Genetic algorithm

Abstract- SVM(Support vector machine) is Mainly established for linear two-class classification through building an optimal splitting hyper plane, here the margin is maximized. SVM is useful for kernel trick to map the novel input space into a high dimensional feature space to improve the classifier generalization ability when the training data is not linear splitable. GA(Genetic Algorithm) is a stochastic and the empirical searching algorithm that is stimulated by natural evolution. The candidate solutions are encoded to a group of strings (called chromosomes) by some kind of encoding method in the evolution.The best candidate solution is obtained after a series of iterative GA computations Based on Darwins principle of “Survival of the fittest".…

4.1 The GABC Approach for Image Segmentation The GABC is the Genetic Artificial Bee Colony Algorithm. The proposed algorithm is the implementation of ABC algorithm with the key concept of GA technique.The GABC is actually the hybrid approach of Artificial Bee Colony Algorithm and the Genetic Algorithm.In this method we extend Genetic Algorithm with Artificial Bee Colony operators i:e Employed Bees and Onlooker Bees to improve the solution space named as Genetic Artificial Bee Colony Algorithm…

GA is search technique that is used in computing to get true or appropriate solutions to optimization and search problems. Inheritance, Mutation, Selection and crossover methods may apply for designing algorithms. Genetic algorithm is implemented as computer stimulation where the population of abstract representations of applicant solutions to optimization problem evolves towards the better solutions.Fig1 (d) shows the genetic algorithm phases. Basically it works on the principle of survival of…

ABSTRACT: Genetic Algorithm is one of the global optimization schemes that have gained popularity as a means to attain water resources optimization. It is an optimization technique, based on the principle of natural selection, derived from the theory of evolution, is used for solving optimization problems. In the present study Genetic Algorithm (GA) has been used to develop a policy for optimizing the release of water for the purpose of irrigation. The study area is Sukhi Reservoir project in…

Abstract—Feature selection, used as a preprocessing step, can reduce the dimensionality of data and thereby increase the efficiency, accuracy, and clarity of learning systems. However feature selection can be costly endeavour. This paper proposes two new feature selection algorithms, based on binary particle swarm optimisation, with the aim of reducing running time without affecting classification accuracy by combining filter and wrapper approaches. The first algorithm proceeds cautiously by…

of the algorithmic processes is a good example of what can be attained using algorithms. This paper is bent on focusing on practical differences in which algorithms are used in solving problems (phishing) through in-depth understanding of the problem and then formulating it as well as using comparative analysis in the same. Introduction Algorithm brings together values taken up as input which produces some other value or a set of values which implies that they are paths used to attain a given…

J Intell Robot Syst (2013) 69:131–146 137 for optimum performance in an iterative fashion. Lastly, from the two concepts, one of them is se- lected in terms of performance, manufacturability and cost. The initial analysis of the wing geometry de- sign process is performed by using the program XFLR5 . XFLR5 utilizes Vortex-Lattice-Method which gives preliminary results from which the configurations can be compared. After the optimization of these two different wing…

October 14, 2015 Olga Naomi Sokolova Why is there redundancy in the genetic code? Does this seem like a useful feature of the genetic code? Why or why not? There is redundancy because there are more codons than there are amino acids, so quite a few amino acids are represented by more than one codon. This is beneficial because if there is a mistake during transcription that damages one of the codons, a visible and detrimental mutation may not occur because all of the others would still be able…

A COMPARITIVE STUDY OF NEAREST NEIGHBOUR ALGORITHM AND GENETIC ALGORITHM IN SOLVING TRAVELLING SALESMAN PROBLEM Ajaz Ahmed Khan Electronics and communication department SSGI FET Bhilai, India ajz70277@gmail.com Mrs. Himani Agrawal Electronics and communication department SSGI FET Bhilai, India Abstract—In this paper, we have used two algorithms, i.e. the Nearest Neighbor algorithm and Genetic Algorithm to solve the Travelling Salesman problem. The Travelling Salesman…

Traveling Salesman Problem (TSP) is one of combinatorial optimization problems. X TSP is NP-hard problem which defined as a set of cities and each city should be visited once with minimum tour length. This paper solved this problem using Firefly Algorithm (FA) and k-means clustering by three steps: cluster the nodes, finding optimal path in each cluster and connect the clusters. The first step is to divide all nodes into sub-problems using k-means clustering, the second step is to use FA to find…