Genetic Algorithms (genetic + algorithms)

Distribution by Scientific Domains
Distribution within Engineering


Selected Abstracts


Human motion reconstruction from monocular images using genetic algorithms

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 3-4 2004
Jianhui Zhao
Abstract This paper proposed an optimization approach for human motion recovery from the un-calibrated monocular images containing unlimited human movements. A 3D skeleton human model based on anatomy knowledge is employed with encoded biomechanical constraints for the joints. Energy Function is defined to represent the deviations between projection features and extracted image features. Reconstruction procedure is developed to adjust joints and segments of the human body into their proper positions. Genetic Algorithms are adopted to find the optimal solution effectively in the high dimensional parameter space by simultaneously considering all the parameters of the human model. The experimental results are analysed by Deviation Penalty. Copyright 2004 John Wiley & Sons, Ltd. [source]


Integrating Messy Genetic Algorithms and Simulation to Optimize Resource Utilization

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2009
Tao-ming Cheng
Various resource distribution modeling scenarios were tested in simulation to determine their system performances. MGA operations were then applied in the selection of the best resource utilization schemes based on those performances. A case study showed that this new modeling mechanism, along with the implemented computer program, could not only ease the process of developing optimal resource utilization, but could also improve the system performance of the simulation model. [source]


Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck Repairs

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2006
Hatem Elbehairy
These decisions, however, represent complex optimization problems that traditional optimization techniques are often unable to solve. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of network-level and project-level decisions. Two evolutionary-based optimization techniques that are capable of handling large-size problems, namely Genetic Algorithms and Shuffled Frog Leaping, are then applied on the model to optimize maintenance and repair decisions. Results of both techniques are compared on case study problems with different numbers of bridges. Based on the results, the benefits of the bridge deck management system are illustrated along with various strategies to improve optimization performance. [source]


Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2004
H. R. Varia
A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods. [source]


Genetic Algorithms for Optimal Urban Transit Network Design

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2003
Partha Chakroborty
This article attempts to highlight the effectiveness of genetic algorithm (GA),based procedures in solving the urban transit network design problem (UTNDP). The article analyzes why traditional methods have problems in solving the UTNDP. The article also suggests procedures to alleviate these problems using GA,based optimization technique. The thrust of the article is three,fold: (1) to show the effectiveness of GAs in solving the UTNDP, (2) to identify features of the UTNDP that make it a difficult problem for traditional techniques, and (3) to suggest directions, through the presentation of GA,based methodologies for the UTNDP, for the development of GA,based procedures for solving other optimization problems having features similar to the UTNDP. [source]


Using GIS, Genetic Algorithms, and Visualization in Highway Development

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2001
Manoj K. Jha
A model for highway development is presented, which uses geographic information systems (GIS), genetic algorithms (GA), and computer visualization (CV). GIS serves as a repository of geographic information and enables spatial manipulations and database management. GAs are used to optimize highway alignments in a complex search space. CV is a technique used to convey the characteristics of alternative solutions, which can be the basis of decisions. The proposed model implements GIS and GA to find an optimized alignment based on the minimization of highway costs. CV is implemented to investigate the effects of intangible parameters, such as unusual land and environmental characteristics not considered in optimization. Constrained optimization using GAs may be performed at subsequent stages if necessary using feedback received from CVs. Implementation of the model in a real highway project from Maryland indicates that integration of GIS, GAs, and CV greatly enhances the highway development process. [source]


Preliminary Highway Design with Genetic Algorithms and Geographic Information Systems

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2000
Jyh-Cherng Jong
A method that integrates geographic information systems (GIS) with genetic algorithms (GAs) for optimizing horizontal highway alignments between two given end points is presented in this article. The proposed approach can be used to optimize alignments in highly irregular geographic spaces. The resulting alignments are smooth and satisfy minimum-radius constraints, as required by highway design standards. The objective function in the proposed model considers land-acquisition cost, environmental impacts such as wetlands and flood plains, length-dependent costs (which are proportional to the alignment length), and user costs. A numerical example based on a real map is employed to demonstrate application of the proposed model to the preliminary design of horizontal alignments. [source]


Development of an Expert System Shell Based on Genetic Algorithms for the Selection of the Energy Best Available Technologies and their Optimal Operating Conditions for the Process Industry

EXPERT SYSTEMS, Issue 3 2001
D.A. Manolas
The development of genetic algorithms started almost three decades ago in an attempt to imitate the mechanics of natural systems. Since their inception, they have been applied successfully as optimization methods, and as expert systems, in many diverse applications. In this paper, a genetic-algorithm-based expert system shell is presented that, when combined with a proper database comprising the available energy-saving technologies for the process industry, is able to perform the following tasks: (a) identify the best available technologies (BATs) among the available ones for a given process industry, and (b) calculate their optimal design parameters in such a way that they comply with the energy requirements of the process. By the term BAT is meant the available energy-saving technology, among the existing ones in the market, that is the best for the case. [source]


Combining the Filtered Evaluation Function With Coevolutionary Shared Niching in Genetic Algorithms

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 3 2001
Keiji Suzuki
In this paper, we propose the combination of filtered evaluation and coevolutionary shared niching (CSN) for extending the search ability of genetic algorithms (GA). The proposed scheme can overcome the problems of the filtering GA (FGA) and the CSN. The successful optimization ability of the FGA is supported by the filtered evaluation method that can modify the landscape for escaping local optima. However, the problem of the FGA is the relatively high cost to maintain the filter. The CSN can autonomously maintain the shared distance using the coevolution between two populations (called customers and businessmen). However, the escaping ability from local optima of the CSN is still insufficient. Therefore, the combination of the filtered evaluation and the CSN is proposed, to reduce the cost of the FGA filter. The effectiveness of the proposed scheme is confirmed through test problems. [source]


Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques

JOURNAL OF CHEMOMETRICS, Issue 8 2008
Davide Ballabio
Abstract Different classification methods (Partial Least Squares Discriminant Analysis, Extended Canonical Variates Analysis and Linear Discriminant Analysis), in combination with variable selection approaches (Forward Selection and Genetic Algorithms), were compared, evaluating their capabilities in the geographical discrimination of wine samples. Sixty-two samples were analysed by means of dynamic headspace gas chromatography mass spectrometry (HS-GC-MS) and the entire chromatographic profile was considered to build the dataset. Since variable selection techniques pose a risk of overfitting when a large number of variables is used, a method for coupling data dimension reduction and variable selection was proposed. This approach compresses windows of the original data by retaining only significant components of local Principal Component Analysis models. The subsequent variable selection is then performed on these locally derived score variables. The results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire chromatographic profile, with the exception of Extended Canonical Variates Analysis, which gave acceptable models in both cases. Copyright 2008 John Wiley & Sons, Ltd. [source]


Optimization of the Viability of Probiotics in a New Fermented Milk Drink by the Genetic Algorithms for Response Surface Modeling

JOURNAL OF FOOD SCIENCE, Issue 2 2003
M.-J. Chen
ABSTRACT: Calcium gluconate (0.0 to 0.5%), sodium gluconate (0.0 to 1.0%), and N-acetylglucosamine (0.0 to 1.0%) were added to skim milk to retain the viability of Lactobacillus acidophilus and Bifidobacterium longum. To carry out response surface modeling, the regression method was performed on experimental results to build mathematical models. The models were then formulated as an objective function in an optimization problem that was consequently optimized using a genetic algorithm approach to obtain the maximum viability of the probiotics. The genetic algorithms (GAs) were examined to search for the optimal value. The results indicated that GAs were very effective for optimizing the activity of probiotic cultures. [source]


A universal index formula suitable to multiparameter water quality evaluation

NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS, Issue 3 2004
Lihong Peng
Abstract Based on hypothesis of "base value" of water quality parameter, a universal index suitable to multi-parameter water quality evaluation was presented, as the monitoring values of parameters in the water quality index formula in logarithm function form were replaced by their relative values, and optimizing the parameters of different indices in the formula was carried out by Genetic Algorithms. Each index of water quality can be weighted into comprehensive index by compromise active function. The correctitude of formula was verified by using this method to asses the water quality states of many spots. The formula has shown its simplicity of calculation, practicability, generality, comparability and objectivity. 2004 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 20: 368,373, 2004 [source]


Reactive Flow Model Parameter Estimation Using Genetic Algorithms

PROPELLANTS, EXPLOSIVES, PYROTECHNICS, Issue 3 2010
Jose Baranda, Ribeiro
Abstract An original real-coded genetic algorithm methodology that has been developed for the estimation of the parameters of the Tarver reactive flow model of shock initiation and detonation of heterogeneous solid explosives is described in detail. This methodology allows, in a single optimisation procedure and without the need for a starting solution, to search for the 15 parameters of the reaction rate law of the reactive flow model that fit the numerical results to the experimental ones. The developed methodology was applied and tested with an experimental situation, described in detail in the literature, involving the acceleration of a tantalum metal plate by an LX-17 explosive charge. The obtained parameters allow a very good description of the experimental results and are close to the ones originally used by Tarver and co-authors in their simulation of the phenomenon. [source]


ChemInform Abstract: Selective Hydrogen Oxidation Catalysts via Genetic Algorithms.

CHEMINFORM, Issue 3 2009
Jurriaan Beckers
Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a "Full Text" option. The original article is trackable via the "References" option. [source]


Handwritten Thai Character Recognition Using Fourier Descriptors and Genetic Neural Networks

COMPUTATIONAL INTELLIGENCE, Issue 3 2002
Pisit Phokharatkul
This article presents a method to solve the rotated and scaling character recognition problem using Fourier descriptors and genetic neural networks. The contours of character image are extracted and separated between the outer contour and inner or loop contours. The loop contours are a special characteristic of Thai characters, called the head of the character. The special features of Thai characters (loop contours) are used at the rough classification stage, and Fourier descriptors with genetic neural networks are used at the fine classification stage. The Fourier descriptors detect the outer contour of a character and it is fed to network. These features are recognized by a multilayer neural network. Genetic algorithms (GAs) are utilized to help compute the weights of the neural network optimally and reduce uncertain states in the neural networks output. Experimental results have shown that the combination of the Fourier descriptors with genetic neural networks, loop features, and local curvature charateristics of similar characters are powerful tools for successfully classifying Thai characters. The recognition rate by this method is 99.12% for 1200 examples of handwritten Thai words (a total of 13,500 characters) written by 60 persons. [source]


Using parallelization and hardware concurrency to improve the performance of a genetic algorithm

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 4 2007
Vijay Tirumalai
Abstract Genetic algorithms (GAs) are powerful tools for solving many problems requiring the search of a solution space having both local and global optima. The main drawback for GAs is the long execution time normally required for convergence to a solution. This paper discusses three different techniques that can be applied to GAs to improve overall execution time. A serial software implementation of a GA designed to solve a task scheduling problem is used as the basis for this research. The execution time of this implementation is then improved by exploiting the natural parallelism present in the algorithm using a multiprocessor. Additional performance improvements are provided by implementing the original serial software GA in dedicated reconfigurable hardware using a pipelined architecture. Finally, an advanced hardware implementation is presented in which both pipelining and duplicated hardware modules are used to provide additional concurrency leading to further performance improvements. Copyright 2006 John Wiley & Sons, Ltd. [source]


Genetic algorithms, by K. F. Man, K. S. Tang and S. Kwong, Springer, Berlin, ISBN 1-85233-072-4

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2005
D. Saez
No abstract is available for this article. [source]


Neural network approach to firm grip in the presence of small slips

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 6 2001
A. M. Al-Fahed Nuseirat
This paper presents a two stage method for constructing a firm grip that can tolerate small slips of the fingertips. The fingers are assumed to be of frictionless contact type. The first stage was to formulate the interaction in the gripper,object system as a linear complementarity problem (LCP). Then it was solved using a special neural network to find minimal fingers forces. The second stage was to use the obtained results in the first stage as a static mapping in training another neural network. The second neural network training included emulating the slips by random noise in the form of changes in the positions of the contact points relative to the reference coordinate system. This noisy training increased robustness against unexpected changes in fingers positions. Genetic algorithms were used in training the second neural network as global optimization techniques. The resulting neural network is a robust, reliable, and stable controller for rigid bodies that can be handled by a robot gripper. 2001 John Wiley & Sons, Inc. [source]


Optimization of integrated circuits placement for electric field reduction inside telecommunications equipment using Monte Carlo simulation and parallel recombinative simulated annealing

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 12 2007
Sotirios K. Goudos
Abstract This article presents a novel approach to the modeling and reduction of electromagnetic interference (EMI) caused by radiated emissions of integrated circuits (ICs) inside rectangular metallic enclosures of telecommunications devices. This type of analysis applies for several types of modern telecommunications equipment found in high-speed networks as well as in mobile communications. A generic model of such a device is created. The ICs are modeled as small electric dipoles and their interaction with the enclosure walls is studied by using the dyadic Green's functions. The electric field on the enclosure walls is computed and its reduction is studied as optimization problem using evolutionary algorithms. Two algorithms are employed: Genetic algorithms (GAs) and parallel recombinative simulated annealing (PRSA). PRSA is a hybrid evolutionary strategy that inherits properties from both GAs and simulated annealing. Monte Carlo simulation is subsequently applied to the optimization results to derive the electric field on the metallic walls and also to perform a worst-case analysis. The applications of the above approach in early PCB design process are discussed. 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 3049,3055, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22893 [source]


Probing genetic algorithms for feature selection in comprehensive metabolic profiling approach

RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 8 2008
Wei Zou
Six different clones of 1-year-old loblolly pine (Pinus taeda L.) seedlings grown under standardized conditions in a green house were used for sample preparation and further analysis. Three independent and complementary analytical techniques for metabolic profiling were applied in the present study: hydrophilic interaction chromatography (HILIC-LC/ESI-MS), reversed-phase liquid chromatography (RP-LC/ESI-MS), and gas chromatography all coupled to mass spectrometry (GC/TOF-MS). Unsupervised methods, such as principle component analysis (PCA) and clustering, and supervised methods, such as classification, were used for data mining. Genetic algorithms (GA), a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative classifiers. From more than 2000 peaks found in total, small subsets were selected by GA as highly potential classifiers allowing discrimination among six investigated genotypes. Annotated GC/TOF-MS data allowed the generation of a small subset of identified metabolites. LC/ESI-MS data and small subsets require further annotation. The present study demonstrated that combination of comprehensive metabolic profiling and advanced data mining techniques provides a powerful metabolomic approach for biomarker discovery among small molecules. Utilizing GA for feature selection allowed the generation of small subsets of potent classifiers. Copyright 2008 John Wiley & Sons, Ltd. [source]


Human motion reconstruction from monocular images using genetic algorithms

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 3-4 2004
Jianhui Zhao
Abstract This paper proposed an optimization approach for human motion recovery from the un-calibrated monocular images containing unlimited human movements. A 3D skeleton human model based on anatomy knowledge is employed with encoded biomechanical constraints for the joints. Energy Function is defined to represent the deviations between projection features and extracted image features. Reconstruction procedure is developed to adjust joints and segments of the human body into their proper positions. Genetic Algorithms are adopted to find the optimal solution effectively in the high dimensional parameter space by simultaneously considering all the parameters of the human model. The experimental results are analysed by Deviation Penalty. Copyright 2004 John Wiley & Sons, Ltd. [source]


Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2004
H. R. Varia
A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods. [source]


Meeting Real,Time Traffic Flow Forecasting Requirements with Imprecise Computations

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2003
Brian L. Smith
This article explores the ability of imprecise computations to address real,time computational requirements in infrastructure control and management systems. The research in this area focuses on the development of nonparametric regression as a means to forecast traffic flow rates for transportation management systems. Nonparametric regression is a forecasting technique based on nearest neighbor searching, in which forecasts are derived from past observations that are similar to current conditions. A key concern regarding nonparametric regression is the significant time required to search for nearest neighbors in large databases. The results presented in this article indicate that approximate nearest neighbors, which are imprecise computations as applied to nonparametric regression, may be used to adequately speed the execution time of nonparametric regression, with acceptable degradations in forecast accuracy. The article concludes with a demonstration of the use of genetic algorithms as a design aid for real,time algorithms employing imprecise computations. [source]


Using GIS, Genetic Algorithms, and Visualization in Highway Development

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2001
Manoj K. Jha
A model for highway development is presented, which uses geographic information systems (GIS), genetic algorithms (GA), and computer visualization (CV). GIS serves as a repository of geographic information and enables spatial manipulations and database management. GAs are used to optimize highway alignments in a complex search space. CV is a technique used to convey the characteristics of alternative solutions, which can be the basis of decisions. The proposed model implements GIS and GA to find an optimized alignment based on the minimization of highway costs. CV is implemented to investigate the effects of intangible parameters, such as unusual land and environmental characteristics not considered in optimization. Constrained optimization using GAs may be performed at subsequent stages if necessary using feedback received from CVs. Implementation of the model in a real highway project from Maryland indicates that integration of GIS, GAs, and CV greatly enhances the highway development process. [source]


Three-Dimensional Optimization of Urban Drainage Systems

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2000
A. Freire Diogo
A global mathematical model for simultaneously obtaining the optimal layout and design of urban drainage systems for foul sewage and stormwater is presented. The model can handle every kind of network, including parallel storm and foul sewers. It selects the optimal location for pumping systems and outfalls or wastewater treatment plants (defining the natural and artificial drainage basins), and it allows the presence of special structures and existing subsystems for optimal remodeling or expansion. It is possible to identify two basic optimization levels: in the first level, the generation and transformation of general layouts (consisting of forests of trees) until a convergence criterion is reached, and in the second level, the design and evaluation of each forest. The global strategy adopted combines and develops a sequence of optimal design and plan layout subproblems. Dynamic programming is used as a very powerful technique, alongside simulated annealing and genetic algorithms, in this discrete combinatorial optimization problem of huge dimension. [source]


Preliminary Highway Design with Genetic Algorithms and Geographic Information Systems

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2000
Jyh-Cherng Jong
A method that integrates geographic information systems (GIS) with genetic algorithms (GAs) for optimizing horizontal highway alignments between two given end points is presented in this article. The proposed approach can be used to optimize alignments in highly irregular geographic spaces. The resulting alignments are smooth and satisfy minimum-radius constraints, as required by highway design standards. The objective function in the proposed model considers land-acquisition cost, environmental impacts such as wetlands and flood plains, length-dependent costs (which are proportional to the alignment length), and user costs. A numerical example based on a real map is employed to demonstrate application of the proposed model to the preliminary design of horizontal alignments. [source]


Applying fuzzy logic and genetic algorithms to enhance the efficacy of the PID controller in buffer overflow elimination for better channel response timeliness over the Internet

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 7 2006
Wilfred W. K. Lin
Abstract In this paper two novel intelligent buffer overflow controllers: the fuzzy logic controller (FLC) and the genetic algorithm controller (GAC) are proposed. In the FLC the extant algorithmic PID controller (PIDC) model, which combines the proportional (P), derivative (D) and integral (I) control elements, is augmented with fuzzy logic for higher control precision. The fuzzy logic divides the PIDC control domain into finer control regions. Every region is then defined either by a fuzzy rule or a ,don't care' state. The GAC combines the PIDC model with the genetic algorithm, which manipulates the parametric values of the PIDC as genes in a chromosome. The FLC and GAC operations are based on the objective function . The principle is that the controller should adaptively maintain the safety margin around the chosen reference point (represent by the ,0' of ) at runtime. The preliminary experimental results for the FLC and GAC prototypes indicate that they are both more effective and precise than the PIDC. After repeated timing analyses with the Intel's VTune Performer Analyzer, it was confirmed that the FLC can better support real-time computing than the GAC because of its shorter execution time and faster convergence without any buffer overflow. Copyright 2005 John Wiley & Sons, Ltd. [source]


Output-only structural identification in time domain: Numerical and experimental studies

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 4 2008
M. J. Perry
Abstract By identifying changes in stiffness parameters, structural damage can be detected and monitored. Although considerable progress has been made in this research area, many challenges remain in achieving robust structural identification based on incomplete and noisy measurement signals. The identification task is made even more difficult if measurement of input force is to be eliminated. To this end, an output-only structural identification strategy is proposed to identify unknown stiffness and damping parameters. A non-classical approach based on genetic algorithms (GAs) is adopted. The proposed strategy makes use of the recently developed GA-based method of search space reduction, which has shown to be able to accurately and reliably identify structural parameters from measured input and output signals. By modifying the numerical integration scheme, input can be computed as the parameter identification task is in progress, thereby eliminating the need to measure forces. Numerical and experimental results demonstrate the power of the strategy in accurate and efficient identification of structural parameters and damage using only incomplete acceleration measurements. Copyright 2007 John Wiley & Sons, Ltd. [source]


Evolutionary aseismic design and retrofit of structures with passive energy dissipation

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 13 2005
G. F. Dargush
Abstract A new computational framework is developed for the design and retrofit of building structures by considering aseismic design as a complex adaptive process. For the initial phase of the development within this framework, genetic algorithms are employed for the discrete optimization of passively damped structural systems. The passive elements may include metallic plate dampers, viscous fluid dampers and viscoelastic solid dampers. The primary objective is to determine robust designs, including both the non-linearity of the structural system and the uncertainty of the seismic environment. Within the present paper, this computational design approach is applied to a series of model problems, involving sizing and placement of passive dampers for energy dissipation. In order to facilitate our investigations and provide a baseline for further study, we introduce several simplifications for these initial examples. In particular, we employ deterministic lumped parameter structural models, memoryless fitness function definitions and hypothetical seismic environments. Despite these restrictions, some interesting results are obtained from the simulations and we are able to gain an understanding of the potential for the proposed evolutionary aseismic design methodology. Copyright 2005 John Wiley & Sons, Ltd. [source]


A new genetic algorithm with diploid chromosomes using probability decoding for adaptation to various environments

ELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 8 2010
Manabu Kominami
Abstract This paper proposes a new diploid operation technique using probability for function optimization in nonstationary environments and describes a feature of diploid genetic algorithms (GAs). The advantage of the technique over previous diploid GAs is that one genotype is transformed into many phenotypes based on probability. This transformation is not made at random. It has a certain range of probabilities. Each individual has a range. The range allows adaptation to various environments. The technique allows genes to give a probabilistic representation of dominance, and can maintain the diversity of individuals. The experimental results show that the technique can adapt to severe environmental changes where previous diploid GAs cannot adapt. This paper shows that the technique can find optimum solutions with high probability and that the distribution of individuals changes when the environment changes. In addition, by comparing the proposed diploid GA with a haploid GA whose chromosome is twice the length, the features of the diploid are described. 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 38,46, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10097 [source]