Merge pull request #1 from ValerGit/master

Let everyone know about this algo

README.md

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+# Genetic algorithm for the travelling salesman problem
+
+## How it works. Take a look at genetic.py
+
+First of all I should say that here we are considering such kind of
+travelling salesman problem where cities to start and to finish
+the salesman journey are given.
+
+Step 1. All distances between cities are given in a matrix called
+weights, so each diagonal cell equals 0 and ij-cell is
+a distance between cities i and j.
+
+Step 2. For making **initial population** we set its size ```pop_size```
+and number of generations(```gen_max```)In method ```generate_population```
+ we generate
+initial population randomly.
+
+Step 3. **Crossover.** We take two random parents(paths) and apply a
+crossover method for them. ```crossover(self, mother, father)```
+The number of taken chromosomes from mother and father are
+generated randomly.
+
+Step 4. **Mutation** Each child after crossover exposed to mutation.
+in ``chromosome.py`` you can see method ```mutate``` which just swaps two
+chromosomes in a gene. So yes 100% of population is mutated.
+
+Step 5. **Fitness** We measure fitness of each generation by measuring
+length of proposed path. In each iteration of algorithm work we are
+looking for the fittest path and put into ```best``` variable.
+
+Step 6. **Selection** Method ```selection``` sort new generation and
+previous generation using fitness function and gives out the best
+representatives of each generation and put it together updating
+population array.
+
+In a cycle we are repeating steps 3, 4, 5 and 6 for 1000 times.