Artificial Intelligence

4. We use the following graph in the class to illustrate the genetic algorithm (GA) process. 24748552 24 31% 327,5 2411 32748552 3274 8152 32752411 23 29% 24748552 24752411 24752411 24415124 20 26% 3275 2411 32752124 32252124 32543213 11 14% 244151124 24415411 24415417 Fitness Selection Pairs Cross-Over Mutation Students work from the last column to repeat the process of fitness calculation, selection, crossover, and mutation. Because of probability involved in selection, students can act as a random selector to make a section. Remember, the high the probability, the better chance to be selected. 32748152 24752411 32252124 24415417 4. We use the following graph in the class to illustrate the genetic algorithm (GA) process. 24748552 24 31% 327,5 2411 32748552 3274 8152 32752411 23 29% 24748552 24752411 24752411 24415124 20 26% 3275 2411 32752124 32252124 32543213 11 14% 244151124 24415411 24415417 Fitness Selection Pairs Cross-Over Mutation Students work from the last column to repeat the process of fitness calculation, selection, crossover, and mutation. Because of probability involved in selection, students can act as a random selector to make a section. Remember, the high the probability, the better chance to be selected. 32748152 24752411 32252124 24415417