pull small changes from main to dev

.github/workflows/publish.yml

@@ -1,20 +1,23 @@
 name: Publish
 
-on: ['workflow_dispatch']
+on: ["workflow_dispatch"]
+
+env:
+  CARGO_REGISTRY_TOKEN: ${{ secrets.CARGO_TOKEN }}
 
 jobs:
-    publish:
-        runs-on: ubuntu-latest
-        environment: publish
+  publish:
+    runs-on: ubuntu-latest
+    environment: publish
 
-        steps:
-            - uses: actions/checkout@v4
-            - uses: actions-rust-lang/setup-rust-toolchain@v1
-              with:
-                toolchain: nightly
-            - name: "Publish common"
-              run: cargo +nightly publish -p genetic-rs-common --token ${{ secrets.CARGO_TOKEN }}
-            - name: "Publish macros"
-              run: cargo +nightly publish -p genetic-rs-macros --token ${{ secrets.CARGO_TOKEN }}
-            - name: "Publish main"
-              run: cargo +nightly publish -p genetic-rs --cfg publish --token ${{ secrets.CARGO_TOKEN }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions-rust-lang/setup-rust-toolchain@v1
+        with:
+          toolchain: nightly
+      - name: "Publish common"
+        run: cargo +nightly publish -p genetic-rs-common
+      - name: "Publish macros"
+        run: cargo +nightly publish -p genetic-rs-macros
+      - name: "Publish main"
+        run: cargo +nightly publish -p genetic-rs --config publish

README.md

@@ -7,11 +7,11 @@
 A small framework for managing genetic algorithms.
 
 ### Features
-First off, this crate comes with the `builtin`, `crossover`, and `genrand` features by default. If you want it to be parallelized (which is true in most cases), you can add the `rayon` feature. If you want your crossover to be speciated, you can add the `speciation` feature.
+First off, this crate comes with the `builtin`, `genrand`, `crossover`, `knockout`, and `speciation` features by default. If you want the simulation to be parallelized (which is most usecases), add the `rayon` feature. There are also some convenient macros with the `derive` feature.
 
 ### How to Use
 > [!NOTE] 
-> If you are interested in implementing NEAT with this, or just want a more complex example, try out the [neat](https://crates.io/crates/neat) crate
+> If you are interested in implementing NEAT with this, or just want a more complex example, check out the [neat](https://crates.io/crates/neat) crate
 
 Here's a simple genetic algorithm:
 
@@ -33,7 +33,7 @@ impl RandomlyMutable for MyGenome {
     }
 }
 
-// allows us to use `Vec::gen_random` for the initial population. note that `Vec::gen_random` has a slightly different function signature depending on whether the `rayon` feature is enabled.
+// allows us to use `Vec::gen_random` for the initial population. note that with the `rayon` feature, we can also use `Vec::par_gen_random`.
 impl GenerateRandom for MyGenome {
     fn gen_random(rng: &mut impl Rng) -> Self {
         Self { field1: rng.random() }
@@ -50,7 +50,7 @@ fn main() {
     let mut rng = rand::rng();
     let mut sim = GeneticSim::new(
         // you must provide a random starting population. 
-        // size will be preserved in builtin nextgen fns, but it is not required to keep a constant size if you were to build your own nextgen function.
+        // size will be preserved in builtin repopulators, but it is not required to keep a constant size if you were to build your own.
         // in this case, the compiler can infer the type of `Vec::gen_random` because of the input of `my_fitness_fn`.
         Vec::gen_random(&mut rng, 100),
         FitnessEliminator::new_with_default(my_fitness_fn),