Basic Genetic Algorithm
{"Image DocumentName": "Basic Genetic Algorithm", "Image ImageDescription": "Step 1. Represent the problem domain as a chromosome of fixed length n, choose the size of population of chromosomes N, a crossover probability pc and a mutation probability pm. Step 2. Define a fitness function f to measure the performance (fitness) of an individual chromosome in the problem domain. The fitness function is the basis for selecting chromosomes that will be mated during reproduction. Step 3: Randomly generate an initial population of chromosomes of size N: x1, x1, ... ,xN. Step 4: Calculate the fitness of each individual chromosome: f(x1), f(x2), ... , f(x)N. Step 5: Select a pair of chromosomes for mating from the current population. Parent chromosomes are selected with a probability related to their fitness (eg by Roulette wheel, tournament, ranking, random walk or remainder methods) ICT219.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}