ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...cscpconf
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...cscpconf
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Multi-Robot Formation Control Based on Leader-Follower Optimized by the IGAIOSRjournaljce
To improve the efficiency of multi-robot formation control, a new formation control algorithm based on leader-follower optimized by the immune genetic algorithm (IGA) is put forward in this paper. Firstly, the formation control is realized by leader-follower algorithm. Then, the proportion coefficients k1, k2 in leaderfollower is optimized by the immune genetic algorithm. Finally, the optimized proportion coefficients k1 and k2 is used in the leader-follower algorithm to finish the multi-robot formation control. Compared with other three formation control algorithms (i.e. GA, simple leader-follower algorithm, behavior algorithm), the experimental results of multi-robot formation control in two environments show that the formation control performance at time and step finishing formation of the proposed formation control algorithm is obviously improved, which verifies the validity of this algorithm.
This course basically deals with the algorithms of genetic part and further deals with how it is formed and what are its techniques and how it can be used in Power
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Multi-Robot Formation Control Based on Leader-Follower Optimized by the IGAIOSRjournaljce
To improve the efficiency of multi-robot formation control, a new formation control algorithm based on leader-follower optimized by the immune genetic algorithm (IGA) is put forward in this paper. Firstly, the formation control is realized by leader-follower algorithm. Then, the proportion coefficients k1, k2 in leaderfollower is optimized by the immune genetic algorithm. Finally, the optimized proportion coefficients k1 and k2 is used in the leader-follower algorithm to finish the multi-robot formation control. Compared with other three formation control algorithms (i.e. GA, simple leader-follower algorithm, behavior algorithm), the experimental results of multi-robot formation control in two environments show that the formation control performance at time and step finishing formation of the proposed formation control algorithm is obviously improved, which verifies the validity of this algorithm.
This course basically deals with the algorithms of genetic part and further deals with how it is formed and what are its techniques and how it can be used in Power
Physiological, Biochemical and Modern Biotechnological Approach to Improvemen...IOSR Journals
Rauwolfia serpentina also known as Sarpagandha (Apocynaceae) is an integral part of Ayurvedic medical system in India for over centuries for the treatment of various ailments. The leaves and roots ofRauwolfiaserpentina contain alkaloids which are secondary metabolites. Major alkaloids identified are Reserpine, Rauwolfine, Serpentine, Sarpagine, Ajmaline, Yohimbine and Ajmalicine.The present paper is an overview of the studies concerning with physiological, biochemical and modern biotechnological approach to improvement of Rauwolfiaserpentina.
A Comparison between Natural and Synthetic Food Flavoring Extracts Using Infr...IOSR Journals
Food is the basic necessity of life. One works hard and earns to satisfy our hunger .But at the end of the day, many of us are not sure of what we eat. We may be eating a dangerous flavors and dyes. Often, we invite diseases rather than good health. The purpose of this article is to detect the presence of food adulterants in some common foods and to create awareness about the artificial tests and dyes. A study of the IR spectra and the optical activitiy of two natural and artificial most common used flavor and colors (Vanilla and Strawberry) were detected. IR spectra of synthetic Vanilla were dominated by specific peaks that attributed to corresponding synthetic pigments (specific spectral band of stretching C=0 ester of aldehydic and ketonic groups in synthetic flavor at1744.87cm-1 with a weak shoulder at1700 cm-1 .And stretching CO of sucrose at (990.49 and 923,70) cm-1.The synthetic Strawberry characterized with specific spectral bands of (C=O stretching at 1634.96 cm-1 in ester and CO stretching of sucrose at 925 cm-1), while these functional groups disappeared in natural. Vanilla and Strawberry extracts. The natural Flavoring extracts posse's levorotatory property; they are optically active, while the synthetic extracts not rotates the plane of polarization of the light which passes through the material, they are said to be; not active optically. The obtained results indicated that, Infrared spectrum and Optical activity could be adapted to detect adulterants added products, and to differentiate between natural and artificial food flavoring extracts.
Video Encryption and Decryption with Authentication using Artificial Neural N...IOSR Journals
Abstract :Multimedia data security is becoming important with the continuous increase of digital communications on internet. With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, commercial, and military fields, which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. . The encryption algorithms developed to secure text data are not suitable for multimedia application because of the large data size and real time constraint. Therefore, there is a great demand for secured data storage and transmission techniques. Information security has traditionally been ensured with data encryption and authentication techniques. The secrecy of communication is maintained by secret key exchange. In effect the strength of the algorithm depends solely on the length of the key. The presented work aims at secure video transmission using randomness in encryption algorithm, thereby creating more confusion to obtain the original data. The security of the original cipher has been enhanced by addition of impurities to misguide the cryptanalyst. Since the encryption process is one way function, the artificial neural networks are best suited for this purpose as they possess features like high security, no distortion and its ability to perform for non linear input-output characteristics, In the presented work the need for key exchange is also eliminated, which is otherwise a perquisite for most of the algorithms used today. The proposed work finds its application in medical imaging systems, military image database communication and confidential video conferencing, and similar such application. The results are obtained through the use of MATLAB 7.14.0 Keywords: Artificial Neural networks, Back propagation algorithm, video encryption and decryption, cipher and decipher.
Dietary Supplementation with Calcium in Healthy Rats Administered with Artemi...IOSR Journals
Reports on the role of calcium on predisposition to cardiovascular disease have been rather inconsistent while studies on its interaction with other medications are ongoing. We therefore investigated the effect of separate and combine administration of calcium supplement with artemisinin-based combination drug on hepatic and serum lipid profile. Thirty two male wistar rats were randomly assigned into four groups of eight rats each. The control (group A) received normal saline. Group B and D were placed on 10mg/Kg calcium twice daily for four weeks. On the thirtieth day, therapeutic dose of artemisinin-based combination was simultaneously administered to group C and group D twice daily for three days. All the rats were then sacrificed after 12 hours fasting, blood was withdrawn and the liver removed and homogenized in an appropriate buffer. Biochemical analysis showed no significant (p>0.05) variation in hepatic triaacylglycerol in all the treated groups whereas calcium supplementation was observed to induce a significant (p<0.05) reduction in hepatic cholesterol. Significant elevations due to calcium supplementation were also observed in serum total cholesterol, LDL cholesterol level and atherogenic risk index with a concomitant reduction in serum HDL cholesterol. No significant change was observed in serum total cholesterol, triacylglycerol and serum lipoproteins in all other treatment groups. Our study suggests that calcium supplementation may predispose to cardiovascular disease and that its co administration with ACT may not aggravate nor reduced the predisposition risk.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
An Improved Iterative Method for Solving General System of Equations via Gene...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
An Improved Iterative Method for Solving General System of Equations via Gene...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 1, Ver. IV (Jan – Feb. 2016), PP 73-77
www.iosrjournals.org
DOI: 10.9790/0661-18147377 www.iosrjournals.org 73 | Page
An Improved Genetic Algorithm Based on Adaptive Differential
Evolution
Feng Jiang1,3
, Yi Shen1,2,3,*
, Panpan Ge2
, Cheng Zhang1,3
, Mingxin Yuan1,2,3
1
(Suzhou Institute of Technology, Jiangsu University of Science and Technology, Zhangjiagang, China)
2
(School of Mechanical and Power Engineering, Jiangsu University of Science and Technology, Zhangjiagang,
China)
3
(Service Center of Zhangjiagang Camphor Tree Makerspace, Zhangjiagang, China)
Abstract: In order to solve the premature convergence and improve the optimization ability of simple genetic
algorithm (SGA) in the complex function optimization, an improved differential evolution based-genetic
algorithm (DGA) is proposed in this paper. On the basis of the selection and crossover operation of SGA, the
adaptive differential evolution operator is used as the main optimization method to realize the mutation
operation. Furthermore, the optimal reservation and worst elimination strategies are added in the SGA. All of
these improvements can significantly improve the optimization performance of SGA. Compared with the SGA,
the simulation results of function optimization show that the proposed DGA is characterized by strong global
optimization ability, quick convergence speed and good stability, which shows the validity of the improvements
to simple genetic algorithm.
Keywords: Self-adaption, Differential evolution, Genetic Algorithm, Function optimization
I. Introduction
Genetic algorithm is an adaptive global optimization probability search algorithm, which simulates the
biological genetic and evolution process in the natural environment [1]. A general framework for solving the
optimization problem of complex systems is provided, which does not depend on the specific areas of the
problems and has strong robustness to various types of problems. An improved genetic algorithm based on
arithmetic crossover operator and non-uniform mutation operator is proposed in [2], which can avoid the
premature convergence, but the search ability is slightly less. The crossover operator and the simulated
annealing algorithm are used to allow the parent generation to participate in the competition in [3], which can
improve the global search ability and help it quickly escape from the local minima, but its optimization
performance is sensitive to the initial temperature of the simulated annealing algorithm. Differential evolution is
a kind of heuristic random search algorithm based on group difference and the optimization is finished using
real encoding in continuous space. But it is incapable of “survival of the fittest” during the selection process.
The mutation operation in the algorithm only aims at the newly generated individual. Besides, all the individuals
in the new generation have no equal chance of mutation, so it is easy to trap into the local optimal solutions.
Compared with the genetic algorithm, the mutation operator of differential evolution algorithm is more
important, and its mutation probability is much higher than that of genetic algorithm. It is easy to see that the
differential evolution algorithm and the genetic algorithm have strong complementarity. Aiming at the problems,
such as premature convergence, weak search ability, in complex function optimization of genetic algorithm,
inspired by the basic principles of differential evolution and the evolution mechanism of genetic algorithm, an
improved genetic algorithm based on adaptive differential evolution is designed in this paper, and the
advantages of genetic algorithm and differential evolution algorithm are fully used to improve the optimization
ability of intelligent algorithm. The function optimization results also verify the validity of the proposed DGA.
II. Simple Genetic Algorithm
The simple genetic algorithm mainly includes encoding, initial population generation, fitness
evaluation, selection, crossover, mutation and other operations. The process can be described as follows:
(1) Code all possible solutions to the problem;
(2) find an objective fitness function for the problem optimization;
(3) Generate an initial population that satisfies all constraints;
(4) calculate the fitness of each chromosome in the population;
(5) Assess whether the termination condition is met. If not, proceed with the following process, otherwise output
the optimal solution and end;
(6) execute the selection operation, that is to generate a new population according to the fitness of each
chromosome;
* Corresponding author. E-mail: sheny456@hotmail.com
2. An Improved Genetic Algorithm Based on Adaptive Differential Evolution
DOI: 10.9790/0661-18147377 www.iosrjournals.org 74 | Page
(7) Execute the crossover and mutation operation. The probabilities of crossover and mutation operation are
denoted as Pc and Pm respectively;
(8) Assess whether the termination condition is met again. If not, turn to step (6), otherwise output the optimal
solution and end.
The workflow of the simple genetic algorithm is shown in Fig.1.
Fig. 1 Workflow of simple genetic algorithm
III. Design of Genetic Algorithm Based on Adaptive Differential Evolution
As mentioned above, the DGA is designed on the basis of the selection and hybridization operation of
SGA and the adaptive differential evolution operator is used to realize the mutation operation. Furthermore, the
optimal reservation and worst elimination strategies are added in the SGA.
Population initialization and coding
In the DGA, according to the range of variables and optimization precision, 20 individuals are
randomly generated to form the initial population. In order to facilitate the subsequent operations, the binary
encoding is used in DGA.
Fitness evaluation and detection
Not only fitness is used to measure the quality of each individual in the population, but also a measure
of each individual's ability of adaptation to its environment. In the algorithm, the chromosome encoding of each
individual can be achieved after binary encoding. An individual is a possible solution to the practical problem,
and all the possible solutions correspond with the function values. Here, the function refers to the fitness
function, and the function value is the fitness value. In intelligent algorithms, the fitness is a very important
index, whose size directly affects the survival probability of each individual in the population. It also has a great
influence on the convergence speed and other optimization performance of the algorithm. Therefore, the fitness
function should be carefully designed. In this paper, because the fitness function is designed according to the
objective function, it also reflects the problem to be solved.
Selection strategy
Before selecting operation, according to the principle of the best reservation and the worst elimination,
the individuals in the population are pretreated. By setting the threshold value of the reservation ratio and the
elimination ratio, the individuals whose fitness is greater than fa are directly retained, and the individuals whose
individual fitness is less than fb are directly eliminated. Then the proportional selection operation is used to
select the operation. The individual selected probability can be described as:
),......,3,2,1(
1
Ni
f
f
P N
i
i
i
(1)
3. An Improved Genetic Algorithm Based on Adaptive Differential Evolution
DOI: 10.9790/0661-18147377 www.iosrjournals.org 75 | Page
where fi is the fitness value of individual i. N is the size of the population.
The probability that an individual is selected is proportional to its fitness. And the selection operation
can improve the average fitness of the population at the same time. New population is composed of direct
retention of individuals and selected individuals.
Crossover operator
The execution of crossover operator is: First, two individuals (namely Xi, Xj) are randomly selected to
make a pair, then whether the crossover operation of the two selected individuals is carried out depends on the
pre-set hybrid probability Ph. That is to say, if a uniform random number which is generated in (0, 1) is less than
Ph, the crossover operation is carried out, and two new individuals (namely Xi
’
, Xj
’
) are generated. The crossover
process can be described as follows:
),,2,1(),,1(
),,1(),,2,1(
jij
jii
XLXX
LXXX
(2)
Where μ is the crossover point, L is the total length of binary encoding.
Adaptive differential evolution operator
In difference strategy[5], three points from the current population are arbitrarily selected. One point is
taken as a base point and the other two points are taken as reference points, then a disturbance is generated. The
generation process of the disturbance can be described as:
))()(()()1( 321 mymymymV rrrn n ≠ r1 ≠ r2 ≠ r3 (3)
where δ is a scaling factor and yn(m) represents the nth individual in the mth generation.
The scaling factor δ is dynamically adjusted along with the individual fitness and evolution generation.
When the individual fitness in the population tends to converge or converge to the local optimal solution, the δ
is increased. When the individual fitness in the population is relatively dispersed, the δ decreases. At the same
time, if the fitness is greater than the average fitness of the population, corresponding to the larger δ, the
individual is eliminated. In contrast, the individuals who are below average fitness and closer to the average
fitness of the population correspond to the smaller δ to ensure population diversity. Therefore, the adaptive
scaling factor is able to provide the best δ to each solution. The introduced adaptive difference operation
maintains the population diversity in the algorithm. At the same time, the convergence of the algorithm is also
guaranteed. The δ is on the basis of the following Eq.(4) to carry out adaptive adjustment:
avg
avg
bestavg
avg
ff
ff
ff
ff
'1
'
'
1
(4)
Where f' is the fitness value of an individual. favg is the average fitness value of current population. fbest is the
maximum fitness value of the current population.
At the beginning of the optimization, because the gap between fbest and fbest is very large, there is no
possibility of local convergence. If the f' is smaller, the δ is smaller, then good genes can be preserved. With the
increase of the evolution generation, the difference of the gap between fbest and fbest becames smaller and δ tends
to decrease. That is to say that the speed of convergence of the optimal solution is gradually accelerated.
Because the convergence speed is gradually accelerated, the risk of local convergence is reduced, and the global
search ability is improved.
Algorithm flow
In order to further improve the optimization performance of the algorithm, based on SGA, the
crossover operator and the adaptive differential evolution operator are introduced. The algorithm flow of the
DGA is as follows:
Step1 Initialize algorithm parameters: population size N , reservation ratio threshold fa , elimination ratio
threshold fb, maximal evolution generation kmax, individual selected probability Pi, crossover probability
Ph, variable precision Vp, and variable range Vr.
Step2 Generated the initial population on the basis of variable precision and variable range.
Step3 Execute adaptive evaluation and detection and generate the population according to the principle of the
best reservation and the worst elimination. Calculate the individual selection probability by Eq.(1), and
execute the selection operation.
Step4 Execute crossover operation according to the probability Ph using Eq.(2).
4. An Improved Genetic Algorithm Based on Adaptive Differential Evolution
DOI: 10.9790/0661-18147377 www.iosrjournals.org 76 | Page
Step5 Execute adaptive differential evolution operation using Eq.(3) and new population is got. The scaling
factor δ is dynamically adjusted according to Eq.(4).
Step6 Judge whether the terminating condition is satisfied. If not, k←k+1, go to Step3, otherwise end.
IV. Function Optimization Test and Analysis
In order to verify the optimization performance of DGA, the following five different functions are
provided to test on a computer using Matlab[6]
. The test results are compared with those of the SGA. In DGA,
N=20, fa=0.5, fb=1.5, kmax=1000, Ph=0.25. In SGA, N=20, Pc=0.3, Pm=0.1, kmax=1000. Considering the
randomness of intelligent algorithms, each function was independently tested with 50 repetitions.
1. Schaffer’s Function
)))(001.01/(()5.0))((sin(5.0min 22
2
2
1
22
2
2
11 xxxxf (5)
]10,10[, 21 xx , f*
=0.
2. Ackley's Path function
20)1exp()))2cos()2(cos(5.0exp())(5.02.0exp(20min 21
2
2
2
12 xxxxf (6)
]12.5,12.5[, 21 xx , f*
=0.
3. Rastrigrin function
)2cos(10)2cos(1020min 2
2
21
2
13 xxxxf (7)
]12.5,12.5[, 21 xx , f*
=0.
4. Michalewicz’s function
202
22
202
114 ))/2)(sin(sin())/)(sin(sin(min xxxxf (8)
],0[, 21 xx , f*
=-1.8013.
5. Camel function
2
2
2
221
2
1
4
1
2
15 )44(]3/1.24[min xxxxxxxf (9)
]12.5,12.5-[, 21 xx , f*
=-1.031628.
Table 1 Comparison results among three algorithms
f Nbest Nmax Nmean Nsd
SGA DGA SGA DGA SGA DGA SGA DGA
f1 10-13
49 50 706 564 355.98 286.54 145.25 86.66
f2 10-11
49 50 849 624 393.41 292.86 153.30 115.66
f3 10-6
48 50 557 398 311.35 221.14 103.78 77.55
f4 10-3
31 47 1000 773 434.97 291.98 256.54 216.14
f5 10-3
42 50 861 451 331.43 236.08 251.93 103.75
Table 1 is the comparison results among DGA and SGA. Nbest denotes the number of times an optimal
solution was found. Nmax,Nmean and Nsd denote the maximum, average convergence and standard deviation
generations needed to find the optimal solutions, respectively. From the comparison results, we can see that the
optimization results of DGA are better than those of SGA, which shows the strong optimization ability of the
DGA. DGA improved the global search capability of SGA by adaptive differential evolution operator. Under the
condition of ensuring population diversity, the algorithm performs adaptive differential evolution operator
according to the fitness value after the individual fitness is tested. So the whole population quality is improved
and the risk of local convergence is reduced. The global searching ability of DGA also is improved.
ƒ(x1,x2)
0 50 100 150 200 250
0
0.5
1
1.5
2
Generation
DGA
SGA
ƒ(x1,x2)
0 50 100 150 200 250
0
0.5
1
1.5
2
Generation
DGA
SGA
Fig. 2 Average evolutionary curves of optimal solution Fig. 3 Average evolutionary curves of whole
Of Rastrigrin function population of Rastrigrin function
5. An Improved Genetic Algorithm Based on Adaptive Differential Evolution
DOI: 10.9790/0661-18147377 www.iosrjournals.org 77 | Page
Fig.2 gives average evolutionary curves of optimal solution of Rastrigrin function. From the two
curves, it can also be seen that the convergence speed of the proposed DGA is faster than the speed of SGA,
which further verify the effectiveness of the DGA. Fig.3 gives Average evolutionary curves of whole population
of Rastrigrin function. From the two curves, it can also be seen that the population convergence performance of
the proposed DGA is better than that of SGA, and the search stability of DGA is higher than that of SGA.
V. Conclusions
In order to solve the problem of premature convergence and improve the optimization ability of the
simple genetic algorithms during the complex function optimization, an improved genetic algorithm based on
adaptive differential evolution is presented. Compared with the simple genetic algorithm, computer simulation
shows that the convergence speed of the proposed DGA algorithm is faster and the global search ability is
improved. Furthermore, the DGA can effectively reduce the dependence of the convergence on the initial
population.
Acknowledgements
This work is supported by the 2015 College Students Practice Innovation Training Program of Jiangsu
Province, 2015 Entry Project of Service Center of Zhangjiagang Camphor Tree Makerspace, and Modern
Educational Technology Project of Jiangsu Province (No. 2015-R-40701).
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