'plus' replacement in replacers.py might have an issue #27
Comments
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The plus replacement is implemented that way by design. The documentation
says it's a combination of parents and offspring, rather than the entire
population plus offspring. It's a design choice rather than a mistake.
Aaron
…--
Aaron Garrett
On Fri, Jul 21, 2023 at 4:32 AM Alberto Tonda ***@***.***> wrote:
- inspyred version: 1.0.1
- Python version: 3.10.9
- Operating System: Windows
Description
By performing debug printouts, I noticed something weird in
inspyred.ec.replacers.plus_replacement ; in theory, "plus" should take the
current population + the offspring, sort them, and return the best
len(population) individuals as the survivors. However, unless I made a
mistake with my debug printouts, it looks like that the 'parent' list in
the argument contains *only individuals that were actually selected for
reproduction*. So, the code for plus_replacement that goes:
pool = list(offspring)
pool.extend(parents)
pool.sort(reverse=True)
survivors = pool[:len(population)]
return survivors
is not correct, as it creates a list with all individuals which reproduced
+ the offspring. In this way, it actually creates a 'survivors' list with
potentially multiple copies of the same individual (if, for example, one
individual was selected for reproduction multiple times). Correcting this
should be easy enough:
pool = list(offspring)
**pool.extend(population)**
pool.sort(reverse=True)
survivors = pool[:len(population)]
return survivors
However, I would double-check all other replacers, because they might have
similar issues if they assume that 'parents' contains the current
population and not just a list (with potential duplicates!) of the
individuals that were selected for reproduction.
Here below is a printout of the content of 'parents', 'population', and
'offspring' for a problem where I have a custom individual.candidate genome
(akin to Genetic Programming). I also have unique ids for the individuals.
As you can see, while 'population' contains one copy of each individual,
'parents' has several copies of individuals 1 and 4, which were selected
for reproduction multiple times.
[DEBUG 2023-07-21 10:09:01] Initial state of the parents:
[DEBUG 2023-07-21 10:09:01] 1:'u' : '(cos(P)/(P-P))'
[DEBUG 2023-07-21 10:09:01] 4:'u' : 'sin(cos(((P-P)+(L/P))))'
[DEBUG 2023-07-21 10:09:01] 1:'u' : '(cos(P)/(P-P))'
[DEBUG 2023-07-21 10:09:01] 1:'u' : '(cos(P)/(P-P))'
[DEBUG 2023-07-21 10:09:01] 4:'u' : 'sin(cos(((P-P)+(L/P))))'
[DEBUG 2023-07-21 10:09:01] 5:'u' : 'max(cos(min(L,(0.6575))),min(min(L,L),((0.6309)+(0.5804))))'
[DEBUG 2023-07-21 10:09:01] 9:'u' : '(((min(P,P)-max(L,P))*(min(L,(-0.9861))+min(L,P)))/(-0.6634))'
[DEBUG 2023-07-21 10:09:01] 4:'u' : 'sin(cos(((P-P)+(L/P))))'
[DEBUG 2023-07-21 10:09:01] 2:'u' : '(max(L,L)+max((-0.0879),(0.9665)))'
[DEBUG 2023-07-21 10:09:01] 8:'u' : 'sin(min(max(L,(0.7915)),(P/(0.2088))))'
[DEBUG 2023-07-21 10:09:01] Initial state of the population:
[DEBUG 2023-07-21 10:09:01] 0:'u' : 'cos(sin((-0.8001)))'
[DEBUG 2023-07-21 10:09:01] 1:'u' : '(cos(P)/(P-P))'
[DEBUG 2023-07-21 10:09:01] 2:'u' : '(max(L,L)+max((-0.0879),(0.9665)))'
[DEBUG 2023-07-21 10:09:01] 3:'u' : 'sqrt(sin(min((L-P),max(P,P))))'
[DEBUG 2023-07-21 10:09:01] 4:'u' : 'sin(cos(((P-P)+(L/P))))'
[DEBUG 2023-07-21 10:09:01] 5:'u' : 'max(cos(min(L,(0.6575))),min(min(L,L),((0.6309)+(0.5804))))'
[DEBUG 2023-07-21 10:09:01] 6:'u' : 'max(max((0.7001),P),sin(P))'
[DEBUG 2023-07-21 10:09:01] 7:'u' : '(cos((0.1225))*sqrt(L))'
[DEBUG 2023-07-21 10:09:01] 8:'u' : 'sin(min(max(L,(0.7915)),(P/(0.2088))))'
[DEBUG 2023-07-21 10:09:01] 9:'u' : '(((min(P,P)-max(L,P))*(min(L,(-0.9861))+min(L,P)))/(-0.6634))'
[DEBUG 2023-07-21 10:09:01] Initial state of the offspring:
[DEBUG 2023-07-21 10:09:01] 10:'u' : '(((P-P)+(L/P))/(P-P))'
[DEBUG 2023-07-21 10:09:01] 11:'u' : '(sin(P)/min(P,L))'
[DEBUG 2023-07-21 10:09:01] 12:'u' : 'sin(cos(min((P-P),max(P,P))))'
[DEBUG 2023-07-21 10:09:01] 13:'u' : 'L'
[DEBUG 2023-07-21 10:09:01] 14:'u' : 'sin(min(max(L,(0.7915)),(P/(0.2088))))'
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I see! However, I still think the implementation should be modified, for two reasons:
Here, it is clear that the population P at line 7, just before selection, is the union of the old population and the new offspring. Even from another point of view, since mu refers to the size of the whole population, if only the parent individuals selected for reproduction were included in the global archive before selection, the size of the population at the end of a generation, just before selection, would not be of size mu+lambda, but rather (size of an archive with just individuals selected for reproduction)+lambda. The size of such an archive would vary with the type of variators applied (e.g. 2 individuals for each activation of crossover(s), 1 individual for each activation of a mutation).
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Alberto is correct. For an ES(µ+λ) the pool of prospective parents and created offspring are poured into the same bucket and then truncation selection yields a new pool of prospective parents of size |µ|. Combining just the selected parents and offspring introduces a new source of genetic drift -- that is, parents not selected drop out of sight even if they happen to be some of the best. This is an ES example from LEAP:
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OK, I'm satisfied. If you want to make the pull request, I'll approve it.
AG
…--
Aaron Garrett
On Fri, Jul 21, 2023 at 4:35 PM Mark Coletti ***@***.***> wrote:
Alberto is correct. For an ES(µ+λ) the pool of perspective parents and
created offspring are poured into the same bucket and then truncation
selection yields a new pool of prospective parents of size |µ|. Combining
just the *selected* parents and offspring introduces a new source of
genetic drift -- that is, parents not selected drop out of sight even if
they happen to be some of the best.
This is an ES example from LEAP:
while generation_counter.generation() < max_generation:
offspring = pipe(parents,
ops.random_selection,
ops.clone,
mutate_gaussian(std=context['leap']['std'], expected_num_mutations=1),
ops.evaluate,
# create the brood
ops.pool(size=len(parents) * BROOD_SIZE),
# mu + lambda
ops.truncation_selection(size=len(parents),
parents=parents))
parents = offspring
pipe() takes a data source as its first argument, in this case the
parents, from which offspring are accumulated via the ops.pool()
operator. Then those offspring are passed to the truncation_selection()
operator where the pool of prospective parents, *including those that
were not selected by happenstance for creating offspring*, are combined
and truncated by fitness; this way we do not lose the best parents (unless
they happen to all be worse than the offspring). Converting this snippet to
an ES(µ,λ) would simply entail dropping the parents=parents argument to
the truncation selection.
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Fixed in #28 |
Description
By performing debug printouts, I noticed something weird in inspyred.ec.replacers.plus_replacement ; in theory, "plus" should take the current population + the offspring, sort them, and return the best len(population) individuals as the survivors. However, unless I made a mistake with my debug printouts, it looks like that the 'parent' list in the argument contains only individuals that were actually selected for reproduction. So, the code for plus_replacement that goes:
is not correct, as it creates a list with all individuals which reproduced + the offspring. In this way, it actually creates a 'survivors' list with potentially multiple copies of the same individual (if, for example, one individual was selected for reproduction multiple times). Correcting this should be easy enough:
However, I would double-check all other replacers, because they might have similar issues if they assume that 'parents' contains the current population and not just a list (with potential duplicates!) of the individuals that were selected for reproduction.
Here below is a printout of the content of 'parents', 'population', and 'offspring' for a problem where I have a custom individual.candidate genome (akin to Genetic Programming). I also have unique ids for the individuals. As you can see, while 'population' contains one copy of each individual, 'parents' has several copies of individuals 1 and 4, which were selected for reproduction multiple times.
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