oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Genetic Programming and Genetic Algorithms for Propositions
Nabil M. HEWAHI
Journal of Applied Computer Science & Mathematics , 2012,
Abstract: In this paper we propose a mechanism to discover the compound proposition solutions for a given truth table without knowing the compound propositions that lead to the truth table results. The approach is based on two proposed algorithms, the first is called Producing Formula (PF) algorithm which is based on the genetic programming idea, to find out the compound proposition solutions for the given truth table. The second algorithm is called the Solutions Optimization (SO) algorithm which is based on genetic algorithms idea, to find a list of the optimum compound propositions that can solve the truth table. The obtained list will depend on the solutions obtained from the PF algorithm. Various types of genetic operators have been introduced to obtain the solutions either within the PF algorithm or SO algorithm.
Linear Antenna Array Design with Use of Genetic, Memetic and Tabu Search Optimization Algorithms
Yavuz Cengiz;Hatice Tokat
PIER C , 2008, DOI: 10.2528/PIERC08010205
Abstract: Antenna array design techniques are focused on two main classes: uniformly spaced antenna arrays and the non-uniform spacing case. These include techniques based on mathematical programming, such as constrained programming and non-linear programming. More recently, meta-heuristics approaches have been successful at designing antenna arrays [5]. In this work, this paper presents efficient methods of genetic algorithm (GA), memetic algorithm (MA) and tabu search algorithm (TSA) for the synthesis of linear antenna design. We present three examples of antenna array design to compare the efficiency of the algorithms through simple design to complex design. The GA, TSA and MA has been used to optimize the spacings between the elements of the linear array to produce a radiation pattern with minimum SLL and null placement control.
Pattern Synthesis of Sparse Phased Array Antenna Using Genetic Algorithms  [cached]
Rongcang Han
Modern Applied Science , 2009, DOI: 10.5539/mas.v3n9p91
Abstract: Sparsely sampled irregular arrays and random arrays have been used or proposed in several fields such as radar, sonar, and ultrasound imaging. One method of pattern synthesis for sparse phased array antenna using genetic algorithms is introduced. We start with an introduction to genetic algorithms and then consider the problem of finding the best amplitude layout of elements in sparse arrays. The optimization criteria are then reviewed: creation of beam patterns with low main lobe width and low side lobes.
Design of Adaptive Null Antenna using Genetic Algorithms  [PDF]
Imran Maqsood,I. M.Qurshi
Journal of Applied Sciences , 2002,
Abstract: This paper describes the design of adaptive null antenna and its implementation through Genetic Algorithm. The step-by-step implementation of GA has been demonstrated using a flow chart to determine the complex excitation co-efficient of adaptive null linear array antenna. This algorithm can avoid the local minimal as observed in LMS) and converges towards the global optimum solution.
Antenna Modeling by Infinitesimal Dipoles Using Genetic Algorithms
Taninder S. Sijher;Ahmed A. Kishk
PIER , 2005, DOI: 10.2528/PIER04081801
Abstract: The binary Genetic Algorithm (GA) optimization method is used to simulate antennas from their near-field distribution by a set of infinitesimal dipoles. The infinitesimal dipoles could be of electric and/or magnetic types that produce the near field of the actual antenna and thus the same far field. The method is verified using near fields from known infinitesimal electric and/or magnetic dipoles. Some simple antennas have been simulated by infinitesimal dipoles such as dipole, loop, waveguide, and dielectric resonator antenna. The obtained equivalent dipoles from single frequency measurements are found to be valid for certain frequency band.
Phase-Only Planar Antenna Array Synthesis with Fuzzy Genetic Algorithms  [PDF]
Boufeldja Kadri,Miloud Boussahla,Fethi Tarik Bendimerad
International Journal of Computer Science Issues , 2010,
Abstract: This paper describes a new method for the synthesis of planar antenna arrays using fuzzy genetic algorithms (FGAs) by optimizing phase excitation coefficients to best meet a desired radiation pattern. We present the application of a rigorous optimization technique based on fuzzy genetic algorithms (FGAs), the optimizing algorithm is obtained by adjusting control parameters of a standard version of genetic algorithm (SGAs) using a fuzzy controller (FLC) depending on the best individual fitness and the population diversity measurements (PDM). The presented optimization algorithms were previously checked on specific mathematical test function and show their superior capabilities with respect to the standard version (SGAs). A planar array with rectangular cells using a probe feed is considered. Included example using FGA demonstrates the good agreement between the desired and calculated radiation patterns than those obtained by a SGA.
Phase-Only Planar Antenna Array Synthesis with Fuzzy Genetic Algorithms  [PDF]
Boufeldja Kadri,Miloud Boussahla,Fethi Tarik Bendimerad
Computer Science , 2010,
Abstract: This paper describes a new method for the synthesis of planar antenna arrays using fuzzy genetic algorithms (FGAs) by optimizing phase excitation coefficients to best meet a desired radiation pattern. We present the application of a rigorous optimization technique based on fuzzy genetic algorithms (FGAs), the optimizing algorithm is obtained by adjusting control parameters of a standard version of genetic algorithm (SGAs) using a fuzzy controller (FLC) depending on the best individual fitness and the population diversity measurements (PDM). The presented optimization algorithms were previously checked on specific mathematical test function and show their superior capabilities with respect to the standard version (SGAs). A planar array with rectangular cells using a probe feed is considered. Included example using FGA demonstrates the good agreement between the desired and calculated radiation patterns than those obtained by a SGA.
A Comparison of Genetic Algorithms, Particle Swarm Optimization and the Differential Evolution Method for the Design of Scannable Circular Antenna Arrays
Marco A. Panduro;Carlos A. Brizuela;Luz I. Balderas;Diana A. Acosta
PIER B , 2009, DOI: 10.2528/PIERB09011308
Abstract: A comparison between different modern population based optimization methods applied to the design of scannable circular antenna arrays is presented in this paper. This design of scannable circular arrays considers the optimization of the amplitude and phase excitations across the antenna elements to operate with optimal performance in the whole azimuth plane (360). Simulation results for scannable circular arrays with the amplitude and phase excitation optimized by genetic algorithms, particle swarm optimization and the differential evolution method are provided. Furthermore, in order to set which design case could provide a better performance in terms of the side lobe level and the directivity, a comparative analysis of the performance of the optimized designs with the case of conventional progressive phase excitation is achieved. Simulation results show that differential evolution and particle swarm optimization have similar performances and both of them had better performance compared to genetic algorithms when all algorithms are allowed equal computation time.
Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems  [PDF]
Gabriel A. Archanjo,Fernando J. Von Zuben
Advances in Software Engineering , 2012, DOI: 10.1155/2012/893701
Abstract: Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks. 1. Introduction Information technology (IT) systems have become the basis of process management of today’s successful enterprises. We can find this kind of system in virtually all fields of activities and inside corporations of any size. The intensive adoption of IT systems has promoted the emergence of an entire ensemble of technologies and services to supply a wide range of demands. Similar to what happens in other areas of product development, methodologies, processes, and tools have been enhanced over the years in order to improve the development of software products, which are going to promote increasing productivity and reduced costs. The first methodologies were inspired by principles found in other areas of product development, like manufacturing. However, the dynamic environment involved in software development is fostering a continuous improvement and customization of methodologies to embrace inevitable uncertainties and necessary redefinition of the product specification, resulting in an iterative and evolutionary process [1]. The need for more agile methodologies is promoting the development of enhanced tools and techniques, more notably in the field of code and design reuse. Approaches to automate entire modules of the software development or to support decision on software engineering have been explored. However, the automated generation of computer algorithms still remains restricted to the scientific field. Knowledge discovery and data mining (KDD) applications are associated with many different approaches to extract relevant patterns from datasets, including solutions
Swarm, genetic and evolutionary programming algorithms applied to multiuser detection  [cached]
Fernando Ciriaco,Leonardo Dagui de Oliveira,Taufik Abr?o,Paul Jean Etienne Jeszensky
Semina : Ciências Exatas e Tecnológicas , 2005,
Abstract: In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA) with multiuser detection (MuD) is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD) is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML). The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum) for the algorithm applied to the MuD problem.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.