1. Population New Population Selection Crossover and Mutation Insert When the new population is full repeat Generational Algorithm

2. Steady-State Algorithm Population Selection Crossover and Mutation Reinsert offspring, replacing low fitness individuals

3. The EC Cycle Population of N individuals Select pairs based on fitness Apply crossover and mutation Parents: Offspring: mutation crossover Individuals are replaced in the population (steady state) Or individuals are placed in a new population (generational) Once the new populations is filled the cycle repeats.

4. Representations Binary strings Integer vectors Equations Programs Graphs Etc. 0 0 1 0 1 1 1 0 0 01 7X 6- + 5 6 19 3 67 12 99 20 X Rea d 6 if do

5. Fitness How good is the solution? How close are the values to optimal? How often does the robot bump into walls? Etc.

6. Crossover and Mutation Parents: Offspring: mutation crossover mutation