A Study on Optimization Algorithm (OA) in Machine Learning and Hierarchical Information

2020 ◽  
Vol 17 (4) ◽  
pp. 1733-1736
Author(s):  
N. Pooranam ◽  
M. Nithya ◽  
D. Praveen Kumar ◽  
Rashmi P. Nayak ◽  
G. Rakesh
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Second Step ◽  
Local Maxima ◽  
Specific Direction ◽  
Random Probability ◽  
The Given ◽  
Over Time

Genetic Algorithm is a division of machine learning, where the computers are programmed to teach themselves to complete the given task over time. In our project, we simulate many rockets to fly towards the target specified. Genetic algorithm revolves around three main concepts. First generate a population of random rockets that fly in random directions. Each rocket is implemented as an array of Vectors, where each vector points to a specific direction at a given time. We then apply a fitness function that calculates the best performing rockets in each generation. With the fitness function, we now select the best rockets with which we form the next population. This involves two steps: First step is the crossover. Choose two parents i.e., two rockets and use their vector values to create a child rocket. This is done by retrieving the first half vectors from the first parent and second half vectors from the second parent and fuses them to build the child rocket, Second step is the mutation. This step is very crucial. If mutation is not applied, we will receive a new population that is only built around best performing ones from the previous population.We will then land in local maxima and may never reach the target. Mutation helps create individual rockets that go beyond the local maxima to reach the target. But over mutation will lead to too much diversity that is not beneficial to the system. Thus, define a mutation rate that is optimally balanced. In mutation, we choose a rocket with random probability, and alter its vector values randomly. This new population of rockets forms the next generation.

A Bi-objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach

2019 ◽  
Vol 28 (2) ◽  
pp. 333-346 ◽  
Author(s):  
Shelza Suri ◽  
Ritu Vijay
Keyword(s):  
Genetic Algorithm ◽  
Image Encryption ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Good Choice ◽  
Algorithm Optimization ◽  
Change Rate ◽  
Fitness Functions ◽  
Significant Performance ◽  
The Given

Abstract The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid (DNA)-based hybrid approach to encrypt images using a bi-objective genetic algorithm (GA) optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient (CC), entropy with unified average changing intensity (UACI), and entropy with number of pixel change rate (NPCR) – simultaneously to optimize the encrypted data in the second stage. The bi-objective optimization using entropy with CC shows significant performance gain over the single-objective GA optimization for image encryption.

scholarly journals Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2321
Author(s):  
Ahmed A. Ewees ◽  
Mohammed A. A. Al-qaness ◽  
Laith Abualigah ◽  
Diego Oliva ◽  
Zakariya Yahya Algamal ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Fitness Function ◽  
Feature Selection Method ◽  
Search Strategies ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

scholarly journals A new genetic algorithm specifically based on mutation and selection

2007 ◽  
Vol 39 (01) ◽  
pp. 141-161 ◽  
Author(s):  
L. Rigal ◽  
L. Truffet
Keyword(s):  
Genetic Algorithm ◽  
Selection Pressure ◽  
Large Deviation ◽  
Fitness Function ◽  
Critical Value ◽  
Gradient Algorithm ◽  
Selection Algorithm ◽  
Local Maxima ◽  
Selection Operator ◽  
Mutation And Selection

In this paper we propose a new genetic algorithm specifically based on mutation and selection in order to maximize a fitness function. This mutation-selection algorithm behaves as a gradient algorithm which converges to local maxima. In order to obtain convergence to global maxima we propose a new algorithm which is built by randomly perturbing the selection operator of the gradient-like algorithm. The perturbation is controlled by only one parameter: that which allows the selection pressure to be governed. We use the Markov model of the perturbed algorithm to prove its convergence to global maxima. The arguments used in the proofs are based on Freidlin and Wentzell's (1984) theory and large deviation techniques also applied in simulated annealing. Our main results are that (i) when the population size is greater than a critical value, the control of the selection pressure ensures the convergence to the global maxima of the fitness function, and (ii) the convergence also occurs when the population is the smallest possible, i.e. 1.

scholarly journals Hybrid gene selection method based on mutual information technique and dragonfly optimization algorithm

2021 ◽  
Vol 3 (3 (111)) ◽  
pp. 64-69
Author(s):  
Sarah Ghanim Mahmood ◽  
Raed Sabeeh Karyakos ◽  
Ilham M. Yacoob
Keyword(s):  
Machine Learning ◽  
Optimization Algorithm ◽  
Gene Selection ◽  
Negative Impact ◽  
Fitness Function ◽  
Classification Model ◽  
K Nearest Neighbors ◽  
Reliable Classification ◽  
Microarray Datasets ◽  
Difficult Part

One of the most prevalent problems with big data is that many of the features are irrelevant. Gene selection has been shown to improve the outcomes of many algorithms, but it is a difficult task in microarray data mining because most microarray datasets have only a few hundred records but thousands of variables. This type of dataset increases the chances of discovering incorrect predictions due to chance. Finding the most relevant genes is generally the most difficult part of creating a reliable classification model. Irrelevant and duplicated attributes have a negative impact on categorization algorithms’ accuracy. Many Machine Learning-based Gene Selection methods have been explored in the literature, with the aim of improving dimensionality reduction precision. Gene selection is a technique for extracting the most relevant data from a series of datasets. The classification method, which can be used in machine learning, pattern recognition, and signal processing, will benefit from further developments in the Gene selection technique. The goal of the feature selection is to select the smallest subset of features but carrying as much information about the class as possible. This paper models the gene selection approach as a binary-based optimization algorithm in discrete space, which directs binary dragonfly optimization algorithm «BDA» and verifies it in a chosen fitness function utilizing precision of the dataset’s k-nearest neighbors’ classifier. The experimental results revealed that the proposed algorithm, dubbed MI-BDA, in terms of precision of results as measured by cost of calculations and classification accuracy, it outperforms other algorithms

scholarly journals A new genetic algorithm specifically based on mutation and selection

2007 ◽  
Vol 39 (1) ◽  
pp. 141-161 ◽  
Author(s):  
L. Rigal ◽  
L. Truffet
Keyword(s):  
Genetic Algorithm ◽  
Selection Pressure ◽  
Large Deviation ◽  
Fitness Function ◽  
Critical Value ◽  
Gradient Algorithm ◽  
Selection Algorithm ◽  
Local Maxima ◽  
Selection Operator ◽  
Mutation And Selection

In this paper we propose a new genetic algorithm specifically based on mutation and selection in order to maximize a fitness function. This mutation-selection algorithm behaves as a gradient algorithm which converges to local maxima. In order to obtain convergence to global maxima we propose a new algorithm which is built by randomly perturbing the selection operator of the gradient-like algorithm. The perturbation is controlled by only one parameter: that which allows the selection pressure to be governed. We use the Markov model of the perturbed algorithm to prove its convergence to global maxima. The arguments used in the proofs are based on Freidlin and Wentzell's (1984) theory and large deviation techniques also applied in simulated annealing. Our main results are that (i) when the population size is greater than a critical value, the control of the selection pressure ensures the convergence to the global maxima of the fitness function, and (ii) the convergence also occurs when the population is the smallest possible, i.e. 1.

scholarly journals Study on optimal conditions of flocculation in deinking wastewater treatment

2021 ◽  
Vol 68 (1) ◽  
Author(s):  
Ming Li ◽  
Kaitang Hu ◽  
Jin Wang
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Support Vector Regression ◽  
Fitness Function ◽  
Real Life ◽  
Support Vector ◽  
Optimal Conditions ◽  
Support Vector Regression Model ◽  
Hydraulic Conditions

AbstractFlocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physical methods. The goal of this paper is to determine the optimal hydraulic conditions using machine learning in order to enable efficient flocculation and improve performance during the treatment of deinking wastewater. Experiments using polymerized aluminum chloride as flocculant to treat deinking wastewater were carried out. Based on the orthogonal array test, 16 different combinations of hydraulic conditions were chosen to investigate the performance of flocculation, which was indicated by the turbidity of the solution after treatment. To develop a model representing the relationship between the hydraulic conditions and the performance of wastewater treatment, the machine learning methods, support vector regression and Gaussian process regression, were compared, whereby the support vector regression method was chosen. According to the fitness function derived from the support vector regression model, a genetic algorithm was applied to evaluate the optimal hydraulic conditions. Based on the optimal conditions determined by the genetic algorithm and real-life experience, a set of hydraulic conditions were implemented experimentally. After treatment under higher stirring speed at 120 rpm for 1 min and lower stirring speed at 20 rpm for 5 min at a temperature of 20 °C, the turbidity of deinking wastewater was measured as 1 NTU. The turbidity reduction was as high as 99.6%, which indicated good performance of the deinking wastewater treatment.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

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