how does genetic algorithm work?
        population -> a number of iterations for new generations -> target population
    
        a. population -- pop_size = 50
           next generation? 
              (1) select parents, create next generation by crossover and then mutate -- select_count = 20
              (2) select the strongest from the current generation, keep_count = 5
              (3) random individuals, pop_size - keep_count - select_count
        b. fitness equation
        
    starter code: assign9.py, picture.py
       a. generate a population of individuals
          what is the population size? input parameter (line 161), initialization (line 140)
          what is each individual's gene? rectangle
              input parameter (line 161), initialization individuals (line 90),
              initialize genes (line 12), gene (a rectangle) (line 7), xywhrgbz meaning?
              
       b. evolving by (1) keep some strongest, (2) select to breed and (3) random breed
          how many to keep? how many to select?
              5, line 161, 142, 81, explain the attributes of Population
          'evolve_step':
              step 1: GIVEN: initialize a new generation, line 117
              step 2: TODO: keep some strongest, how? a for loop, what range? how to pick the strongest?
              step 3: TODO: select to breed, how? another for loop, what range? three mini steps:
                  i) how to select two people? -- select strategy
                  ii) crossover to breed
                  iii) mutate
              step 4: TODO: random breed, what range? for loop? 
              step 5: GIVEN: replace the current generation with the new one
              step 6: GIVEN: sort by fitness? lower better or larger better?
       c. more details about select, crossover_with and mutate:
          select: input? output?
              input: self, which is a population of individuals
              output: two individuals from this population, e.g., self
          crossover_with: input? output?
              input: self, ind2, which means crossover genes between self and ind2,
                  both of which are individuals. explain individuals in line 11
              output: a new individual or a baby, which is an Individual
              how: initialize a new individual and then manipulate genes
          mutate:
              input: self: an individual, rate: the probability to mutate, amt: the amount to change
              output: a new individual
       d. how to determine fitness? line 44, and picture->compare
          the output of run_ga, after step number, the best fitness and the worst fitness will be printed out
       e. add simple strategy for select, crossover and mutate first, and finish evolve_step. After that, you could test more advanced strategies.