readme: fix evolutionary pipeline plot

README.md

@@ -189,7 +189,7 @@ A detailed example is available as a [IPython Notebook](examples/example-2-evolu
 `neurolib` also implements evolutionary parameter optimization, which works particularly well with brain networks. In an evolutionary algorithm, each simulation is represented as an individual and the parameters of the simulation, for example coupling strengths or noise level values, are represented as the genes of each individual. An individual is a part of a population. In each generation, individuals are evaluated and ranked according to a fitness criterion. For whole-brain network simulations, this could be the fit of the simulated activity to empirical data. Then, individuals with a high fitness value are `selected` as parents and `mate` to create offspring. These offspring undergo random `mutations` of their genes. After all offspring are evaluated, the best individuals of the population are selected to transition into the next generation. This process goes on for a given amount generations until a stopping criterion is reached. This could be a predefined maximum number of generations or when a large enough population with high fitness values is found.
 
 <p align="center">
-  <img src="https://github.com/neurolib-dev/neurolib/raw/master/resources/evolutionary-algorithm.svg", width="600">
+  <img src="https://github.com/neurolib-dev/neurolib/raw/master/resources/evolutionary-algorithm.png", width="600">
 </p>
 
 `neurolib` makes it very easy to set up your own evolutionary optimization and everything else is handled under the hood. Of course, if you like, you can dig deeper, define your own selection, mutation and mating operators. In the following demonstration, we will simply evaluate the fitness of each individual as the distance to the unit circle. After a couple of generations of mating, mutating and selecting, only individuals who are close to the circle should survive: