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I am doing my thesis on Evolutionary Computation. In n-dimensional space point (let's call individual) has n variables associated with each dimension. Constraint is summation these variables must be around 1 (between .95 to 1.05).

So the question is, how can I generate n random numbers whose summation will be around 1?

This isn't directly relate stats. But I would really appreciate any sort of help.

A fitness function evaluates fitness of every individual. Say, I have two individuals a and b. Variables associated with individual a will be adapted by some function (which I haven't figured out yet) of euclidian distance with b & difference of fitness value. These variables will be adaptive (by I guess something like covariance matrix). So if I increase value of some variables, values of some variables have to be decreased to maintain summation 1. Any idea or suggestion?

Sorry for not using latex and I don't have stronghold on stat.

You can have a look at CMA-ES.

Generating n random variables whose summation will be 1. [I got the answer.]

EDIT

On genetic algorithm, we have to maintain population. Say, I have two individuals a and b. Every individual consists of $n$ pairs of ($x_i, \theta_i$), where $ 0 \leq i < n$. A fitness function evaluates fitness, $f$ of every individual. Constraint is for every individual is $\Sigma\theta_i \approx 1$ ($0.95 \leq \Sigma\theta_i < 1.05$ would suffice). $\theta_i$ associated with individual a will be adapted by some function (which I haven't figured out yet) of $d(a, b)$ & $\Delta f$. $\theta_i$ will be adaptive (by I guess something like covariance matrix). So if I increase value of $\theta_i$, values of some $\theta_j$ have to be decreased to maintain summation $\Sigma\theta_i \approx 1$. So I am seeking suggestion how can be $\theta_i$ adapted based on $d(a, b)$ & $\Delta f$?

I am doing my thesis on Evolutionary Computation. In n-dimensional space point (let's call individual) has n variables associated with each dimension. Constraint is summation these variables must be around 1 (between .95 to 1.05).

So the question is, how can I generate n random numbers whose summation will be around 1?

P.S.

This isn't directly relate stats. But I would really appreciate any sort of help

A fitness function evaluates fitness of every individual. Say, I have two individuals a and b. Variables associated with individual a will be adapted by some function (which I haven't figured out yet) of euclidian distance with b & difference of fitness value. These variables will be adaptive (by I guess something like covariance matrix). So if I increase value of some variables, values of some variables have to be decreased to maintain summation 1. Any idea or suggestion?

Sorry for not using latex and I don't have stronghold on stat.

You can have a look at CMA-ES.

I am doing my thesis on Evolutionary Computation. In n-dimensional space point (let's call individual) has n variables associated with each dimension. Constraint is summation these variables must be around 1 (between .95 to 1.05).

So the question is, how can I generate n random numbers whose summation will be around 1?

This isn't directly relate stats. But I would really appreciate any sort of help.

A fitness function evaluates fitness of every individual. Say, I have two individuals a and b. Variables associated with individual a will be adapted by some function (which I haven't figured out yet) of euclidian distance with b & difference of fitness value. These variables will be adaptive (by I guess something like covariance matrix). So if I increase value of some variables, values of some variables have to be decreased to maintain summation 1. Any idea or suggestion?

Sorry for not using latex and I don't have stronghold on stat.

You can have a look at CMA-ES.

I have n parameters. I want summation of parameters will be around 1 with 5% error margin. Is there any method such that, it will generate n random number whose summation will be around?

I am doing my thesis on Evolutionary Computation. In n-dimensional space point (let's call individual) has n variables associated with each dimension. Constraint is summation these variables must be around 1 (between .95 to 1.05).

So the question is, how can I generate n random numbers whose summation will be around 1?

P.S.

This isn't directly relate stats. But I would really appreciate any sort of help

A fitness function evaluates fitness of every individual. Say, I have two individuals a and b. Variables associated with individual a will be adapted by some function (which I haven't figured out yet) of euclidian distance with b & difference of fitness value. These variables will be adaptive (by I guess something like covariance matrix). So if I increase value of some variables, values of some variables have to be decreased to maintain summation 1. Any idea or suggestion?

Sorry for not using latex and I don't have stronghold on stat.

You can have a look at CMA-ES.