A meta-parameter tuning model to improve the genetic algorithms design of labeling diversity mappers

2021 ◽  
Author(s):  
Shaheen Solwa ◽  
Ayodeji James Bamisaye
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Parameter Tuning ◽  
Space Time ◽  
Diversity Gain ◽  
Parameter Setting ◽  
Fitness Value ◽  
Best Parameter ◽  
Diversity Gains

Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.

scholarly journals Optimasi Pendistribusian Kelas Pada Dosen di STMIK STIKOM Indonesia Menggunakan Algoritma Genetika

2019 ◽  
Vol 2 (1) ◽  
pp. 145-154
Author(s):  
Aniek Suryanti Kusuma ◽  
Komang Sri Aryati
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Parameters ◽  
Fitness Function ◽  
Genetic Process ◽  
Fitness Value ◽  
Distribution Process ◽  
Class Scheduling ◽  
System Requirements

The stage of class scheduling starts from scheduling courses in classes, then distributing the class to lecturers. The process of distributing classes to lecturers becomes an obstacle for the STMIK STIKOM Indonesia academic body because the academic body must adjust the existing class with the lecturer who is interested in it as well as the lecturer chosen to support a class so that it does not have classes that have a time conflict. One method for solving these problems is by using genetic algorithms that work by generating a number of random solutions and then processing the collection of solutions in a genetic process. There are eight genetic algorithm procedures, which are random chromosome generation procedures, chromosome repair to validate chromosomes from their limits, fitness function to calculate the feasibility of a solution, crossover, mutation, child repair and elitism. The output of this research is in the form of an analysis and determination of the system requirements that must exist. In addition, it produces a trial report on the effect of genetic parameters to determine the effect of changes in the value of genetic parameters on the fitness value and the time used to carry out the distribution process.  

scholarly journals Power Factor Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

2019 ◽  
Author(s):  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
Francisco M. Arrabal-Campos ◽  
Raul Baños
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Power Factor ◽  
Geometric Algebra ◽  
Active Filters ◽  
High Quality ◽  
Passive Filters ◽  
Algebra Theory ◽  
Open Question

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control over the flow of electricity between the source and the load, they are often used passive or active filters. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtain high quality solutions that could be generalized to similar problems of any dimension.

An Object-Oriented Framework for Rapid Genetic Algorithm Development

2007 ◽  
pp. 608-624 ◽  
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Rapid Development ◽  
Object Oriented ◽  
Lot Sizing Problem ◽  
Fast Development ◽  
Complex Optimization

JGA, the acronym for Java Genetic Algorithm, is a computational object-oriented framework for rapid development of evolutionary algorithms for solving complex optimization problems. This chapter describes the JGA framework and illustrates its use on the dynamic inventory lot-sizing problem. Using this problem as benchmark, JGA is compared against three other tools, namely, GAlib, an open C++ implementation; GADS, a commercial MatlabÒ toolbox; and PROC GA, a commercial (yet experimental) SASÒ procedure. JGA has proved to be a flexible and extensible object-oriented framework for the fast development of single (and multi-objective) genetic algorithms by providing a collection of ready-to-use modules (Java classes) that comprise the nucleus of any genetic algorithm. Furthermore, JGA has also been designed to be embedded in larger applications that solve complex business problems.

Penjadwalan Produksi Dengan Algoritma Genetik Untuk Meniminisasi Makespan di PT AAA

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
M. Ghassan Fattah ◽  
Rosnani Ginting
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Machine Scheduling ◽  
Optimal Timing ◽  
Scheduling Problems ◽  
Fitness Value ◽  
Genetic Algorithm Method

PT. AAA dari bulan Januari sampai Desember mendapat total 88 order dengan jumlah keterlambatan 12 order maka persentase keterlembatan adalah 13,63%. Tujuan penelitian ini adalah untuk merancangan penerapan algoritma genetik yang dapat menghindari keterlambatan order yaitu untuk mengukur makespan produk dan merancang urutan penjadwalan mesin. Penyelesaian masalah penjadwakan dengan algoritma genetik. Algoritma genetik merupakan teknik search stochastic yang berdasarkan mekanisme seleksi alam dan genetika natural dengan melakukan proses inisialisasi awal lalu dicari nilai fitness dari setiap individu, yang akan menjadi induk adalah yang memiliki nilai fitness terbaik lalu dilakukan proses penyilangan dan mutasi dan pemilihan waktu optimal. Dari hasil perhitungan dengan menggunakan metode Algoritma Genetika diperoleh urutan penjadwalan mesin terbaik dan dengan nilai makespan terkecil.   PT. AAA from January to December received a total of 88 orders with the number of delays of 12 orders, the percentage of bridges was 13.63%. The purpose of this study is to design the application of a genetic algorithm that can avoid delay in order to measure product makespan and design the order of machine scheduling. Resolving scheduling problems with genetic algorithms. Genetic algorithm is a search stochastic technique that is based on the mechanism of natural selection and natural genetics by carrying out the initial initialization process and then looks for the fitness value of each individual, who will be the parent who has the best fitness value and then the process of crossing and mutation and optimal timing. From the results of calculations using the Genetic Algorithm method, the best sequence of machine scheduling is obtained and with the smallest makespan value.

scholarly journals Evolutionary Algorithms in Stabilization of Inverted Pendulum

2020 ◽  
Vol 8 (6) ◽  
pp. 1447-1453
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Test Bench ◽  
Design Parameters ◽  
Characteristic Parameters ◽  
Linkage Learning

A novel optimization algorithm of fuzzy logic controller (FLC) using genetic algorithms is used to characterize the major design parameters of an FLC known as characteristic parameters. The characteristic parameters simplify the design of FLC which are encoded into a chromosome as an integer string. These are optimized by maximizing the evaluated fitness through genetic operations to achieve the optimized FLC .An effective Genetic Algorithm (GA) is proposed using linkage learning, or building block identification. The genes arranged to have a fitness enhancement is the essence of linkage learning. A perfect and faster extended GA is suggested using an effective method to learn distributions and then by linking them. Stabilization of Inverted pendulum pole angle is taken as test bench.

scholarly journals Penyelesaian CCVRPTW Menggunakan Biased Random Key Genetic Algorithm-Populasi Degradasi

2017 ◽  
Vol 16 (1) ◽  
pp. 28
Author(s):  
Listy Avri Christiana ◽  
Hari Prasetyo
Keyword(s):  
Genetic Algorithm ◽  
Soft Drink ◽  
Time Windows ◽  
Heuristic Method ◽  
Cost Savings ◽  
Parameter Setting ◽  
Routing Problem ◽  
Best Parameter ◽  
Matlab Programming ◽  
Better Than

The article presents the Biased Random Key Genetic Algorithm-Population Degradation (BRKGA-PD) design for completing Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) on soft drink distributions that have been studied by Sembiring (2008). The goal is to determine some closed routes in meeting consumer demand with time limits and limit the capacity of vehicles used, so the total cost of distribution is minimal. The proposed algorithm adopts the extinction of population size. BRKGA-PD is coded using Matlab programming with the best parameter setting. The resulting solution is a subrute with a minimum of distribution fee. This algorithm is compared with two other methods, namely BRKGA general and heuristic methods. The results of this study can be concluded that the BRKGA-PD method is able to improve the general BRKGA because with a time difference that is not significant can provide cost savings of Rp. 6.857,- and BRKGA-PD is better than heuristic method because it can save more cost Rp. 87.000,-.

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

Heuristic Artificial Intelligent Algorithm for Genetic Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 516-521 ◽  
Author(s):  
Luo Lie
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Search Technique ◽  
Intelligent Algorithm ◽  
Search Problems ◽  
Artificial Intelligent ◽  
Search Heuristics

A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

scholarly journals A Genetic Algorithm based Control Strategy for the Energy Management Problem in PHEVs

10.29007/md3x ◽  
2018 ◽  
Author(s):  
Johanna Nellen ◽  
Benedikt Wolters ◽  
Lukas Netz ◽  
Sascha Geulen ◽  
Erika Abraham
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Optimization Problems ◽  
Combustion Engine ◽  
Control Strategies ◽  
Parameter Tuning ◽  
Management Problem ◽  
Hybrid Electric

Genetic algorithms have been applied to various optimization problems in the past. Our library GeneiAL implements a framework for genetic algorithms specially targeted to the area of hybrid electric vehicles. In a parallel hybrid electric vehicle (PHEV), an internal combustion engine and an electrical motor are coupled on the same axis in parallel. In the area of PHEVs, genetic algorithms have been extensively used for the optimization of parameter tuning of control strategies. We use GeneiAL to control the torque distribution between the engines directly. The objective function of this control strategy minimizes the weighted sum of functions that evaluate the fuel consumption, the battery state of charge, and drivability aspects over a prediction horizon of fixed finite length.We analyze the influence of these weights and different configurations for the genetic algorithm on the computation time, the convergence, and the quality of the optimization result. For promising configurations, we compare the results of our control strategy with common control strategies.

scholarly journals Optimisation of Rice Fertiliser Composition using Genetic Algorithms

2019 ◽  
Vol 2 (2) ◽  
pp. 72
Author(s):  
Retno Dewi Anissa ◽  
Wayan Firdaus Mahmudy ◽  
Agus Wahyu Widodo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Test Results ◽  
Random Mutation ◽  
Rice Quality ◽  
Food Scarcity ◽  
Fitness Value ◽  
Reproduction Process ◽  
Tournament Selection

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate of 0.9 and the mutation rate of 0.1. The amount of generation is 10 with the best fitness value is generated is equal to 1,603.

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