A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare

Dispersed differential hunger games search for high dimensional gene data feature selection

The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.

Worldwide Prevalence of Alcohol Use in Non-Fatally Injured Motor Vehicle Drivers: A Systematic Review and Meta-Analysis

Drunk driving is an important risk factor significantly contributing to traffic accidents and their associated lethality. This meta-analysis of observational studies aims to provide the estimates of drunk driving prevalence in non-lethally injured motor vehicle drivers in relation to the world region, blood alcohol concentration (BAC), and quality of the primary study. A systematic search for observational studies that examined the prevalence of drunk driving in injured drivers was performed, and 17 studies comprising 232,198 drivers were included in the pooled analysis. The pooled prevalence of drunk driving in injured drivers was found to be 16.6% (95% CI: 12.8-20.3%; I² = 99.87%, p < 0.001). In addition, the prevalence of alcohol use ranged from 5.5% (95% CI: 0.8-10.1%) in the Middle East, North Africa, and Greater Arabia region to 30.6% (95% CI: 24.6-36.5%) in the Asia region. As for the subgroups with different thresholds of BAC, the maximum value of 34.4% (95% CI: 28.5-40.3%) was found for a dose of 0.3 g/L. The prevalence of alcohol use reported by high-quality studies was 15.7% (95% CI: 11.1-20.3%), compared to 17.7% (95% CI: 11.3-24.2%) reported by studies of moderate quality. These findings could inform law enforcement efforts to promote road safety.

Font Design in Visual Communication Design of Genetic Algorithm

The text has played an essential role in the advancement of human civilization. It is used now as a valuable cultural heritage that has been experienced for a thousand years. The passing text must have its irreplaceable advantages and charm. People are extremely sensitive to visual symbols, and it is also the first step in which people can know things. Most of the books in today's market focus on pictures and colors, which ignores the design of the text. This makes the text boring taste, causing the reader's visual fatigue, which is not conducive to readers absorbing information through books. Therefore, this paper studies the font design in the design of the visual algorithm based on the genetic algorithm, and the font design is analyzed by the particle swarming algorithm, the decision MIMO-SCMA system of the genetic algorithm. It aims to address the dryness of today's texts by constructing texts that are instantly recognizable and visually appealing to readers. Through innovative visual concepts, readers can enjoy the process of acquiring knowledge. In this paper, to investigate the effectiveness of the genetic algorithm in font design, the number of experimental research subjects was set to 300, and 280 valid questionnaires were collected to investigate the satisfaction of users with the newly designed fonts. Experiments showed that the visual communication design based on a genetic algorithm has increased by 6.52% for the design satisfaction and the number of fonts that use the system is also increasing.

An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm

Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning methods which are time-consuming during the training phase, ELM-RBF (extreme learning machine-radial basis function) is more efficient and has become a research hotspot in multi-label classification. However, because of the lack of effective optimization methods, conventional extreme learning machines are always unstable and tend to fall into local optimum, which leads to low prediction accuracy in practical applications. To this end, a modified ELM-RBF with a synergistic adaptive genetic algorithm (ELM-RBF-SAGA) is proposed in this paper. In ELM-RBF-SAGA, we present a synergistic adaptive genetic algorithm (SAGA) to optimize the performance of ELM-RBF. In addition, two optimization methods are employed collaboratively in SAGA. One is used for adjusting the range of fitness value, the other is applied to update crossover and mutation probability. Sufficient experiments show that ELM-RBF-SAGA has excellent performance in multi-label classification.

A Novel Urban Tourism Path Planning Approach Based on a Multiobjective Genetic Algorithm

One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.

A review on genetic algorithm: past, present, and future

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.