Optimum design of a probe fed dual frequency patch antenna using genetic algorithm

Abstract: Recent research has concentrated on different designs in order to increase the bandwidth of patch antennas and thus improve functionality of wireless communication systems. An alternative approach as shown in this paper is to design a matched probe fed rectangular patch antenna which can operate at both dual frequency (1.9 GHz and 2.4 GHz) and dual polarisation. In this design there are four variables, the two dimensions of the rectangular patch, ‘a ’ and ‘b ’ and position of the probe feed ‘Xp ’ and ‘YP’. As there is not a unique solution Genetic Algorithm (GA) was applied using two objective functions for the return loss at each frequency. The antenna was then modelled using AWR software and the predicted and practical results are shown to be in good agreement. Key Words: Genetic algorithm (GA), dual frequency, dual polarisation, probe fed patch antenn

Genetic Algorithm Assisted Error Probability Optimisation for Beamforming

A novel Genetic Algorithm (GA) assisted direct error probability optimisation technique is proposed for adaptive beamforming, which reduces the achievable error probability by nearly two orders of magnitude at a signal-to-noise ratio of 10dB in the investigated scenario in comparison to the minimum mean-squared error beamforming benchmarker

Extracting Boolean rules from CA patterns

A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois

An evolutionary approach to the identification of Cellular Automata based on partial observations

In this paper we consider the identification problem of Cellular Automata (CAs). The problem is defined and solved in the context of partial observations with time gaps of unknown length, i.e. pre-recorded, partial configurations of the system at certain, unknown time steps. A solution method based on a modified variant of a Genetic Algorithm (GA) is proposed and illustrated with brief experimental results.Comment: IEEE CEC 201

INTRUSION DETECTION SYSTEM (IDS) USING ROUGH SET (RS) AND GENETIC ALGORITHM (GA)

ABSTRAKSI: Fungsi utama dari IDS (Intrusion Detection System) adalah melindungi system, menganalisa, dan memprediksi kebiasaan user. Kebiasaan ini yang digunakan untuk melihat aktifitas normal atau aktifitas intrusi. IDS telah dikembangkan, akan tetapi alert messages yang tinggi membuat managers/system administrator memelihara network tidak efektif. Di thesis ini, rough set dan genetic algorithm digunakan untuk mendeteksi intrusi. Rough set digunakan untuk data preprocessing lebih tepatnya mereduksi dimensi data. Selanjutnya di gunakan pada genetic algorithm untuk dilakukan klasifikasi pada training dan testing data. GA menghasilkan accuracy rate, false positive rate yang lebih baik jika dibandingkan dengan Chen et al dan GA+RST, akan tetapi GA+RST menghasilkan attack detection rate yang lebih baik jika dibandingkan dengan GA.Kata Kunci : Rough set, Genetic Algorithm, IDS.ABSTRACT: The main function of the IDS (Intrusions Detection System) is to protect the system, analyze, and predict the user’s habits. This habit used see the normal activity or intrusion activity. IDS has been developed, however high alert messages make managers/system administrators maintain the network ineffective. In this study IDS used rough set and genetic algorithm to detect intrusion. Rough set was used for preprocessing data and reduced the dimension data / feature selection. That data was used genetic algorithm for classifying training and testing data. GA has better accuracy rate, false positive rate than Chen et al and GA+RST. However GA+RST has better attack detection rate than Chen et al and GA.Keyword: Rough set, Genetic Algorithm, IDS

Representing Space: A Hybrid Genetic Algorithm for Aesthetic Graph Layout

This paper describes a hybrid Genetic Algorithm (GA) that is used to improve the layout of a graph according to a number of aesthetic criteria. The GA incorporates spatial and topological information by operating directly with a graph based representation. Initial results show this to be a promising technique for positioning graph nodes on a surface and may form the basis of a more general approach for problems involving multi-criteria spatial optimisation

Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance

The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of order packets characterizing the network.Comment: 6 pages, 12 figure

Automated university lecture timetable using Heuristic Approach

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity

Comparative study of different approaches to solve batch process scheduling and optimisation problems

Effective approaches are important to batch process scheduling problems, especially those with complex constraints. However, most research focus on improving optimisation techniques, and those concentrate on comparing their difference are inadequate. This study develops an optimisation model of batch process scheduling problems with complex constraints and investigates the performance of different optimisation techniques, such as Genetic Algorithm (GA) and Constraint Programming (CP). It finds that CP has a better capacity to handle batch process problems with complex constraints but it costs longer time