method is more adaptable and flexible for GPS receivers, particularly for those used in handset equipment and mobile phones. PMID:26943638
Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing
Gong, Dunwei
2014-01-01
The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years. However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved. In this paper, we establish a mathematical model of generating test data for multiple paths coverage. Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model. We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test. The experimental results show that the proposed method can improve the efficiency of generating test data for many paths' coverage significantly. PMID:25691894
NASA Astrophysics Data System (ADS)
Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.
2016-06-01
The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.
Learning to play like a human: case injected genetic algorithms for strategic computer gaming
NASA Astrophysics Data System (ADS)
Louis, Sushil J.; Miles, Chris
2006-05-01
We use case injected genetic algorithms to learn how to competently play computer strategy games that involve long range planning across complex dynamics. Imperfect knowledge presented to players requires them adapt their strategies in order to anticipate opponent moves. We focus on the problem of acquiring knowledge learned from human players, in particular we learn general routing information from a human player in the context of a strike force planning game. By incorporating case injection into a genetic algorithm, we show methods for incorporating general knowledge elicited from human players into future plans. In effect allowing the GA to take important strategic elements from human play and merging those elements into its own strategic thinking. Results show that with an appropriate representation, case injection is effective at biasing the genetic algorithm toward producing plans that contain important strategic elements used by human players.
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.
Algorithm-dependent fault tolerance for distributed computing
P. D. Hough; M. e. Goldsby; E. J. Walsh
2000-02-01
Large-scale distributed systems assembled from commodity parts, like CPlant, have become common tools in the distributed computing world. Because of their size and diversity of parts, these systems are prone to failures. Applications that are being run on these systems have not been equipped to efficiently deal with failures, nor is there vendor support for fault tolerance. Thus, when a failure occurs, the application crashes. While most programmers make use of checkpoints to allow for restarting of their applications, this is cumbersome and incurs substantial overhead. In many cases, there are more efficient and more elegant ways in which to address failures. The goal of this project is to develop a software architecture for the detection of and recovery from faults in a cluster computing environment. The detection phase relies on the latest techniques developed in the fault tolerance community. Recovery is being addressed in an application-dependent manner, thus allowing the programmer to take advantage of algorithmic characteristics to reduce the overhead of fault tolerance. This architecture will allow large-scale applications to be more robust in high-performance computing environments that are comprised of clusters of commodity computers such as CPlant and SMP clusters.
NASA Astrophysics Data System (ADS)
Hashemi-Dezaki, Hamed; Mohammadalizadeh-Shabestary, Masoud; Askarian-Abyaneh, Hossein; Rezaei-Jegarluei, Mohammad
2014-01-01
In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.
Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm
NASA Astrophysics Data System (ADS)
Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi
2012-03-01
This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.
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...
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-01-01
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655
Genetic Algorithm to minimize flowtime in a no-wait flowshop scheduling problem
NASA Astrophysics Data System (ADS)
Chaudhry, Imran A.; Ahmed, Riaz; Munem Khan, Abdul
2014-07-01
No-wait flowshop is an important scheduling environment having application in many industries. This paper addresses a no-wait flowshop scheduling problem, where the objective function is to minimise total flowtime. A Genetic Algorithm (GA) optimization approach implemented in a spreadsheet environment is suggested to solve this important class of problem. The proposed algorithm employs a general purpose genetic algorithm which can be customised with ease to address any objective function without modifying the optimization routine. Performance of the proposed approach is compared with eight previously reported algorithms for two sets of benchmark problems. Experimental analysis shows that the performance of the suggested approach is comparable with earlier approaches in terms of quality of solution.
Zhang Changjiang; Wang Xiaodong
2008-11-06
An efficient typhoon cloud image restoration algorithm is proposed. Having implemented contourlet transform to a typhoon cloud image, noise is reduced in the high sub-bands. Weight median value filter is used to reduce the noise in the contourlet domain. Inverse contourlet transform is done to obtain the de-noising image. In order to enhance the global contrast of the typhoon cloud image, in-complete Beta transform (IBT) is used to determine non-linear gray transform curve so as to enhance global contrast for the de-noising typhoon cloud image. Genetic algorithm is used to obtain the optimal gray transform curve. Information entropy is used as the fitness function of the genetic algorithm. Experimental results show that the new algorithm is able to well enhance the global for the typhoon cloud image while well reducing the noises in the typhoon cloud image.
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-01-01
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
Neural network and genetic algorithm technology in data mining of manufacturing quality information
NASA Astrophysics Data System (ADS)
Song, Limei; Qu, Xing-Hua; Ye, Shenghua
2002-03-01
Data Mining of Manufacturing Quality Information (MQI) is the key technology in Quality Lead Control. Of all the data mining methods, Neural Network and Genetic Algorithm is widely used for their strong advantages, such as non-linear, collateral, veracity etc. But if you singly use them, there will be some limitations preventing your research, such as convergence slowly, searching blindness etc. This paper combines their merits and use Genetic BP Algorithm in Data Mining of MQI. It has been successfully used in the key project of Natural Science Foundation of China (NSFC) - Quality Control and Zero-defect Engineering (Project No. 59735120).
An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hudec, Ján; Gramatová, Elena
2015-07-01
The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm
NASA Astrophysics Data System (ADS)
Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; Khan, Sabbir A.; Krafft, Geoffrey A.; Benesch, Jay; Freyberger, Arne; Ranjan, Desh
2014-10-01
In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.
Optimal placement of tuning masses on truss structures by genetic algorithms
NASA Technical Reports Server (NTRS)
Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.
1993-01-01
Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.
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. PMID:15619928
NASA Astrophysics Data System (ADS)
Gallagher, Kerry; Sambridge, Malcolm; Drijkoningen, Guy
In providing a method for solving non-linear optimization problems Monte Carlo techniques avoid the need for linearization but, in practice, are often prohibitive because of the large number of models that must be considered. A new class of methods known as Genetic Algorithms have recently been devised in the field of Artificial Intelligence. We outline the basic concept of genetic algorithms and discuss three examples. We show that, in locating an optimal model, the new technique is far superior in performance to Monte Carlo techniques in all cases considered. However, Monte Carlo integration is still regarded as an effective method for the subsequent model appraisal.
Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller
NASA Astrophysics Data System (ADS)
Saini, Sanju; Saini, J. S.
2012-11-01
This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.
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. PMID:25807448
Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana
2008-11-06
This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.
Mutual information image registration based on improved bee evolutionary genetic algorithm
NASA Astrophysics Data System (ADS)
Xu, Gang; Tu, Jingzhi
2009-07-01
In recent years, the mutual information is regarded as a more efficient similarity metrics in the image registration. According to the features of mutual information image registration, the Bee Evolution Genetic Algorithm (BEGA) is chosen for optimizing parameters, which imitates swarm mating. Besides, we try our best adaptively set the initial parameters to improve the BEGA. The programming result shows the wonderful precision of the algorithm.
Research on Formation of Microsatellite Communication with Genetic Algorithm
Wu, Guoqiang; Bai, Yuguang; Sun, Zhaowei
2013-01-01
For the formation of three microsatellites which fly in the same orbit and perform three-dimensional solid mapping for terra, this paper proposes an optimizing design method of space circular formation order based on improved generic algorithm and provides an intersatellite direct spread spectrum communication system. The calculating equation of LEO formation flying satellite intersatellite links is guided by the special requirements of formation-flying microsatellite intersatellite links, and the transmitter power is also confirmed throughout the simulation. The method of space circular formation order optimizing design based on improved generic algorithm is given, and it can keep formation order steady for a long time under various absorb impetus. The intersatellite direct spread spectrum communication system is also provided. It can be found that, when the distance is 1 km and the data rate is 1 Mbps, the input wave matches preferably with the output wave. And LDPC code can improve the communication performance. The correct capability of (512, 256) LDPC code is better than (2, 1, 7) convolution code, distinctively. The design system can satisfy the communication requirements of microsatellites. So, the presented method provides a significant theory foundation for formation-flying and intersatellite communication. PMID:24078796
Optimizing chromatic aberration calibration using a novel genetic algorithm
NASA Astrophysics Data System (ADS)
Fang, Yi-Chin; Liu, Tung-Kuan; MacDonald, John; Chou, Jyh-Horng; Wu, Bo-Wen; Tsai, Hsien-Lin; Chang, En-Hao
2006-10-01
Advances in digitalized image optics has increased the importance of chromatic aberration. The axial and lateral chromatic aberrations of an optical lens depends on the choice of optical glass. Based on statistics from glass companies worldwide, more than 300 optical glasses have been developed for commercial purposes. However, the complexity of optical systems makes it extremely difficult to obtain the right solution to eliminate small chromatic aberration. Even the damped least-squares technique, which is a ray-tracing-based method, is limited owing to its inability to identify an enhanced optical system configuration. Alternatively, this study instead attempts to eliminate even negligible axial and lateral colour aberration by using algorithms involving the theories of geometric optics in triplet lens, binary and real encoding, multiple dynamic crossover and random gene mutation techniques.
The study of Genetic Algorithm by Hierarchical Coded for the MMRCPSP
NASA Astrophysics Data System (ADS)
Shi-man, Xie; Chen, Jian-wei; Xuan, Zhao-yan
In order to solve the problem of Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP), this paper suggests Genetic Algorithm (GA) by hierarchical coded. In the first layer, the chromosomes are used to choose the activity sequence. In the second layer, the chromosomes are used to decide the combination of activity modes. The chromosomes produced by the Activities Resource Competition Relation (ARCR) are coded by binary code. That is to say, the subsequent operation will be improved by mature algorithm including selection, crossover and mutation. Finally, programing used PSBLIB standard data shows that this algorithm is feasible.
Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm
Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E
2009-01-01
Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, or retrieving relevant data for the user.
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.
A novel method to design S-box based on chaotic map and genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Yong; Wong, Kwok-Wo; Li, Changbing; Li, Yang
2012-01-01
The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes.
Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence
NASA Astrophysics Data System (ADS)
Enayatifar, Rasul; Abdullah, Abdul Hanan; Isnin, Ismail Fauzi
2014-05-01
The paper studies a recently developed evolutionary-based image encryption algorithm. A novel image encryption algorithm based on a hybrid model of deoxyribonucleic acid (DNA) masking, a genetic algorithm (GA) and a logistic map is proposed. This study uses DNA and logistic map functions to create the number of initial DNA masks and applies GA to determine the best mask for encryption. The significant advantage of this approach is improving the quality of DNA masks to obtain the best mask that is compatible with plain images. The experimental results and computer simulations both confirm that the proposed scheme not only demonstrates excellent encryption but also resists various typical attacks.
Color tongue image segmentation using fuzzy Kohonen networks and genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Aimin; Shen, Lansun; Zhao, Zhongxu
2000-04-01
A Tongue Imaging and Analysis System is being developed to acquire digital color tongue images, and to automatically classify and quantify the tongue characteristics for traditional Chinese medical examinations. An important processing step is to segment the tongue pixels into two categories, the tongue body (no coating) and the coating. In this paper, we present a two-stage clustering algorithm that combines Fuzzy Kohonen Clustering Networks and Genetic Algorithm for the segmentation, of which the major concern is to increase the interclass distance and at the same time decrease the intraclass distance. Experimental results confirm the effectiveness of this algorithm.
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.