diff --git a/README.md b/README.md
index 94e3bcf..c4ae76d 100644
--- a/README.md
+++ b/README.md
@@ -10,21 +10,23 @@ $ nix run
 ```
 and it will run through the pipeline. it will generate data/image files in `output/`.
 
-### implementation overview
+## how to use
 
-i implement a genetic algorithm to solve the binary knapsack problem.
+in `src/common.odin` there are multiple (hyper)parameters that can be set to influence how the genetic algorithm will run. here i will go through the available options to set.
 
-this is done by 
-- first generating a population of individuals/chromosomes, modeled as bit_arrays for memory performance.
-- then continue for some fixed amount of generations
-  - calculate the fitness of each chromosome in the population by calculating the distance to the capacity. to avoid negative fitness values, chromosomes that overshoot the capacity are penalized.
-  - create offspring by
-    - selecting two parents using a tournament selection
-    - then performing single point crossover between the parents to create two children
-    - then mutate each child with random bit-flip mutations.
-  - create a new population via elitism survivor selection
-- repeat
-
-each generation, the fitness value maximum, minimum and mean values are logged in `output/data.csv`.
-
-this data is read through uiua `src/plot.ua` to plot the data to `output/plot.png`.
+| parameter | possible values | note |
+|---|---|---|
+| `PROBLEM_TYPE` | `"knapsack"`, `"feature_selection"` | part 1 or part 2 of the task |
+| `GENERATIONS` | int > 0 | how many generations to simulate for |
+| `POPULATION_SIZE` | int > 0 | how many individuals in population. remains constant. |
+| `ELITISM_COUNT` | unsigned int | how many elites to bypass selection pipeline |
+| `SKEW` | unsigned int | reproductive skew, influences how many parents and children compete for selection |
+| `TOURNAMENT_SIZE` | unsigned int | how many to participate in tournament, if tournament selection is used |
+| `CROSSOVER_RATE` | [0, 1] | chance of performing crossover |
+| `MUTATION_RATE` | [0, 1] | chance of random mutation occuring |
+| `PARENT_SELECTION_POLICY` | `random_selection`, `roulette_selection`, `tournament_selection` | what operator to use |
+| `CROSSOVER_POLICY` | `single_point_crossover`, `two_point_crossover`, `uniform_crossover` | |
+| `MUTATION_POLICY` | `bit_flip_mutation`, `swap_mutation`, `inversion_mutation` | |
+| `SURVIVOR_SELECTION_POLICY` | `generational_replacement`, `deterministic_crowding`, `probabilistic_crowding` | |
+| `RANDOM_SEED` | unsigned int | random seed if set to 0 |
+| `OUTPUT_FILE` | string path | where to store data. for simplicity, `plot.ua` expects `output/data.csv` as the output path. |