Computational Intelligence and Mathematics for Tackling Complex Problems

2020 ◽  
Keyword(s):  
Computational Intelligence ◽  
Complex Problems ◽  
And Mathematics

Hybrid Computational Intelligence and the Basic Concepts and Recent Advances

2018 ◽  
pp. 180-190
Author(s):  
Georgios Dounias
Keyword(s):  
Computational Intelligence ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Hybrid Intelligent Systems ◽  
Complex Problems ◽  
Recent Advances ◽  
New Findings ◽  
Wide Range ◽  
Computational Intelligent ◽  
Computational Technology

In this paper computational intelligence and its major methodologies are introduced in the first place, and then hybrid intelligent systems are defined and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade, is the result of the extensive success of these systems in a wide range of real-world complex problems, but also has to do with the increased capabilities of computational technology. One of the reasons for this success has to do with the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks, genetic algorithms, or other intelligent algorithms and techniques. Each of the partial methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. The paper includes recent advances and new findings in the area of hybrid computational intelligence.

scholarly journals Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Laszlo T. Koczy ◽  
Jesus Medina ◽  
Marek Reformat ◽  
Kok Wai Wong ◽  
Jin Hee Yoon
Keyword(s):  
Complex Systems ◽  
Computational Intelligence ◽  
Complex Problems

scholarly journals A Physics Based Novel Approach for Travelling Tournament Problem: Optics Inspired Optimization

2019 ◽  
Vol 48 (3) ◽  
pp. 373-388 ◽  
Author(s):  
Bilal Alatas ◽  
Harun Bingol
Keyword(s):  
Computational Intelligence ◽  
Search Space ◽  
Complex Problems ◽  
Candidate Solution ◽  
Novel Approach ◽  
Light Point ◽  
Convex Mirror ◽  
Total Movement ◽  
Computational Intelligence Methods ◽  
First Time

Computational intelligence search and optimization algorithms have been efficiently adopted and used for many types of complex problems. Optics Inspired Optimization (OIO) is one of the most recent physics inspired computational intelligence methods which treats the search space of the problem to be optimized as a wavy mirror in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each candidate solution is treated as an artificial light point that its glittered ray is reflected back by the search space of the problem and the artificial image is formed based on mirror equations adopted from Optics, as a new candidate solution. In this study, OIO for the first time has been designed as solution search strategy for travelling tournament problem which is one of the current sports problems and aids to minimize transportation and total movement of teams. Furthermore, this problem has been firstly solved by League Championship Algorithm and obtained results from both synthetic and real datasets have been compared in this study for the first time. Obtained results show the superiority of OIO which is a novel algorithm and seems to efficiently solve many complex problems.

scholarly journals Development of the Sci-math Sensemaking Framework: categorizing sensemaking of mathematical equations in science

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
FangFang Zhao ◽  
Anita Schuchardt
Keyword(s):  
Scientific Knowledge ◽  
Teaching And Learning ◽  
Review Of The Literature ◽  
Complex Problems ◽  
Making Sense ◽  
Mathematics Knowledge ◽  
Mathematical Equations ◽  
And Mathematics ◽  
Scientific Ideas

AbstractScientific ideas are often expressed as mathematical equations. Understanding the ideas contained within these equations requires making sense of both the embedded mathematics knowledge and scientific knowledge. Students who can engage in this type of blended sensemaking are more successful at solving novel or more complex problems with these equations. However, students often tend to rely on algorithmic/procedural approaches and struggle to make sense of the underlying science. This deficit may partly be the fault of instruction that focuses on superficial connections with the science and mathematics knowledge such as defining variables in the equation and demonstrating step-by-step procedures for solving problems. Research into the types of sensemaking of mathematical equations in science contexts is hindered by the absence of a shared framework. Therefore, a review of the literature was completed to identify themes addressing sensemaking of mathematical equations in science. These themes were compiled into nine categories, four in the science sensemaking dimension and five in the mathematics sensemaking dimension. This framework will allow for comparison across studies on the teaching and learning of mathematical equations in science and thus help to advance our understanding of how students engage in sensemaking when solving quantitative problems as well as how instruction influences this sensemaking.

scholarly journals Mining and crafting a game to teach research methodology

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Carl Marnewick ◽  
Jacqui Chetty
Keyword(s):  
Research Methodology ◽  
Complex Problems ◽  
Science Technology ◽  
Traditional Lectures ◽  
And Mathematics ◽  
Game Elements

AbstractGamification is used in various disciplines to elucidate complex problems. These disciplines are typically the science, technology, engineering, and mathematics disciplines. It is not known whether gamification can be used to teach research methodology. MinecraftEDU was used to create games to explain the various concepts within research methodology. The purpose was to force students to engage with the theory and literature and create a game based on their insights. An analysis of the students’ feedback indicates that they preferred this method to more traditional lectures. Although they experienced initial problems with the MinecraftEDU environment, the overall experience was perceived as positive. The results indicate that gamification can be used to teach research methodology, but more research is needed to determine how game elements can be incorporated into a research methodology game.

Coupling of Optimization Algorithms Based on Swarm Intelligence

2018 ◽  
pp. 27-50 ◽  
Author(s):  
Kamalanand Krishnamurthy ◽  
Mannar Jawahar Ponnuswamy
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Computational Intelligence ◽  
Biomedical Engineering ◽  
Optimization Algorithms ◽  
Bacterial Foraging Optimization ◽  
Complex Problems ◽  
Bacterial Foraging

Swarm intelligence is a branch of computational intelligence where algorithms are developed based on the biological examples of swarming and flocking phenomena of social organisms such as a flock of birds. Such algorithms have been widely utilized for solving computationally complex problems in fields of biomedical engineering and sociology. In this chapter, two different swarm intelligence algorithms, namely the jumping frogs optimization (JFO) and bacterial foraging optimization (BFO), are explained in detail. Further, a synergetic algorithm, namely the coupled bacterial foraging/jumping frogs optimization algorithm (BFJFO), is described and utilized as a tool for control of the heroin epidemic problem.

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