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About 10,000 papers for “Selection (genetic algorithm)”
Hossain, Ahmed · Figshare · 2022
This study aims to explore the coding of GA for variable selection. The selected variables will be later used to build a linear regression model and then report model performance. The study will consider three simple variations of GA based on population size (20, 50, 200) and then plot graphs to show whether any of these variations performed better than others in a statistically significant way.
Cited by 0Open access
Hossain, Ahmed · Figshare · 2022
This study aims to explore the coding of GA for variable selection. The selected variables will be later used to build a linear regression model and then report model performance. The study will consider three simple variations of GA based on population size (20, 50, 200) and then plot graphs to show whether any of these variations performed better than others in a statistically significant way.
Cited by 0Open access
Efe Precious Onakpojeruo, Berna Uzun, Leena R. David et al. · 2024
Genetic algorithms (GA) are search engines that either optimize or reduce predefined functions. The technique of selection is an important phase in GA. This research study aims to evaluate, compare, and rank the selection techniques in GA. The evaluated selection techniques are; roulette wheel selection, elitist selection, rank selection, tournament selection, truncation selection, Boltzmann selection, and stochastic Universal Sampling selection. The comparison was based on the following selected criteria; (performance accuracy, fitness value accuracy, execution time, preservation of diversity, computational cost, ease of use, and bias levels. The paper incorporates the aforementioned criteria into the fuzzy preference ranking organization method for enrichment evaluation (PROMETHEE). This decision-making tool was used to determine the most preferred selection techniques in GA. The results from this research study showed that with an outranking net flow of 0.0690 elitism was determined as the most effective selection technique for GA based on the given criteria and their importance levels. Followed by the tournament selection technique with a positive net flow of 0.0343 and then, the rank selection technique, roulette wheel selection technique, truncation selection technique, and stochastic universal sampling selection technique occupied the third, fourth, fifth, and sixth positions with a net flow of 0.0078, -0.0102, -0.0116, -0.0156. Boltzmann selection technique ranked least among the considered selection techniques with a net flow of -0.0738 due to its features with all the criteria. With this study, we have provided a supportive tool for the decision-makers in the selection of the genetic algorithm techniques, and we have shown the applicability of the fuzzy PROMETHEE approach in this case by providing the advantages and disadvantages of each decision point.
Cited by 6
Zhe Chen, Gaowei Yan, Gang Xie et al. · 2008
Genetic Algorithm (GA), a programming technique that mimics natural evolution as a problem-solving strategy, has become popular since its appearance. It keeps the properties similar to natural selection systems. Many improved GAs has been proposed, however, natural selection essence is not changed. Granular Computing (GrC) brings a new thought for problem solving, it arose a fast growing interests in recent years. This paper build up GrC Model in traditional GA, propose Artificial Selection Algorithm (ASA), which realize intentional reproduction of individuals in a population that have desirable traits. ASA simulates artificial selection behavior of human beings to generate untold diversity in individuals, so as to help guarantee the global convergence and searching efficiency. Artificial selection is a contrast to natural selection, in which the random forces of nature determine becomes the combination of natural selection and artificial selection. The algorithm is validated by benchmark function optimization.
Cited by 2
A.J. Sobey, Przemyslaw Grudniewski · Bioinspiration & Biomimetics · 2018
Genetic algorithms are integral to a range of applications. They utilise Darwin's theory of evolution to find optimal solutions in large complex spaces such as engineering, to visualise the design space, artificial intelligence, for pattern classification, and financial modelling, improving predictions. Since the original genetic algorithm was developed, new theories have been proposed which are believed to be integral to the evolution of biological systems. However, genetic algorithm development has focused on mathematical or computational methods as the basis for improvements to the mechanisms, moving it away from its original evolutionary inspiration. There is a possibility that the new evolutionary mechanisms are vital to explain how biological systems developed but they are not being incorporated into the genetic algorithm; it is proposed that their inclusion may provide improved performance or interesting feedback to evolutionary theory. Multi-level selection is one example of an evolutionary theory that has not been successfully implemented into the genetic algorithm and these mechanisms are explored in this paper. The resulting multi-level selection genetic algorithm (MLSGA) is unique in that it has different reproduction mechanisms at each level and splits the fitness function between these mechanisms. There are two variants of this theory and these are compared with each other alongside a unified approach. This paper documents the behaviour of the two variants, which show a difference in behaviour especially in terms of the diversity of the population found between each generation. The multi-level selection 1 variant moves rapidly towards the optimal front but with a low diversity amongst its children. The multi-level selection 2 variant shows a slightly slower evolution speed but with a greater diversity of children. The unified selection exhibits a mixed behaviour between the original variants. The different performance of these variants can be utilised to provide specific solvers for different problem types when using the MLSGA methodology.
Cited by 19Open access
Lutfi Mohammed Omer Khanbary, Deo Prakash Vidyarthi · 2009
Genetic Algorithm (GA) is widely used for the number of optimization problems. For a large set of problems, GA provides the sub-optimal solution and thus the thrust to improve the GA for better result is on. In all the variants of the GA a selection operator guides the result towards the convergence. Selection, being a major component of the GA, attracts a good number of researchers with a new selection method. The current work introduces a variant of selection method in the GA that results in better improvement of the result. Genetic algorithm is modified by the new selection method that incorporates a problem specific knowledge in the new population generation. The GA with the new selection method is called as the Modified Genetic Algorithm (MGA). In the proposed selection method, not all the chromosomes of a generation are used for mating rather it is selected based on its goodness. Two threshold values, based on some prior knowledge, are used to classify the chromosomes. Efficacy of the proposed threshold method has been evaluated by conducting experiment and comparing the threshold method with some existing selection methods viz. sigma scaling, rank selection and tournament selection.
Cited by 7
Ting Kuo, Shu‐Yuen Hwang · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 1996
Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the "survival-of-the-fittest" principle, traditional genetic algorithms allocate more trials to above-average schemata. However, increasing the sampling rate of schemata that are above average does not guarantee convergence to a global optimum; the global optimum could be a relatively isolated peak or located in schemata that have large variance in performance. In this paper we propose a novel selection method, disruptive selection. This method adopts a nonmonotonic fitness function that is quite different from traditional monotonic fitness functions. Unlike traditional genetic algorithms, this method favors both superior and inferior individuals. Experimental results show that GAs using the proposed method easily find the optimal solution of a function that is hard for traditional GAs to optimize. We also present convergence analysis to estimate the occurrence ratio of the optima of a deceptive function after a certain number of generations of a genetic algorithm. Experimental results show that GAs using disruptive selection in some occasions find the optima more quickly and reliably than GAs using directional selection. These results suggest that disruptive selection can be useful in solving problems that have large variance within schemata and problems that are GA-deceptive.
Cited by 13
Soon Lee Lai, Mohammad Jalali Varnamkhasti · Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) · 2009
Cited by 0
Shirin Dehghan (19837936), Reza Rabiei (9138998), Hamid Choobineh (8476908) et al. · Figshare · 2024
<div><p>Introduction</p><p>IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial due to emotional, financial, and health-related implications for infertile couples. This study aimed to develop a model for predicting IVF outcome by comparing five machine-learning techniques.</p><p>Method</p><p>The research approached five prominent machine learning algorithms, including Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM), Recursive Partitioning and Regression Trees (RPART), and AdaBoost, in the context of IVF success prediction. The study also incorporated GA as a feature selection method to enhance the predictive models’ robustness.</p><p>Results</p><p>Findings demonstrate that AdaBoost, particularly when combined with GA feature selection, achieved the highest accuracy rate of 89.8%. Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. Ten crucial features, including female age, AMH, endometrial thickness, sperm count, and various indicators of oocyte and embryo quality, were identified as key determinants of IVF success.</p><p>Conclusion</p><p>These findings underscore the potential of machine learning and feature selection techniques to assist IVF clinicians in providing more accurate predictions, enabling tailored treatment plans for each patient. Future research and validation can further enhance the practicality and reliability of these predictive models in clinical IVF practice.</p></div>
Cited by 0Open access
Michael Potter, Ayberk yarkin Yildiz, Nishanth Marer Prabhu et al. · arXiv (Cornell University) · 2024
We empirically show that process-based Parallelism speeds up the Genetic Algorithm (GA) for Feature Selection (FS) 2x to 25x, while additionally increasing the Machine Learning (ML) model performance on metrics such as F1-score, Accuracy, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC).
Cited by 1Open access
Baracchini Paul, Guillebaud Claire, Kromm Francois-Xavier et al. · Zenodo (CERN European Organization for Nuclear Research) · 2015
The aim of this study is to propose a multi-material design method allowing a simultaneous selection of architectures and materials using genetic algorithm.
Cited by 0Open access
Baracchini Paul, Guillebaud Claire, Kromm Francois-Xavier et al. · Zenodo (CERN European Organization for Nuclear Research) · 2015
The aim of this study is to propose a multi-material design method allowing a simultaneous selection of architectures and materials using genetic algorithm.
Cited by 0Open access
Oluleye, B., Armstrong, L., Leng, J. et al. · Murdoch Research Repository (Murdoch University) · 2014
This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy.
Cited by 32Open access
sadathosseiny1367 · Zenodo (CERN European Organization for Nuclear Research) · 2026
Genetic algorithm–optimized feature selection for sEMG–IMU fusion, improving motion intention classification accuracy and robustness through compact, discriminative feature subsets.
Cited by 0Open access
sadathosseiny1367 · Zenodo (CERN European Organization for Nuclear Research) · 2026
Genetic algorithm–optimized feature selection for sEMG–IMU fusion, improving motion intention classification accuracy and robustness through compact, discriminative feature subsets.
Cited by 0Open access
Simone A. Ludwig · Zenodo (CERN European Organization for Nuclear Research) · 2012
Quality of Service (QoS) aware service selection of workflows is a very important aspect for service-oriented systems. The selection based on QoS allows the user to include also non-functional attributes in their query, such as availability and reliability. Several exact methods have been proposed in the past, however, given that the workflow selection problem is NP-hard, approximate algorithms can be used to find suboptimal solutions for requested workflows. Genetic algorithm is one such method that can find approximate solutions in the form of services selected. In this paper, we propose an improved version of the standard genetic algorithm approach by making use of the clonal selection principle from artificial immune systems. Experimental results show that the clonal selection based genetic algorithm achieves much higher fitness values for the workflow selection problem than standard genetic algorithm.
Cited by 23Open access
Jia Wei Zhang, Bo Li, Quanli Liu et al. · World Scientific Publishing Company eBooks · 2005
Cited by 0
Ghasem Mahdevar, Javad Zahiri, Mehdi Sadeghi et al. · Journal of Biomedical Informatics · 2010
Cited by 26
Pappa, Gisele L., Kaestner, Celso A.A. · Kent Academic Repository (University of Kent) · 2002
The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach. The algorithm’s main goal is to find the best subset of features that minimizes both the error rate and the size of the tree discovered by a classification algorithm, namely C4.5, using the Pareto dominance concept
Cited by 14
S. N. Omkar, R. Verma · 2008
Cricket is a team sport for two teams of eleven players each. Because of its tremendous popularity, it is really difficult to choose right players for a team of 15 or so. Moreover the human judgment cannot be relied upon. A system is formulated which takes into account various available performance data of players and gives an optimized and balanced team, with right number of batsmen, bowler, and wicket-keeper without any human interference, which is limited to entering performance data only. 1.
Cited by 0