GA settings

MC Manual has not been updated in many areas since 2018 (e.g. Kase bars?)

But I am specifically interested in understanding the meaning/ implications of different GA settings.

- when "least fit individuals are discarded ", - how many - as a % of population? is this changeable?
- does a (single) "crossover" always occur between 2 parents ?

Basic GA:
* This algorithm uses non-overlapping generations and Elitism mode (optional).
* For each generation, the algorithm creates an entirely new population of individuals (if the Elitism option is selected, the most fit individuals move
on to the next generation). -- This is not clear at all
-->What exactly happens with the initial population?
-->What are 'non-overlapping generations'?? and how are they created? how is the "entirely new population" created?
-> how is "Crossover probability" applied in this case?
-> how many "Elite/most fit" individual (as a % of population) move to the next generation?
--> if Elitism is not selected, how does Evolution happen altogether?

Incremental GA:
* It simply adds only one or two children to the population each time the next generation is created. These one or two children replace one or two individuals in the previous generation. --> so, does it add or replace?
--> With "Parent" replacement scheme, do children replace parents only if their fitness is better?

- "Number of individuals in population for crossover" --> what's the meaning of this and how does this work?
--> what should it be set to (cpu cores? % of population)?
-> how does "Crossover probability" work in this case?

Convergence Type/Proximal:
* as per manual, GA calculation is stopped after meeting: С [x – N] / C [x] >= P

--> shouldn't it be C[x] / C[x-N] >=P

I have the same questions. Any suggestions about these settings? I am so confused about the settings of the GA optimization.

Hello Zheka and shargo,

• When "least fit individuals are discarded ", - how many - as a % of population? is this changeable?

For Incremental the parameter ‘Number of individuals in population for crossover’ specifies the number of individuals that will be replaced.

• does a (single) "crossover" always occur between 2 parents?

Yes, crossover is a procedure for generating a “child” from two “parent” genomes. Crossover involves multiple genomes.

Basic GA:
The probabilities for Crossover and Mutations are set in Crossover probability and Mutation probability.

• What exactly happens with the initial population?
• What are 'non-overlapping generations'?? and how are they created? how is the "entirely new population" created?
• how is "Crossover probability" applied in this case?
• how many "Elite/most fit" individual (as a % of population) move to the next generation?
• if Elitism is not selected, how does Evolution happen altogether?[/b]

The generation is generated randomly
When Elitism is turned on, the best are not destroyed (so as not to lose the “elite”).
Non-overlapping generation means that MultiCharts uses one generation for generation.

Incremental GA:

• With "Parent" replacement scheme, do children replace parents only if their fitness is better?

The population size at each step is fixed. In the new generation, children replace individuals from the previous generation as described further.

• "Number of individuals in population for crossover" --> what's the meaning of this and how does this work?
  what should it be set to (cpu cores? % of population)?

In a generation, the Set Population Size will replace the number of individuals specified in ‘Number of individuals in population for crossover’

By default, ‘Number of individuals in population for crossover’ is set in the range: from number of CPU cores (so that all cores are loaded during optimization) to Population Size /2. There is not much sense in making this number bigger as it GA will most likely not find the best if the entire generation is updated.
Replacement Scheme is also taken into account (replace the worst/parent/random individual).

• how does "Crossover probability" work in this case?

Randomly selected individuals are crossed with ‘Crossover probability’ until we get the required quantity indicated in ‘Number of individuals in population for crossover’.

• as per manual, GA calculation is stopped after meeting: С [x - N] / C [x] >= P
  shouldn't it be C[x] / C[x-N] >=P

No, because C's fitness generally increases i.e. this fraction must be less than 1 and greater than or equal to P().