A Java-Based Package for Genetic Algorithm

2013 ◽  
Vol 717 ◽  
pp. 428-432
Author(s):  
Shan Ping Qiao ◽  
Bao Qiang Yan
Keyword(s):  
Genetic Algorithm ◽  
Programming Language ◽  
Computer Programming ◽  
Optimization Problems ◽  
Object Oriented ◽  
Function Optimization ◽  
Good Effect ◽  
The Future ◽  
Advanced Computer

Genetic algorithm is a very important and popular kind of algorithm of evolution computing. In order to use this algorithm better and platform-independently, this paper introduces an implement package which is coded in Java, an object-oriented and platform-independent advanced computer programming language, for genetic algorithm. This package includes several sub-packages. In each sub-package, there are some classes with different roles and functions. After the test, these classes can work properly and efficiently in together. The good effect has been received through using this algorithm in four function optimization problems. For the further goal, some studies even need to be carried out in the future.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem

2013 ◽  
pp. 8-16
Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li
Keyword(s):  
Genetic Algorithm ◽  
Sexual Reproduction ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Function Optimization ◽  
Premature Convergence ◽  
Multimodal Functions ◽  
Multimodal Function Optimization ◽  
Multimodal Function

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.

Genetic Algorithm-Based COD Calibration Method Research

2013 ◽  
Vol 341-342 ◽  
pp. 748-753
Author(s):  
Jia Ni Qian ◽  
Tian Wang ◽  
Xi Peng Lv ◽  
Yun Long Tang ◽  
Xiu Fen Ye
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Evolutionary Process ◽  
Function Optimization ◽  
Measuring System ◽  
Good Effect ◽  
Fitness Value ◽  
Radial Basis

For better realization of the function of Chemical oxygen demand (COD) online measuring instrument and improving its measurement accuracy , a good calibration and identification of signals collected is needed. During the process, the problem on parameter identification of undetermined function can be transformed into function optimization. Considering the characteristics of genetic algorithm It is introduced into the function identification of the measuring system and compare it with the radial basis function neural network. As for the premature of population evolutionary process, this article presents the method to select operators according to genetic fitness value of each individual and designs a set of system identifier based on Genetic Algorithm to identify the system. Finally, test the experimental data get from water bath in the lab dish. The relative error of output value does not exceed 8%.The experiment results show that genetic algorithm has a good effect in the system identifier on the calibration and identification of COD measuring system, better than radial basis function neural network.

scholarly journals EL FUTURO DE APPLE: SWIFT VERSUS OBJECTIVE-C

2015 ◽  
Vol 6 (2) ◽  
pp. 6
Author(s):  
Cristian González García ◽  
Jordán Pascual Espada B. ◽  
Cristina Pelayo G-Bustelo ◽  
Juan Manuel Cueva Lovelle
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Quantitative Evaluation ◽  
Functional Programming ◽  
Computer Programming ◽  
Object Oriented ◽  
Qualitative Evaluation ◽  
Object Oriented Programming ◽  
Computer Languages ◽  
Programming Software

Hace unos meses Apple presentó un nuevo lenguaje de programación para sus plataformas: Swift. Con Swift, Apple pretende atraer a los programadores de los lenguajes de programación basados en la sintaxis de C++ y darles una mayor abstracción, que con Objective-C, para que sea más fácil programar para las plataformas de Apple. Por estas razones, se hace necesario contrastar lo pretendido por Apple y realizar un estudio del lenguaje de programación a fin de contrastar su objetivo. Para ello, se hicieron dos evaluaciones, una cualitativa y otra cuantitativa, con el propósito de verificar en qué medida Swift es un avance respecto a Objective-C.DThe Future of Apple: Swift Versus Objective-CABSTRACTFew months ago, Apple presented a new programming language: Swift. With Swift, Apple pretends to attract the programmers of the programming languages based on C++ syntax and gives them a higher abstraction than with Objective-C for being easier to programme to Apple’s platforms. For these reasons, it is necessary to contrast what is intended by Apple and do a study of the programming language to ascertain their goal. For this purpose, we did two evaluations, firstly a qualitative evaluation and after, a quantitative evaluation to verify in how much Swift is an advance with respect to Objective-C.Keywords: computer languages, computer programming, functional programming, object oriented programming, programming, software.

scholarly journals Exploration based Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 5 (3) ◽  
pp. 91-100
Author(s):  
Hanaa Abdelrahman ◽  
Mohammed Bakri Bashir ◽  
Adil Yousif
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Large Scale ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Adaptive Method ◽  
Virtual Organizations ◽  
Good Effect ◽  
Exploration Process

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.

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.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2021 ◽  
pp. 148-180
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

scholarly journals pyFOOMB: Python Framework for Object Oriented Modelling of Bioprocesses

2020 ◽  
Author(s):  
Johannes Hemmerich ◽  
Niklas Tenhaef ◽  
Wolfgang Wiechert ◽  
Stephan Noack
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Programming Language ◽  
Modular Design ◽  
Optimization Problems ◽  
Object Oriented ◽  
Data Generation ◽  
Seamless Integration ◽  
Model Parameter ◽  
Model Parameter Estimation

Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modelling with non-linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi-purpose programming language, ODEs can be formulated in an object-oriented manner, which facilitates their modular design, reusability and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi-stage processes, discontinuities in the right-hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programming language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

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