scholarly journals Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 823
Author(s):  
Nabeel Al-Milli ◽  
Amjad Hudaib ◽  
Nadim Obeid
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Search Algorithm ◽  
Search Space ◽  
Bankruptcy Prediction ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Exploration Process ◽  
Injection Method ◽  
Population Based Algorithm

Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and exploitation are usually controlled offline by proper settings of parameters that affect the population-based algorithm performance. In this paper, we proposed a dynamic controller for one of the most well-known search algorithms, which is the Genetic Algorithm (GA). Population Diversity Controller-GA (PDC-GA) is proposed as a novel feature-selection algorithm to reduce the search space while building a machine-learning classifier. The PDC-GA is proposed by combining GA with k-mean clustering to control population diversity through the exploration process. An injection method is proposed to redistribute the population once 90% of the solutions are located in one cluster. A real case study of a bankruptcy problem obtained from UCI Machine Learning Repository is used in this paper as a binary classification problem. The obtained results show the ability of the proposed approach to enhance the performance of the machine learning classifiers in the range of 1% to 4%.

scholarly journals Genetic Algorithm Approach to Find the Estimated Value of HMM parametersfor NS5 Methyltransferase Protein

2021 ◽  
Vol 14 (3) ◽  
pp. 1567-1578
Author(s):  
Nidhi Katiyar ◽  
Ravindra Nath ◽  
Shashwat Katiyar
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Search Algorithm ◽  
Specific Treatment ◽  
Machine Learning Techniques ◽  
Severe Dengue ◽  
Severe Illness ◽  
Support Vector ◽  
Learning Approaches ◽  
Observation Sequence

Dengue is the pandemic disease caused by Dengue virus (DENV), a mosquito-borne flavivirus. In recent years dengue has emerged as a foremost cause of severe illness and deaths in developing countries.About 400 million dengue infections occur worldwide each year.In general, dengue infections create only mild illness but infrequently expand into a lethal illness termed as severe dengue for which no specific treatment. The machine learning approach plays a significant role in bioinformatics and other fields of computer science.It exploitsapproaches like Hidden Markov Model (HMM), Genetic Algorithm (GA), Artificial Neural Network (ANN), and Support Vector Machine (SVM).The GA is a randomized search algorithm for solving the problem based on natural selection phenomena.Many machine learning techniques are based on HMM have been positively applied. In this work, We firstly used HMM parameters on the biological sequence,and after that, we catch the probability of the observation sequence of a mutated gene sequence. This study comparesboth methods, G.A. and HMM, to get the highest estimated value of the observation sequence. In this paper, we also discuss the applications ofGA in the bioinformatics field. In a further study, we will apply the other machine learning approaches to find the best result of protein studies.

scholarly journals Genetic Algorithm – A Sensible Evolutionary Optimization Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 2805-2809
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Effective Population ◽  
Natural Systems ◽  
Heuristic Search Algorithm ◽  
Feasible Solutions

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

Benchmarking the Performance of a Machine Learning Classifier Enabled Multiobjective Genetic Algorithm on Six Standard Test Functions

2017 ◽  
Author(s):  
Kayla Zeliff ◽  
Walter Bennette ◽  
Scott Ferguson
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Objective Function ◽  
Hybrid Approach ◽  
Pareto Frontier ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multiobjective Genetic Algorithm ◽  
Learning Classifier ◽  
Function Calls

Previous work tested a multi-objective genetic algorithm that was integrated with a machine learning classifier to reduce the number of objective function calls. Four machine learning classifiers and a baseline “No Classifier” option were evaluated. Using a machine learning classifier to create a hybrid multiobjective genetic algorithm reduced objective function calls by 75–85% depending on the classifier used. This work expands the analysis of algorithm performance by considering six standard benchmark problems from the literature. The problems are designed to test the ability of the algorithm to identify the Pareto frontier and maintain population diversity. Results indicate a tradeoff between the objectives of Pareto frontier identification and solution diversity. The “No Classifier” baseline multiobjective genetic algorithm produces the frontier with the closest proximity to the true frontier while a classifier option provides the greatest diversity when the number of generations is fixed. However, there is a significant reduction in computational expense as the number of objective function calls required is significantly reduced, highlighting the advantage of this hybrid approach.

scholarly journals An Improved Squirrel Search Algorithm for Global Function Optimization

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 80 ◽  
Author(s):  
Yanjiao Wang ◽  
Tianlin Du
Keyword(s):  
Search Algorithm ◽  
Statistical Tests ◽  
Search Space ◽  
Search Method ◽  
Population Diversity ◽  
Convergence Speed ◽  
Function Optimization ◽  
Experimental Results ◽  
Selection Strategy ◽  
Evolutionary Information

An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.

A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems

2015 ◽  
Vol 24 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Second Phase ◽  
Examination Timetabling ◽  
Bee Colony ◽  
Population Based Algorithm

AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

scholarly journals An Improved Crow Search Algorithm for Data Clustering

2020 ◽  
Vol 8 (1) ◽  
pp. 86-101 ◽  
Author(s):  
Vivi Nur Wijayaningrum ◽  
Novi Nur Putriwijaya
Keyword(s):  
Data Clustering ◽  
Search Algorithm ◽  
Search Space ◽  
Large Datasets ◽  
The Other ◽  
Computational Time ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
High Dimensions ◽  
Swarm Optimization

Metaheuristic algorithms are often trapped in local optimum solutions when searching for solutions. This problem often occurs in optimization cases involving high dimensions such as data clustering. Imbalance of the exploration and exploitation process is the cause of this condition because search agents are not able to reach the best solution in the search space. In this study, the problem is overcome by modifying the solution update mechanism so that a search agent not only follows another randomly chosen search agent, but also has the opportunity to follow the best search agent. In addition, the balance of exploration and exploitation is also enhanced by the mechanism of updating the awareness probability of each search agent in accordance with their respective abilities in searching for solutions. The improve mechanism makes the proposed algorithm obtain pretty good solutions with smaller computational time compared to Genetic Algorithm and Particle Swarm Optimization. In large datasets, it is proven that the proposed algorithm is able to provide the best solution among the other algorithms.

PSO-based group-oriented crow search algorithm (PGCSA)

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudeepa Das ◽  
Tirath Prasad Sahu ◽  
Rekh Ram Janghel
Keyword(s):  
Design Methodology ◽  
Search Algorithm ◽  
Search Space ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Content Type ◽  
Engineering Problems ◽  
Novel Approaches ◽  
Two Phases

Purpose The purpose of this paper is to modify the crow search algorithm (CSA) to enhance both exploration and exploitation capability by including two novel approaches. The positions of the crows are updated in two approaches based on awareness probability (AP). With AP, the position of a crow is updated by considering its velocity, calculated in a similar fashion to particle swarm optimization (PSO) to enhance the exploiting capability. Without AP, the crows are subdivided into groups by considering their weights, and the crows are updated by conceding leaders of the groups distributed over the search space to enhance the exploring capability. The performance of the proposed PSO-based group-oriented CSA (PGCSA) is realized by exploring the solution of benchmark equations. Further, the proposed PGCSA algorithm is validated over recently published algorithms by solving engineering problems. Design/methodology/approach In this paper, two novel approaches are implemented in two phases of CSA (with and without AP), which have been entitled the PGCSA algorithm to solve engineering benchmark problems. Findings The proposed algorithm is applied with two types of problems such as eight benchmark equations without constraint and six engineering problems. Originality/value The PGCSA algorithm is proposed with superior competence to solve engineering problems. The proposed algorithm is substantiated hypothetically by using a paired t-test.

scholarly journals Illuminating Elite Patches of Chemical Space

2020 ◽  
Author(s):  
Jonas Verhellen ◽  
Jeriek Van den Abeele
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
State Of The Art ◽  
Chemical Space ◽  
Search Space ◽  
Robot Design ◽  
Soft Robot ◽  
Learning Approaches ◽  
High Performing ◽  
The Past

In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.

scholarly journals Illuminating Elite Patches of Chemical Space

2020 ◽  
Author(s):  
Jonas Verhellen ◽  
Jeriek Van den Abeele
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
State Of The Art ◽  
Chemical Space ◽  
Search Space ◽  
Robot Design ◽  
Soft Robot ◽  
Learning Approaches ◽  
High Performing ◽  
The Past

In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.

A New Genetic Algorithm Based on Optimal Solution Orientation

2010 ◽  
Vol 139-141 ◽  
pp. 1779-1784
Author(s):  
Quan Wang ◽  
Jin Chao Liu ◽  
Pan Wang ◽  
Juan Ying Qin
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Local Domain ◽  
Local Search Algorithm ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Optimum Solution ◽  
Standard Genetic Algorithm

Many researchers have indicated that standard genetic algorithm suffers from the dilemma---premature or non-convergence. Most researchers focused on finding better search strategies, and designing various new heuristic methods. It seemed effective. From another view, we can transform search space with a samestate-mapping. A special genetic algorithm applied to the new search space would achieve better performance. Thus, we present a new genetic algorithm based on optimal solution orientation. In this paper, a new genetic algorithm based on optimum solution orientation is presented. The algorithm is divided into "optimum solution orientation" phase and "highly accurately searching in local domain of global optimal solution" phase. Theoretical analysis and experiments indicate that OSOGA can find the "optimal" sub domain effectively. Cooperating with local search algorithm, OSOGA can achieve highly precision solution with limited computing resources.

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