adding to readme

README.md

@@ -1 +1,73 @@
-# Metahueristics-library-placeholder
+# Metaheuristics-library-placeholder
+
+## Current Features:
+* Genetic Algorithm
+* Evolutionary Algorithm
+* Simulated Annealing
+* Particle Swarm Optimization
+* Tabu Search
+* Harmony Search
+* Stochastic Hill Climb
+
+<hr>
+
+## Usage:
+* ```pip install``` 
+* Import the relevant algorithm
+* Create a class that inherits from that algorithm, and that implements the necessary abstract methods
+* Call its ```.run()``` method
+
+<hr>
+
+## Example:
+
+```python
+from random import choice, randint, random
+from string import lowercase
+from library.EvolutionaryAlgorithm import EvolutionaryAlgorithm
+
+
+class Algorithm(EvolutionaryAlgorithm):
+    """
+    Tries to get a randomly-generated string to match string "clout"
+    """
+    def _initial_population(self):
+        return list(''.join([choice(lowercase) for _ in range(5)]) for _ in range(50))
+
+    def _fitness(self, member):
+        return float(sum(member[i] == "clout"[i] for i in range(5)))
+
+    def _crossover(self, parent1, parent2):
+        partition = randint(0, len(self.population[0]) - 1)
+        return parent1[0:partition] + parent2[partition:]
+
+    def _mutate(self, member):
+        if self.mutation_rate >= random():
+            member = list(member)
+            member[randint(0,4)] = choice(lowercase)
+            member = ''.join(member)
+        return member
+
+
+def test_algorithm():
+    algorithm = Algorithm(.5, .7, 500, max_fitness=None)
+    algorithm.run()
+
+```
+
+<hr>
+
+## Building and Testing
+
+To run tests, look in the ```tests``` folder. 
+
+I use [pytest](https://docs.pytest.org/en/latest/); it should automatically find the test files. 
+
+<hr>
+
+## Contributing
+
+Feel free to send a pull request if you want to add any features or if you find a bug.
+
+Check the issues tab for some potential things to do.
+

library/TabuSearch.py

@@ -25,7 +25,7 @@ class TabuSearch:
     def __init__(self, initial_state, tabu_size, max_steps, max_score=None):
         """
 
-        :param initial_state: initial state
+        :param initial_state: initial state, should implement __eq__ or __cmp__
         :param tabu_size: number of states to keep in tabu list
         :param max_steps: maximum number of steps to run algorithm for
         :param max_score: score to stop algorithm once reached