Genetic Algorithm In Conjunction Research Articles

GenMin: An enhanced genetic algorithm for global optimization

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 summary Program 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 objective function. The test example given takes only a few seconds to run.

The Role of Fermi Resonance in Formation of Valence Band of Water Raman Scattering

The role of Fermi resonance in formation of valence band of water Raman scattering was investigated. Simultaneous measurement of characteristics of bending and valence bands of water in solutions, KBr, and KCl and using genetic algorithms in conjunction with variation methods allowed increasing accuracy of estimation of Fermi resonance coupling constant and of Fermi resonance contribution into formation of water Raman valence band.

Low-cost method for the estimation of the shape of quadric mirrors and calibration of catadioptric cameras

This paper tackles the problem of quadric mirror estimation for omnidirectional imaging systems, as well as the poses of the camera relative to the mirror and to the world reference system. These estimates are obtained for quadric-shaped mirrors (including elliptic, parabolic, hyperbolic, and spherical mirrors) where the positions of the camera and mirror are unconstrained (fully noncentral configurations). The apparent contour of the mirror can be used to reduce the uncertainty in the estimation. Although it can enhance both the accuracy of the estimation (allowing the method to converge from farther initial configurations) and also its performance, its use is not strictly required. The intrinsic parameters of the camera model (pinhole or orthographic) are assumed to be known, as well as the structure of a calibration object in local coordinates. The method is nonlinear, and we use a genetic algorithm in conjunction with the Nelder-Mead simplex algorithm to minimize the objective function. Experimental results using real data are presented, demonstrating the accuracy of this calibration method, comparing its accuracy with and without the apparent contour, and computing its reprojection error. Because the mirror quadric is estimated, this method can also be used to identify and classify the mirror.

A Dual Band Patch Antenna Design for WLAN and DSRC Applications Based on a Genetic Algorithm Optimization

This paper proposes a dual-band patch antenna design for the applications of wireless local area network (WLAN) and dedicated short-range communications (DSRC). To minimize the antenna size, a shorting plate as well as a shorting pin is used inside the antenna structure. In addition, slots are cut on the patch surface to create dual band operations. In order to accelerate the antenna design procedure and to optimize the antenna performance, we used a program based on an implementation of a genetic algorithm in conjunction with the IE3D simulation software to auto-adjust the geometric parameters of the patch. The program examines the fitness of the design via the antenna's input impedance and bandwidth of operations, and utilizes a natural selection method to generate new values of the parameters. Simulated and measurement results shows distinguished antenna performances while retaining relatively small antenna profile.

Pre-processing methodology for optimizing stereolithography apparatus build performance

The performance of a rapid prototyping technology depends, to a great extent, on the way parts are oriented and packed on the machine's build platform. The present work focuses on stereolithography systems. It describes a pre-processing methodology that ‘automates’ the procedure of finding ‘good’ fabrication orientations and packing arrangements. The method proposed consists of two separate, but interrelated, stages: the orientation and the packing stage. At first, each part is appropriately oriented to achieve better surface quality and either minimal support structure or lower build time or minimal projection area. The second stage considers the projections of the parts on the fabrication platform. The associated 2D bin-packing problem is addressed by a genetic algorithm in conjunction with a new improved placement rule. The performance of the present approach is demonstrated via two sets of case studies, which concern simple nearly orthogonal-shaped parts and representative ‘real-world’ objects/parts.

A genetic based method for optimal reactive power compensation of transmission network

Reactive power compensation is known as an effective tool to overcome the violations of voltage limits in electric power systems. It is also deployed to be an efficient method to minimise transmission losses. In order to maximize the effectiveness of reactive power compensation, VAr sources of proper sizes should be placed at some network buses. In this paper, a new VAr expansion tool has been developed to improve the voltage profile and reduce MW losses in a bulk power network. Keeping the minimum expansion cost and minimum switching of capacitor banks during change of load level of the system are emphasized in the proposed method. This method is based on application of genetic algorithm in conjunction with linear programming. Selection of candidate buses for VAr compensation is performed with a modified weak bus oriented method. Genetic algorithm is effectively employed in this algorithm both for selecting the candidate buses for VAr compensation and for preliminary suggestion of optimal solution. The developed method is then applied to the transmission network of Bakhtar Regional Electric Company (BREC) which supplies three provinces of Iran. The simulation results of this case study are also presented in this paper.

A Compact Espar Antenna with Planar Parasitic Elements on a Dielectric Cylinder

This paper presents a technique for designing a dielectric Electronically Steerable Parasitic Array Radiator (Espar) antenna to achieve miniaturization of the conventional Espar antenna. The antenna's size is reduced by immersing the central active element in a dielectric cylinder, mounting the surrounding planar parasitic elements at the circumference of the cylinder, and decreasing the radius of the ground skirt to that of the parasitic elements. An example of a polycarbonate (∈ r = 2.9 + j0.006) Espar antenna operating at 2.484 GHz is optimised by using a genetic algorithm in conjunction with an FEM-based cost function. The designed antenna generates a half-power beam width of 78° and a main lobe that elevates at an angle of only 5° from the horizontal plane. The designed antenna is also fabricated and measured. Good agreement between the measurement and simulation results is obtained. We reduce the size of the designed Espar antenna to 1/8 the size of its conventional counterpart while achieving a 12° improvement in half-power beam width.

A Genetic Algorithm Approach to Multiple-Response Optimization

Many designed experiments require the simultaneous optimization of multiple responses. A common approach is to use a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors. However, as the problem grows even moderately in either the number of factors or the number of responses, conventional optimization algorithms can fail to find the global optimum. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). This paper proposes and develops a multiple-response solution technique using a GA in conjunction with an unconstrained desirability function. The GA requires that several parameters be determined in order for the algorithm to operate effectively. We perform a robust designed experiment in order to tune the genetic algorithm to perform well regardless of the complexity of the multiple-response optimization problem. The performance of the proposed GA method is evaluated and compared with the performance of the method that combines the desirability with the generalized reduced gradient (GRG) optimization. The evaluation shows that only the proposed GA approach consistently and effectively solves multiple-response problems of varying complexity.

Exact Schema Theory and Markov Chain Models for Genetic Programming and Variable-length Genetic Algorithms with Homologous Crossover

Genetic Programming (GP) homologous crossovers are a group of operators, including GP one-point crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. In this paper we present an exact schema theory for GP and variable-length Genetic Algorithms (GAs) which is applicable to this class of operators. The theory is based on the concepts of GP crossover masks and GP recombination distributions that are generalisations of the corresponding notions used in GA theory and in population genetics, as well as the notions of hyperschema and node reference systems, which are specifically required when dealing with variable size representations. In this paper we also present a Markov chain model for GP and variable-length GAs with homologous crossover. We obtain this result by using the core of Vose's model for GAs in conjunction with the GP schema theory just described. The model is then specialised for the case of GP operating on 0/1 trees: a tree-like generalisation of the concept of binary string. For these, symmetries exist that can be exploited to obtain further simplifications. In the absence of mutation, the Markov chain model presented here generalises Vose's GA model to GP and variable-length GAs. Likewise, our schema theory generalises and refines a variety of previous results in GP and GA theory.

Adaptive Selection of Aircraft Engine Technologies in the Presence of Risk

The objective of this paper is to describe a method for selecting optimal engine technology solution sets while simultaneously accounting for the presence of technology risk. This method uses a genetic algorithm in conjunction with technology identification, evaluation, and selection methods to find optimal combinations of technologies. The unique feature of this method is that the technology evaluation itself is probabilistic in nature. This allows the performance impact and associated risk of each technology to be quantified in terms of a distribution on key engine technology metrics. The resulting method can best be characterized as a concurrent genetic algorithm/Monte Carlo analysis that yields a performance and risk-optimal technology solution set. This solution set is inherently a robust solution because the method will naturally strive to find those technologies representing the best compromise between performance improvement and technology risk. Finally, a practical demonstration of the method and accompanying results is given for a typical commercial aircraft engine technology selection problem.

A Real Coded Genetic Algorithm Approach for Detection of Subsurface Isotropic and Anisotropic Inclusions

An inverse problem is considered to identify the geometry of discontinuities in a conductive material with anisotropic conductivity from Cauchy data measurements taken on the boundary. In this study we propose a real coded genetic algorithm in conjunction with a boundary element method to detect an anisotropic inclusion, such as a circle, by a single boundary measurement. Numerical results are presented for both isotropic and anisotropic inclusions. The genetic algorithms based method developed in this article is found to be a robust, efficient method for detecting the size and location of subsurface inclusions.

Genetic algorithm for input/output selection in MIMO systems based on controllability and observability indices

A time domain optimisation algorithm using a genetic algorithm in conjunction with a linear search scheme has been developed to find the smallest or near-smallest subset of inputs and outputs to control a multi-input-multi-output system. Experimental results have shown that this proposed algorithm has a very fast convergence rate and high computation efficiency.

Frequency selective surface design by integrating optimisation algorithms with fast full wave numerical methods

Previous work on optimisation has primarily focused on the applications of genetic algorithms in conjunction with problem-specific or semi-analytic tools. Recent developments in fast algorithms now offer the possibility of designs and moreover allow for full flexibility in material specification across three dimensions. As an example, the paper combines genetic algorithms with fast hybrid finite-element/boundary-integral simulations to conduct shape optimisation of frequency selective surfaces (FSSs). Optimal shapes for FSS geometries subject to given performance criteria are demonstrated.

Optimum design for compact diversity wire antenna with two highly isolated ports

A compact diversity wire antenna with two highly isolated ports is obtained using a genetic algorithm in conjunction with the moment method. It has much better performance than a standard dual-monopole. Envelope correlation of the two ports is much less than 0.1 and easily meets the requirement for achieving diversity.

Genetic algorithms and finite element analysis in optimization of composite structures

The overall objective of the paper is to describe the concept of using genetic algorithms (GA) procedures in layout optimization of composite structures. The layout optimization is understood in the sense of stacking sequence, shape and size (material, volume) optimization. The attention is focused on the applicability of genetic algorithms in conjunction with the finite element computation of objective functions. Therefore, the problem of appropriate coding and selection procedures is particularly emphasized. Various numerical examples illustrate the discussed problems and show especially the effects of anisotropic material properties on optimization results.

Placement of sensors/actuators on civil structures using genetic algorithms

AbstractThe optimal design and placement of controllers at discrete locations is an important problem that will have impact on the control of civil engineering structures. Though algorithms exist for the placement of sensor/actuator systems on continuous structures, the placement of controllers on discrete civil structures is a very difficult problem. Because of the nature of civil structures, it is not possible to place sensors and actuators at any location in the structure. This usually creates a non‐linear constrained mixed integer problem that can be very difficult to solve. Using genetic algorithms in conjunction with gradient‐based optimization techniques will allow for the simultaneous placement and design of an effective structural control system. The introduction of algorithms based on genetic search procedures should increase the rate of convergence and thus reduce the computational time for solving the difficult control problem. The newly proposed method of simultaneously placing sensors/actuators will be compared to a commonly used method of sensors/actuators placement where sensors/actuators are placed sequentially. The savings in terms of energy requirements and cost will be discussed. Copyright © 2001 John Wiley & Sons, Ltd.

Design of a vehicular antenna for GPS/Iridium using a genetic algorithm

In this paper, we describe a vehicular wire antenna, designed using a genetic algorithm that may be used for both the GPS and Iridium systems. It has right-hand circular polarization, near hemispherical coverage, and operates over the frequency band from 1225 to 1625 MHz. This antenna was simulated using the numerical electromagnetics code (NEC) and then fabricated and tested. The antenna consists of five copper tubing segments connected in series, has an unusually odd shape, and is very inexpensive. It fits in a volume approximately 10 cm/spl times/10 cm/spl times/15 cm, The input voltage standing wave ratio (VSWR) and circular polarization radiation patterns were computed and measured. The VSWR was under 2.2 at the design frequencies of 1225, 1575, and 1625 MHz. The gain varied by less than 12 dB for a 170/spl deg/ sector; it generally fell off near the horizon so the variation was less for 150/spl deg/ and 160/spl deg/ sectors. This new design process, which uses a genetic algorithm in conjunction with an electromagnetics code, produces configurations that are unique and seem to outperform more conventional designs.

Application of combined genetic algorithms with cascade correlation to diagnosis of delayed gastric emptying from electrogastrograms

The current standard method (radioscintigraphy) for the diagnosis of delayed gastric emptying (GE) of a solid meal involves radiation exposure and considerable expense. Based on combining genetic algorithms with the cascade correlation learning architecture, a neural network approach is proposed for the diagnosis of delayed GE from electrogastrograms (EGGs). EGGs were measured by placing surface electrodes on the abdominal skin over the stomach in 152 patients with suspected gastric motility disorders for 30 min in the fasting state and for 2 h after a standard test meal. The GE rate of the stomach was simultaneously monitored after the meal using radioscintigraphy. Five spectral parameters of EGG data in each patient were used as the inputs to a classifier. The classifier was designed by using genetic algorithms in conjunction with the cascade correlation learning architecture. The main advantage of this technique over the back-propagation (BP) for supervised learning is that it can automatically develop the architecture of neural networks to give a suitable network size for a specific problem. The resulted neural network with three hidden units exhibits 83% correct classification for the EGG data, and has comparable performance with the BP network. This study demonstrates the potential of the neural network approach based on combined genetic algorithms with cascade correlation for diagnosis of gastric emptying from the EGG.

Shape Optimization To Perform Prescribed Air Lubrication Using Genetic Algorithm

A shape optimization of magnetic head for magnetic recording device with rotating drum was studied. An optimization algorithm was developed based on genetic algorithms in conjunction with finite element method which analyzed interface phenomena between a magnetic tape and a magnetic head. The head shape was defined by a second order spline function. Three reference points and first order derivatives at a origin point were coded by the genetic algorithm. As a result, an applicability of genetic algorithms to the given optimization problem was verified. Presented as a Society of Tribologists and Lubrication Engineers Paper at the ASME/STLE Tribology Conference in Seattle, Washington, October 1–4, 2000

Optimal design of electric machine using genetic algorithms coupled with direct method

This paper discusses the development of a new optimization algorithm for DC motor design. In principle, the new algorithm utilizes a mixed method that consists of genetic algorithms in conjunction with direct search method. The genetic algorithms are used for locating the global optimum region while the direct search method is used to achieve objective function convergence. In order to validate the effectiveness, the new algorithm has been applied to an actual DC motor. Field and torque characteristics of the DC motor are computed using the finite element method and the principle of virtual work, respectively.