scholarly journals Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Michael A. Lones
Keyword(s):  
Evolutionary Algorithms ◽  
Algorithm Design ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Metaheuristic Algorithms ◽  
Future Directions ◽  
Nature Inspired Algorithms ◽  
Different Sources ◽  
Nature Inspired Metaheuristics

AbstractIn recent years, a plethora of new metaheuristic algorithms have explored different sources of inspiration within the biological and natural worlds. This nature-inspired approach to algorithm design has been widely criticised. A notable issue is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This makes it difficult to both comprehend how these algorithms work and understand their relationships to other metaheuristics. This paper attempts to address this issue, at least to some extent, by providing accessible descriptions of the most cited nature-inspired algorithms published in the last 20 years. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation, and finishes with a discussion of future directions for the field.

scholarly journals Geometric Particle Swarm Optimization

2008 ◽  
Vol 2008 ◽  
pp. 1-14 ◽  
Author(s):  
Alberto Moraglio ◽  
Cecilia Di Chio ◽  
Julian Togelius ◽  
Riccardo Poli
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Particle Swarm ◽  
Combinatorial Problem ◽  
Particle Swarm Optimisation ◽  
Experimental Results ◽  
Geometric Framework ◽  
Swarm Optimization ◽  
Intimate Connection ◽  
Solution Representation

Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new Geometric PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinatorial spaces. The Sudoku puzzle is a perfect candidate to test new algorithmic ideas because it is entertaining and instructive as well as being a nontrivial constrained combinatorial problem. We apply GPSO to solve the Sudoku puzzle.

Frequencies Wave Sound Particle Swarm Optimisation (FPSO)

2021 ◽  
pp. 1-32
Author(s):  
Ahmad K. Al Hwaitat ◽  
Rizik M. H. Al-Sayyed ◽  
Imad K. M. Salah ◽  
Saher Manaseer ◽  
Hamed S. Al-Bdour ◽  
...  
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimisation

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Will mesenchymal stem cells be future directions for treating radiation-induced skin injury?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhuoqun Fang ◽  
Penghong Chen ◽  
Shijie Tang ◽  
Aizhen Chen ◽  
Chaoyu Zhang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Malignant Tumors ◽  
Skin Injury ◽  
Future Directions ◽  
Cytokines And Chemokines ◽  
Radiation Induced ◽  
The Common ◽  
Apoptotic Activity ◽  
Different Sources

AbstractRadiation-induced skin injury (RISI) is one of the common serious side effects of radiotherapy (RT) for patients with malignant tumors. Mesenchymal stem cells (MSCs) are applied to RISI repair in some clinical cases series except some traditional options. Though direct replacement of damaged cells may be achieved through differentiation capacity of MSCs, more recent data indicate that various cytokines and chemokines secreted by MSCs are involved in synergetic therapy of RISI by anti-inflammatory, immunomodulation, antioxidant, revascularization, and anti-apoptotic activity. In this paper, we not only discussed different sources of MSCs on the treatment of RISI both in preclinical studies and clinical trials, but also summarized the applications and mechanisms of MSCs in other related regenerative fields.

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