An Efficient Approach towards Network Routing using Genetic Algorithm

The network field has been very popular in recent times and has aroused much of the attention of researchers. The network must keep working with the varying infrastructure and must adapt to rapid topology changes. Graphical representation of the networks with a series of edges varying over time can help in analysis and study. This paper presents a novel adaptive and dynamic network routing algorithm based on a Regenerate Genetic Algorithm (RGA) with the analysis of network delays. With the help of RGA at least a very good path, if not the shortest one, can be found starting from the origin and leading to a destination. Many algorithms are devised to solve the shortest path (SP) problem for example Dijkstra algorithm which can solve polynomial SP problems. These are equally effective in wired as well as wireless networks with fixed infrastructure. But the same algorithms offer exponential computational complexity in dealing with the real-time communication for rapidly changing network topologies. The proposed genetic algorithm (GA) provides more efficient and dynamic solutions despite changes in network topology, network change, link or node deletion from the network, and the network volume (with numerous routes)

Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.Comment: 14 Pages, 11 figure

A Comparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

Search engine optimization applies search principles in search engines to assign a higher ranking to the most suitable webpage.  Nowadays, information searching is done ubiquitously on the World Wide Web with the help of search engines. However, the process needs to be efficient and produces accurate results at the same time. In this research, the objectives are to implement and evaluate the Artificial Neural Network and Genetic Algorithms. The accuracy result for both algorithms is compared by implementing keyword ranking, Search Engine Result Page visibility and time retrieval for document-based and e-commerce websites. To achieve them, firstly the problem and data are defined. Next, two datasets are imported from Kaggle and transformed into a more useful format. Then, the Artificial Neural Network and Genetic Algorithms are implemented on these datasets in Python using Jupyter Notebook tools. Subsequently, the accuracy of keyword ranking, Search Engine Result Page visibility and time retrieval for these datasets are observed based on the output and graph displayed. Lastly, an analysis of the results is performed. Conclusively, the Genetic Algorithm demonstrates a higher percentage of accuracy results than Artificial Neural Network algorithm in keyword ranking and SERP visibility. However, the accuracy results of time retrieval are vice versa. The results in Genetic Algorithm shows 9.0%, 9.0% and 3.0% in e-commerce dataset for keyword ranking and 4.0%, 51.0% and 1.0% in document-based dataset for SERP visibility. Next, Artificial Neural Network algorithm shows result 8.0%, 7.0% and 7.0% in e-commerce dataset and 3.0%, 50.0% and 4.0% in document-based dataset for time retrieval. Therefore, the results validated the ability of the Genetic Algorithm as one of the most applied algorithms in the search engine optimization field

THE USE OF NEURAL NETWORKS IN THE OPERATIONAL RISK DATA MODELING

In this article it is presented a proposal of improving the data analysis process of Operational Risk (OpRisk) assessment in the financial institutions, for the Loss Distribution Approach (LDA) method, using the Artificial Intelligence (AI). In the first part of the paper a substitute tool of the traditional model-based Autoregressive Moving Average (ARMA) is described, for analyzing and representing stochastic processes. An Artificial Neural Network (ANN) is particularly suitable for this challenge, especially when dealing with limited data sets. In this case, an ANN is able to operate model-free by extracting the pattern of the training data set and by learning from the data observed during the generalized delta rule back-propagation training. The proposed ANN is a time lagged Feed-Forward Network (FFN) with log-sigmoid activation function.Operational Risk, Advanced Measurement Approach, Loss Distribution Approach, Artificial Neural Networks, Genetic Algorithms

Spatio-Temporal Point Pattern Analysis Using Genetic Algorithms

The effectiveness of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely, and efficient manner upon an event’s occurrence. A typical methodology to deal with such a task is through the application of an appropriate location - allocation model. In such a case, however, the spatial distribution of demand although stochastic in nature and layout, when aggregated to a specific spatial reference unit, appears to be spatially structured or semi – structured. Aiming to exploit the above incentive, the spatial tracing and analysis of emergency incidents is achieved through the utilisation of Artificial Intelligence. More specifically, in the proposed approach, each location problem is dealt with at two interacting levels. Firstly, spatio-temporal point pattern of demand is analysed over time by a new genetic algorithm. The proposed genetic algorithm interrelates sequential events formulating moving objects and as a result, every demand point pattern is correlated both to previous and following events. Secondly, the approach provides the ability to predict, by means of an artificial neural network, how the pattern of demand will evolve and thus the location of supplying centres and/or vehicles can be optimally defined. The proposed neural network is also optimised through genetic algorithms. The approach is applied to Athens Metropolitan Area and the data come from Fire Department’s records for the years 2003-2004.

An evolutionary factor analysis computation for mining website structures

This paper explores website link structure considering websites as interconnected graphs and analyzing their features as a social network. Two networks have been extracted for representing websites: a domain network containing subdomains or external domains linked through the website and a page network containing webpages browsed from the root domain. Factor analysis provides the statistical methodology to adequately extract the main website profiles in terms of their internal structure. However, due to the large number of indicators, the task of selecting a representative subset of indicators becomes unaffordable. A genetic search of an optimum subset of indicators is proposed in this paper, selecting a multiobjective fitness function based on factor analysis results. The optimum solution provides a coherent and relevant categorization of website profiles, and highlights the possibilities of genetic algorithms as a tool for discovering new knowledge in the field of web miningMinisterio de Educación y Ciencia DPI2007- 60128Junta de Andalucía. Consejería de Innovación, Ciencia y Empresa P07-TIC-0262

Database Extract Information Using Genetic Algorithm and Sending Message in HL7 Formatted Using Back Propagation

To analysis the speed of sending message in Healthcare standard 7 with the use of back propagation in neural network. Various algorithms are define in back propagation in neural network we can use back propagation algorithm for sending message purpose. Genetic Algorithm are used to extract information and send these information with this algorithm appears to be fastest method for training moderate sized feed forward neural network. It has a very efficient mat lab implementation. The need of this algorithm are used for analysis, increase the speed of sending message faster and accurately and more efficiently