scholarly journals Evolutionary Machine Learning: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-35
Author(s):  
Akbar Telikani ◽  
Amirhessam Tahmassebi ◽  
Wolfgang Banzhaf ◽  
Amir H. Gandomi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Optimization Problems ◽  
Ensemble Methods ◽  
Problem Formulation ◽  
Fitness Value ◽  
Future Work ◽  
Rule Optimization

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.

ЛІДЕРСЬКІ ЯКОСТІ МАЙБУТНЬОГО СОЦІАЛЬНОГО ПЕДАГОГА ЯК УМОВА УСПІШНОЇ ПРОФЕСІЙНОЇ ДІЯЛЬНОСТІ

2012 ◽  
Vol 33 ◽  
Author(s):  
К. В. Бабко
Keyword(s):  
Key Words ◽  
Social Workers ◽  
Leadership Skills ◽  
Leadership Quality ◽  
Social Pedagogue ◽  
Future Work

Автор статті робить спробу визначити особливості формування лідерських якостей майбутніх соціальних педагогів, а також роль цих якостей у майбутній професійній діяльності.Ключові слова: лідерство, лідерські якості, соціальний педагог.  Author of the article makes an attempt to determine the features of the formation of leadership quality of future social workers, as well as the role of these qualities in future work professionally. Key words: leadership, leadership skills, social pedagogue.

scholarly journals Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

2020 ◽  
Author(s):  
Tomohiro Harada ◽  
Misaki Kaidan ◽  
Ruck Thawonmas
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computing Time ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evaluation Time ◽  
Surrogate Function

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

The Role of Machine Learning in Building Image Interpretation Systems

1997 ◽  
Vol 11 (01) ◽  
pp. 143-168 ◽  
Author(s):  
Terry Caelli ◽  
Walter F. Bischof
Keyword(s):  
Machine Learning ◽  
Data Structures ◽  
Image Interpretation ◽  
Problem Formulation ◽  
Data Models ◽  
New Techniques ◽  
Relational Structures ◽  
Complex Scenes ◽  
Rule Evaluation

Machine learning has been applied to many problems related to scene interpretation. It has become clear from these studies that it is important to develop or choose learning procedures appropriate for the types of data models involved in a given problem formulation. In this paper, we focus on this issue of learning with respect to different data structures and consider, in particular, problems related to the learning of relational structures in visual data. Finally, we discuss problems related to rule evaluation in multi-object complex scenes and introduce some new techniques to solve them.

scholarly journals P.013 Machine Learning on Drawing Tests of Cognition: A Systematic Review

2021 ◽  
Vol 48 (s3) ◽  
pp. S23-S23
Author(s):  
R Kamhawy ◽  
R Mcginn ◽  
H He ◽  
J Ho ◽  
M Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Inclusion Criteria ◽  
Quality Of Evidence ◽  
Drawing Test ◽  
Published Evidence ◽  
Full Text Review ◽  
Grade Methodology ◽  
Future Work

Background: Machine learning (ML) methods hold promise in allowing early detection of dementia. We performed a systematic review to assess the quality of published evidence for using ML methods applied to drawing tests of cognition, and to describe the accuracy of the methods. Methods: Embase, Medline, and Cochrane Central Library databases were searched for potential studies up to December 8, 2018 by four independent reviewers. Included articles satisfied the following criteria: 1) use of ML on 2) a drawing test in order to 3) assess cognition. The quality of evidence was then assessed using GRADE methodology. Results: The initial search yielded 4620 citations. Of these, 64 were eligible for full text review. 18 articles then met inclusion criteria. Median AUC across all models was 0.765, with certain ML algorithms performing better in terms of AUC or diagnostic accuracy. However, based on GRADE, the quality of evidence was deemed very low. Conclusions: ML has been applied by several groups to drawing tests of cognition. The quality of evidence is currently too low to make recommendations on their use. Future work must focus on improving reporting, and using standard algorithms and larger, more diverse datasets to improve comparability and generalizability.

scholarly journals ARGUMENTATION-BASED COMPUTER SUPPORTED COLLABORATIVE LEARNING (ABCSCL): THE ROLE OF INSTRUCTIONAL SUPPORTS

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Omid Noroozi
Keyword(s):  
Collaborative Learning ◽  
Relevant Literature ◽  
Learning Processes ◽  
Teaching Approach ◽  
Computer Supported Collaborative Learning ◽  
Instructional Supports ◽  
Quality Of Learning ◽  
Future Work

This paper investigates the role of instructional supports for argumentation-based computer supported collaborative learning (ABCSCL), a teaching approach that improves the quality of learning processes and outcomes. Relevant literature has been reviewed to identify the instructional supports in ABCSCL environments. A range of instructional supports in ABCSCL is proposed including scaffolding, scripting, and representational tools. Each of these instructional supports are discussed in detail. Furthermore, the extent to which and the way in which such instructional supports can be applied in ABCSCL environments are discussed. Finally, suggestions for future work and implications for the design of ABCSCL environments are provided.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0610/a.php" alt="Hit counter" /></p>

scholarly journals Improving Lagrange Dual Bounds for Quadratic Extremal Problems

2020 ◽  
pp. 15-22
Author(s):  
Oleg Berezovskyi
Keyword(s):  
Lagrangian Relaxation ◽  
Optimization Problems ◽  
Problem Formulation ◽  
Extremal Problems ◽  
General Concept ◽  
Duality Gap ◽  
Sdp Relaxation ◽  
Dual Bound ◽  
Dual Bounds

Introduction. Due to the fact that quadratic extremal problems are generally NP-hard, various convex relaxations to find bounds for their global extrema are used, namely, Lagrangian relaxation, SDP-relaxation, SOCP-relaxation, LP-relaxation, and others. This article investigates a dual bound that results from the Lagrangian relaxation of all constraints of quadratic extremal problem. The main issue when using this approach for solving quadratic extremal problems is the quality of the obtained bounds (the magnitude of the duality gap) and the possibility to improve them. While for quadratic convex optimization problems such bounds are exact, in other cases this issue is rather complicated. In non-convex cases, to improve the dual bounds (to reduce the duality gap) the techniques, based on ambiguity of the problem formulation, can be used. The most common of these techniques is an extension of the original quadratic formulation of the problem by introducing the so-called functionally superfluous constraints (additional constraints that result from available constraints). The ways to construct such constraints can be general in nature or they can use specific features of the concrete problems. The purpose of the article is to propose methods for improving the Lagrange dual bounds for quadratic extremal problems by using technique of functionally superfluous constraints; to present examples of constructing such constraints. Results. The general concept of using functionally superfluous constraints for improving the Lagrange dual bounds for quadratic extremal problems is considered. Methods of constructing such constraints are presented. In particular, the method proposed by N.Z. Shor for constructing functionally superfluous constraints for quadratic problems of general form is presented in generalized and schematized forms. Also it is pointed out that other special techniques, which employ the features of specific problems for constructing functionally superfluous constraints, can be used. Conclusions. In order to improve dual bounds for quadratic extremal problems, one can use various families of functionally superfluous constraints, both of general and specific type. In some cases, their application can improve bounds or even provide an opportunity to obtain exact values of global extrema.

Meeting the Needs of All Cultureless Learners: Culture Discourse and Quality Assumptions in Personalized Learning Research

2021 ◽  
Vol 45 (1) ◽  
pp. 372-407
Author(s):  
Ekaterina Strekalova-Hughes ◽  
Kindel T. Nash ◽  
Bevin Schmer ◽  
Karnissa Caldwell
Keyword(s):  
Relevant Literature ◽  
Deep Understanding ◽  
Problem Formulation ◽  
Personalized Learning ◽  
Research Process ◽  
Theory Of Change ◽  
School Settings ◽  
Evidence Analysis

This chapter reviews recent qualitative studies on personalized learning in middle/secondary school settings to analyze the role of culture in how this concept is enacted and researched. Personalized learning is posited as a pedagogical approach that aims to revolutionize schooling and challenge educational inequity by foregrounding learners’ agency in what and how they learn, tailoring pedagogy and its purpose to learners’ unique interests, needs, and abilities. Given the strong emphasis of the approach on the uniquenesses of the persons who are learning, our analysis interrogates the discourse on culture in studies on personalized learning and extrapolates how this discourse informs problem formulation, design and logic, sources of evidence, analysis and interpretation, and implications for practice. This review reveals a disconnect between the relevant literature on culture in learning and omissions of researchers and research participants’ cultural positionalities and identities. This appears to affect the quality of educational evidence, inhibiting a deep understanding of the implementation of the personalized learning approach for different communities of learners. We assert that research into practices that intend to meet the needs of diverse learners should center learner and researcher cultures and positionalities as part of a theory of change that permeates the entire research process.

Review of Metamodeling Techniques in Support of Engineering Design Optimization

2006 ◽  
Author(s):  
G. Gary Wang ◽  
S. Shan
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Design Space Exploration ◽  
Optimization Problems ◽  
Problem Formulation ◽  
Model Approximation ◽  
Design Problems ◽  
Computation Efficiency ◽  
Modern Engineering

Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.

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