Genetic Algorithm Fitness Stop Increasing at Some Point

Question

I am trying to implement a Genetic Algorithm which will recreate a given image by positioning, sizing and colouring 1000 circles. However, I found that when the fitness reaches a certain value (around 0.37 in my case) it stop increasing. In other words it just converges on the value.

I can't think of a way to improve this

Some Details

population size: 50

mutation rate: 0.025

the genes of each child is an 1000 element array with element type CircleData, which contains the size, color and position of a circle

Fitness Function

I loop through every pixels on the target image and the canvas the algorithm drew on. For each pixel, I map the RGB value to a point in 3D space and calculate the distance between the two points. The distance will then be summed up for every pixels and normalise to get a fitness value in [0, 1], where 1 means each pixel are identical. Finally I take the value of pow(fitness, 2) as the final fitness value.

Selection

I sort the children base on their fitness, and drop the worse 2/3. For the remaining children, I use Fitness Proportionate Selection to select two parents

Crossover

I use one point crossover with random crossover point

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Update

I suspect the problem is due to local maximum, still trying to fix that...

Some SO questions on local maximum in GA:

How to prevent genetic algorithm from converging on local minima?

How can I prevent my program from getting stuck at a local maximum (Feed forward artificial neural network and genetic algorithm)

Max Fitness stuck at local maxima in genetic algorithm implementation