Flowchart of GA

Ultimate guide to flowchart of genetic algorithm.
Simple answer to: what is ga? what are the Application of ga?

1. FLOW CHART OF GA made by, R.ISHWARIYA, M.sc(cs).,

2. GENETIC ALGORITHM

3. INTRODUCTION Genetic Algorithm  (GA) is a search-based optimization technique based on the principles of  Genetics  and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve.

4. What are genetic algorithm? Nature has always been a great source of inspiration to all mankind. Genetic Algorithms (GAs) are search based algorithms based on the concepts of natural selection and genetics. GAs are a subset of a much larger branch of computation.

5. Benefits of genetic algorithms Easy to understand Supports multi-objective optimisation Good for noisy environment We always get answer and answer gets better with time Inherently parallel and easily distributed Easy to exploit for precious or alternate solutions Flexible in forming building blocks for hybrid applications Has substantial history and range of use

6. Basic genetic algorithms Step1 : Represent the problem variable domain as a chromosome of a fixed length, choose the size of a chromosomes population N , the crossover probability P , and the mutation probability Pm. Step2 : Define a fitness function to measure the performance, or fitness, of a individual chromosome in the problem domain. The fitness function establishes the basis for selecting chromosomes that will be mated during reproduction. Step3 : Randomly generate an initial population of chromosomes of size N: x 1 , x 2 , ……x N.

7. Step4 : Calculate the fitness of each individual chromosome: f(x 1 ), f(x 2 ), ……..f(x N ) Step5 : Select a pair of chromosomes for mating from the current population. Parent chromosomes are selected with a probability related to their fitness. Step6 : Create a pair of offspring chromosomes by applying the genetic operators – crossover and mutation. Step7 : Place the created offspring chromosomes in the new population. Step8 : Repeat step5 until the size of the new chromosome population becomes equal to the size of the initial population, N. Step9 : Replace the initial (parent) chromosomes population with the new (offspring) population. Step10 : Go to step4, and separate the process until the termination criterion is satisfied .

8. Flowchart of genetic algorithm

9. Basic operation of ga Reproduction: It is usually the first operator applied on population. Chromosomes are selected from the population of parents to cross over and produce offspring. It is based on Darwin’s evolution theory of “Survival of the fittest”. Therefore, this operation is also known as ‘Selection Operation’. Cross Over : After reproduction phase, population is enriched with better individuals. It makes clones of good strings but doesn’t create new ones. Cross over operator is applied to the mating pool with a hope that it would create better strings.

10. Mutation : After cross over, the strings are subjected to mutation. Mutation of a involves flipping it, changing 0 to 1 and vice-versa.

11. APPLICATION OF GA Travelling Salesman Problem Artificial Life(A-Life) Robotics Automotive Design Evolvable Hardware Computer Gaming Encryption and Code Breaking Optimizing Chemical Kinetic Analysis

12. They are Robust. Provide optimization over large space state. Unlike traditional AI, they do not break on slight change in input or presence of noise. Why we use Genetic Algorithms

13. CONCLUSION: Genetic Algorithms(Gas) are search based algorithms based on the concepts of natural selection and genetics. There is know better algorithm than genetic algorithm.