Python Genetic Algorithm Implementation
On the style of the code:
While I'm not saying the code is or isn't correct, there's some small tweaks you could make it to improve it's legibility:
Spacing
Write code like you would write English (in some sense :P). After commas you needs spaces, so do

def crossover(p1, p2): instead of def crossover(p1,p2):;
random.uniform(b[i][0], b[i][1]) instead of random.uniform(b[i][0],b[i][1])
same thing around binary ops: if locii[i] &gt; 4: instead of if locii[i]&gt;4:
etc.

List comprehensions often do not need a leading space, so you can go with
p = [random_param(geno_size, bounds) for i in range(p_size)]

instead of
p = [ random_param(geno_size,bounds) for i in range(p_size)]

crossover
You have written:
def crossover(p1,p2):
    
    crossover = []
    locii = [random.randint(0,8) for _ in range(len(p1))]
    
    for i in range(len(p1)):
        if locii[i]&gt;4:
            crossover.append(p2[i])
        else:
            crossover.append(p1[i])
        
    return crossover

Consider this alternative definition:
def crossover(p1, p2):
    
    crossover = []
    for p1_elem, p2_elem in zip(p1, p2):
        if random.randint(0,8) &gt; 4:
            crossover.append(p2_elem)
        else:
            crossover.append(p1_elem)
        
    return crossover

Notice I don't precompute all locii, although you could do that, of course. Also note the usage of zip which means I can traverse both p1 and p2, instead of having to use indices to later grab the values. (You can read more about zip and find other tips and tricks here.
random_param
In line with one of the things I showed above, instead of doing
def random_param(g,b):
     return [random.uniform(b[i][0],b[i][1]) for i in range(g)]

you could iterate directly over b:
def random_param(g, b):
     return [random.uniform(bs[0], bs[1]) for bs in b]

(More on better iterating in Python here.)
Then, take that and unpack bs with * instead of with indices:
def random_param(g, b):
     return [random.uniform(*bounds) for bounds in b]