MOEA mu+lamda loosing diversity
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I'm using deap to solve an optimization (2 parameter minimization) with multiple objective using NSGAII and im seeing a NOT monotonically decreasing fitness. This goes hand in hand with a loss of diversity at the generations just before the increase in fitness value.
Unrolling all functions used in deap showed that after the sorting, all individuals needed for a selection of mu ones lie on the first front. Thats ok I guess. This front, however, looses diversity. After a few more generations, only one "type" of individual is left, that is having mu individuals that are absolutely equal.
At this point, I feel it is connected to my parameters..
Let's go back to basics: Are my settings massively flawed? Im using:
mu, lamda = 50,500
with
CX,MUT, IN_MUT = 0.35,0.4, 0.1
My train of thought here: I create 500 offsprings where 25% are taken from the population (e.g. 125) and then select 50 from the 550 containing (125 + 50=) 175 old ones. Maybe my issue is actually with this setting....Can anyone help to elaborate?
Since the code is a bit longer, a working example can be found on the gist here
https://gist.github.com/max3-2/8370cd60e85b2941d7b526c0cb6d7a47
python evolutionary-algorithm deap
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I'm using deap to solve an optimization (2 parameter minimization) with multiple objective using NSGAII and im seeing a NOT monotonically decreasing fitness. This goes hand in hand with a loss of diversity at the generations just before the increase in fitness value.
Unrolling all functions used in deap showed that after the sorting, all individuals needed for a selection of mu ones lie on the first front. Thats ok I guess. This front, however, looses diversity. After a few more generations, only one "type" of individual is left, that is having mu individuals that are absolutely equal.
At this point, I feel it is connected to my parameters..
Let's go back to basics: Are my settings massively flawed? Im using:
mu, lamda = 50,500
with
CX,MUT, IN_MUT = 0.35,0.4, 0.1
My train of thought here: I create 500 offsprings where 25% are taken from the population (e.g. 125) and then select 50 from the 550 containing (125 + 50=) 175 old ones. Maybe my issue is actually with this setting....Can anyone help to elaborate?
Since the code is a bit longer, a working example can be found on the gist here
https://gist.github.com/max3-2/8370cd60e85b2941d7b526c0cb6d7a47
python evolutionary-algorithm deap
add a comment |
I'm using deap to solve an optimization (2 parameter minimization) with multiple objective using NSGAII and im seeing a NOT monotonically decreasing fitness. This goes hand in hand with a loss of diversity at the generations just before the increase in fitness value.
Unrolling all functions used in deap showed that after the sorting, all individuals needed for a selection of mu ones lie on the first front. Thats ok I guess. This front, however, looses diversity. After a few more generations, only one "type" of individual is left, that is having mu individuals that are absolutely equal.
At this point, I feel it is connected to my parameters..
Let's go back to basics: Are my settings massively flawed? Im using:
mu, lamda = 50,500
with
CX,MUT, IN_MUT = 0.35,0.4, 0.1
My train of thought here: I create 500 offsprings where 25% are taken from the population (e.g. 125) and then select 50 from the 550 containing (125 + 50=) 175 old ones. Maybe my issue is actually with this setting....Can anyone help to elaborate?
Since the code is a bit longer, a working example can be found on the gist here
https://gist.github.com/max3-2/8370cd60e85b2941d7b526c0cb6d7a47
python evolutionary-algorithm deap
I'm using deap to solve an optimization (2 parameter minimization) with multiple objective using NSGAII and im seeing a NOT monotonically decreasing fitness. This goes hand in hand with a loss of diversity at the generations just before the increase in fitness value.
Unrolling all functions used in deap showed that after the sorting, all individuals needed for a selection of mu ones lie on the first front. Thats ok I guess. This front, however, looses diversity. After a few more generations, only one "type" of individual is left, that is having mu individuals that are absolutely equal.
At this point, I feel it is connected to my parameters..
Let's go back to basics: Are my settings massively flawed? Im using:
mu, lamda = 50,500
with
CX,MUT, IN_MUT = 0.35,0.4, 0.1
My train of thought here: I create 500 offsprings where 25% are taken from the population (e.g. 125) and then select 50 from the 550 containing (125 + 50=) 175 old ones. Maybe my issue is actually with this setting....Can anyone help to elaborate?
Since the code is a bit longer, a working example can be found on the gist here
https://gist.github.com/max3-2/8370cd60e85b2941d7b526c0cb6d7a47
python evolutionary-algorithm deap
python evolutionary-algorithm deap
asked Dec 27 at 13:23
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