update README

README.md

@@ -7,14 +7,14 @@
 [![Build status](https://ci.appveyor.com/api/projects/status/eu0nns2dsgocwntw/branch/master?svg=true)](https://ci.appveyor.com/project/jakobbossek/mcMST/branch/master)
 [![Coverage Status](https://coveralls.io/repos/github/jakobbossek/mcMST/badge.svg?branch=master)](https://coveralls.io/github/jakobbossek/mcMST?branch=master)
 
-## Brief introduction
+## Introduction
 
-It is well known, that the single-objective spanning tree problem (MST) is solvable in polynomial time, e.g., by the algorithm of Prim. However, in real-world applications, e.g., in network design, often multiple conflicting objectives have to be considered simultaneously. The multi-criteria version of the MST is NP-hard. The **mcMST** package for the statistical programming language [R](https://www.r-project.org) contains methods for benchmark instance generation of multi-objective graph problems and methods for solving the multi-criteria spanning tree problem (mcMST).
+It is well known, that the single-objective spanning tree problem (MST) is solvable in polynomial time, e.g., by the Prim's algorithm. However, in real-world applications, e.g., in network design, often multiple conflicting objectives have to be considered simultaneously. The multi-criteria version of the MST is NP-hard. The **mcMST** package for the [R](https://www.r-project.org) programming language contains methods for benchmark instance generation of multi-objective graph problems and methods for solving the multi-criteria spanning tree problem (mcMST).
 
 Key features of the **mcMST** package are:
 
 * A multi-objective version of Prim's algorithm.
-* Two evolutionary multi-objective algorothms (one based on the Prüfer-encoding and another based on a direct edge list representation).
+* Evolutionary multi-objective algorithms (based on the Prüfer-encoding or  direct edge list representation) with several mutation operators.
 * A modular approach for benchmark problem generation. 
 
 ## Example
@@ -29,7 +29,7 @@ g = addWeights(g, method = "random", weight.fun = rnorm, mean = 5, sd = 1.5)
 print(g)
 ```
 
-Next, we apply the Genetic Algorithm proposed by Zhou & Gen with population size `mu = 10` and number of offspring `lambda = 10` for `max.iter = 100` generations.
+Next, we apply the genetic algorithm proposed by Zhou & Gen with population size `mu = 10` and number of offspring `lambda = 10` for `max.iter = 100` generations.
 ```r
 library(ggplot2)
 res = mcMSTEmoaZhou(g, mu = 10L, lambda = 10L, max.iter = 100L)
@@ -39,11 +39,11 @@ See the package vignettes for more details.
 
 ## Installation Instructions
 
-The package will be available at [CRAN](http://cran.r-project.org) soon. Install the release version via:
+Install the [CRAN](http://cran.r-project.org) release version via:
 ```r
 install.packages("mcMST")
 ```
-If you are interested in trying out and playing around with the current github developer version use the [devtools](https://github.com/hadley/devtools) package and type the following command in R:
+If you are interested in trying out and playing around with the current development version use the [devtools](https://github.com/hadley/devtools) package and install directly from GitHub:
 
 ```r
 install.packages("devtools", dependencies = TRUE)