Cloud based Financial Market Prediction through Genetic Algorithms: A Review

Soni, Nitasha; Kumar, T. Suresh

doi:10.5120/ijca2015905413

Cited by 4 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Permutation mutation method involves selecting two positions in the chromosome, at random, and swapping the elements at those positions [46]. Scramble mutation method involves selecting two positions in the chromosome at random and scrambling the elements between those positions [47]. At last, the Reset mutation method involves selecting a position in the chromosome at random and resetting the element at that position to a specified value [48], however for this research, it was adopted to work with 2 random positions in the R method, and the new value is randomly generated inside the search space.…”

Section: F Mutation Function Methods Choicementioning

confidence: 99%

Adaptive Radiation as an Autotuning Strategy for Genetic Algorithms on Dynamic Problems

Costa,

Oliveira

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

As optimization processes have become more complex for embracing problems of different characteristics, it is necessary to have multiple adequate algorithms to tackle each of such problems. Bio-inspired evolutionary algorithms are suitable solutions for these situations, but they require re-tuning when working with different systems. Autotuning is a popular strategy to increase the adaptability of optimization algorithms. Adaptive Radiation (AR) is a phenomenon in nature that optimizes a population by diversity increase and niche specialization through intense mutation. This research aimed to insert this effect into the Genetic Algorithm (GA) workflow as a biological-inspired autotuning method, creating a new model called Genetic Algorithm with Adaptive Radiation (GAAR). The implementation of the AR component resulted in consistent and improved results on multiple benchmark functions from the CEC2019 challenge. The GAAR only changes the value of the AR component, which is enough to make this model achieve the best results in 57% of the tests and the worst results in 0% of the tests, while the Adaptive Particle Swarm Optimization (APSO) presented 39% and 12% of the best and worst results, respectively.

show abstract

Section: F Mutation Function Methods Choicementioning

confidence: 99%

Adaptive Radiation as an Autotuning Strategy for Genetic Algorithms on Dynamic Problems

Costa,

Oliveira

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

show abstract

“…One-point, two-point, order, order-2, best-order, position crossover 28 , and even crossover 29 operations are incorporated. At the same time, twors, insertion, inversion 30 and complement mutation 29 operations are implemented. Further, performance and results of each combination is analyzed.…”

Section: Optimization With Best Operator Combination Genetic Algorithmmentioning

confidence: 99%

Fast Power Density Aware 3D-IC Floorplanning for Hard Macro-Blocks Using Best Operator Combination Genetic Algorithm

Meitei

Baishnab

Trivedi

2023

Preprint

View full text Add to dashboard Cite

In this article, we propose a fast 3D-IC floorplanning method for hard macro-blocks that includes a thermal management scheme. It applies a genetic algorithm constituted by an optimal combination of crossover and mutation operations to identify the optimal solution for design variables, namely, total wire length, number of through-silicon vias (TSVs), and maximum average layer power density. The proposed method additionally makes use of a unique TSV placement scheme that arranges TSVs next to their respective functional blocks. To enable efficient heat transmission to the ambient environment, layers with higher power densities are placed closer to the heat sink. The proposed 3D-IC floorplanning approach provides the fewest TSVs, the lowest peak temperature, and promising values of wire length within the least amount of computation time. Compared to the recent fast thermal analysis for fixed-outline 3D-floorplanning, it generates 13.14% shorter wire length, 39.27% lower peak temperature, and 34.35% lesser number of TSVs on average with significant improvement in computation time, while analyzing GSRC thermal benchmark circuits.

show abstract

“…Therefore, scientific decisions will be made on business improvement, risk control and accurate marketing. [5]…”

Section: Real Time Processing Of Structured and Unstructured Datamentioning

confidence: 99%

The Impact of Cloud Computing and Big Data Technology on Digitalization in Financial Industry

2023

FERM

View full text Add to dashboard Cite

As synthesizer of information technologies, cloud computing and big data produce a profound impact on all walks of life. Therefore, financial industry should tread on the heels of times and explore reform paths. Under the background, this study analyzes how reform of traditional financial industry is affected by cloud computing and big data technology. Moreover, in view of shortcomings of traditional finance, it narrates importance and significance of cloud computing and big data technology. Finally, it proposes application fields and prospects of cloud computing and big data technology in financial industry.

show abstract

Cloud based Financial Market Prediction through Genetic Algorithms: A Review

Cited by 4 publications

References 11 publications

Adaptive Radiation as an Autotuning Strategy for Genetic Algorithms on Dynamic Problems

Adaptive Radiation as an Autotuning Strategy for Genetic Algorithms on Dynamic Problems

Fast Power Density Aware 3D-IC Floorplanning for Hard Macro-Blocks Using Best Operator Combination Genetic Algorithm

The Impact of Cloud Computing and Big Data Technology on Digitalization in Financial Industry

Contact Info

Product

Resources

About