Merge branch 'develop'

Author: pkalivas

crates/radiate-engines/src/steps/evaluate.rs

@@ -64,8 +64,7 @@ where
             }
         }
 
-        let problem = Arc::clone(&problem);
-        let work_results: Vec<_> = jobs
+        let work_results = jobs
             .into_iter()
             .map(|(idx, geno)| {
                 let problem = Arc::clone(&problem);
@@ -74,7 +73,7 @@ where
                     (idx, score, geno)
                 })
             })
-            .collect();
+            .collect::<Vec<_>>();
 
         let count = work_results.len();
         for work_result in work_results {

docs/alterers.md

@@ -6,7 +6,7 @@ As such, the choice of alterer can have a significant impact on the performance
 Adding alterers to the engine can be done in a few ways. The simplest way is to add a single mutator and a single crossover to the engine as such:
 
 ```rust
-    let engine = GeneticEngine::from_codex(codex)
+    let engine = GeneticEngine::builder()
 		...
         .crossover(MultiPointCrossover::new(0.75, 2))
         .mutator(UniformMutator::new(0.05))
@@ -16,7 +16,7 @@ Adding alterers to the engine can be done in a few ways. The simplest way is to
 However, if more than one is desired it can be done as such:
 
 ```rust
-	let engine = GeneticEngine::from_codex(codex)
+	let engine = GeneticEngine::builder()
 		...
 		.mutators(vec![
 			Box::new(ScrambleMuator::new(0.75)),
@@ -32,7 +32,7 @@ However, if more than one is desired it can be done as such:
 Alternatively, the `alterers` method and macro can be used to add both mutators and crossovers at the same time:
 
 ```rust
-	let engine = GeneticEngine::from_codex(codex)
+	let engine = GeneticEngine::builder()
 		...
 		.alterers(alters![
 			ScrambleMuator::new(0.75),

docs/examples.md

@@ -25,7 +25,8 @@ comprehensive list of examples.
     fn main() {
         let codex = IntCodex::vector(NUM_GENES, MIN_GENE_VALUE..MAX_GENE_VALUE);
 
-        let engine = GeneticEngine::from_codex(codex)
+        let engine = GeneticEngine::builder()
+            .codex(codex)
             .population_size(150)
             .minimizing()
             .offspring_selector(EliteSelector::new())
@@ -66,7 +67,8 @@ comprehensive list of examples.
 
         let codex = IntCodex::<i8, Vec<i8>>::vector(N_QUEENS, 0..N_QUEENS as i8);
 
-        let engine = GeneticEngine::from_codex(codex)
+        let engine = GeneticEngine::builder()
+            .codex(codex)
             .minimizing()
             .num_threads(5)
             .offspring_selector(BoltzmannSelector::new(4.0))
@@ -131,7 +133,8 @@ comprehensive list of examples.
         let capacity = knapsack.capacity;
         let codex = SubSetCodex::new(knapsack.items);
 
-        let engine = GeneticEngine::from_codex(codex)
+        let engine = GeneticEngine::builder()
+            .codex(codex)
             .max_age(MAX_EPOCHS)
             .fitness_fn(move |genotype: Vec<Arc<Item>>| Knapsack::fitness(&capacity, &genotype))
             .build();
@@ -268,7 +271,8 @@ comprehensive list of examples.
     fn main() {
         let codex = FloatCodex::vector(N_GENES, -RANGE..RANGE);
 
-        let engine = GeneticEngine::from_codex(codex)
+        let engine = GeneticEngine::builder()
+            .codex(codex)
             .minimizing()
             .population_size(500)
             .alter(alters!(
@@ -325,7 +329,8 @@ comprehensive list of examples.
             target: target.clone(),
         };
 
-        let engine = GeneticEngine::from_codex(codex.clone())
+        let engine = GeneticEngine::builder()
+            .codex(codex.clone())
             .minimizing()
             .num_threads(5)
             .offspring_selector(BoltzmannSelector::new(4_f32))
@@ -442,13 +447,12 @@ comprehensive list of examples.
 
 > Objective - Evolve a `Graph<Op<f32>>` to solve the XOR problem (NeuroEvolution).
 >
->  Warning - only available with the `radiate-gp` crate
+>  Warning - only available with the `gp` feature flag.
 
 ??? example
 
     ```rust
     use radiate::*;
-    use radiate_gp::*;
 
     const MAX_INDEX: i32 = 500;
     const MIN_SCORE: f32 = 0.01;
@@ -466,7 +470,8 @@ comprehensive list of examples.
         let graph_codex = GraphCodex::directed(2, 1, values);
         let regression = Regression::new(get_dataset(), Loss::MSE, graph_codex);
 
-        let engine = GeneticEngine::from_problem(regression)
+        let engine = GeneticEngine::builder()
+            .problem(regression)
             .minimizing()
             .alter(alters!(
                 GraphCrossover::new(0.5, 0.5),
@@ -515,13 +520,12 @@ comprehensive list of examples.
 
 ## Tree - Regression
 
-> Objective - Evolve a `Tree<Op<f32>>` to solve the a regression problem (Genetic Programming).
+> Objective - Evolve a `Tree<Op<f32>>` to solve the a regression problem (Genetic Programming) - `gp` feature flag.
 
 ??? example
 
     ```rust
     use radiate::*;
-    use radiate_gp::*;
 
     const MIN_SCORE: f32 = 0.01;
     const MAX_SECONDS: f64 = 1.0;
@@ -537,7 +541,8 @@ comprehensive list of examples.
         let tree_codex = TreeCodex::single(3, store).constraint(|root| root.size() < 30);
         let problem = Regression::new(get_dataset(), Loss::MSE, tree_codex);
 
-        let engine = GeneticEngine::from_problem(problem)
+        let engine = GeneticEngine::builder()
+            .problem(problem)
             .minimizing()
             .mutator(HoistMutator::new(0.01))
             .crossover(TreeCrossover::new(0.7))

docs/gp.md

@@ -1,7 +1,7 @@
 
 # Genetic Programming 
 
-The `radiate-gp` crate provides the fundamental building blocks for [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) (GP). It includes **data structures and algorithms** for building and evolving **`Tree`s** and **`Graph`s**.
+The `gp` feature provides the fundamental building blocks for [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) (GP). It includes **data structures and algorithms** for building and evolving **`Tree`s** and **`Graph`s**.
 
 Genetic Programming (GP) is an evolutionary algorithm that **evolves programs** to solve problems. Programs are represented as **expression trees, decision trees, random forests, or neural networks** and evolved over generations.
 

docs/quickstart.md

@@ -9,7 +9,7 @@ Radiate is inspired from a multitude of other genetic algorithm libraries, all o
 ## Configuration
 ```toml
 [dependencies]
-radiate = "1.2.10"
+radiate = "1.2.11"
 ```
 
 ## Core Implementations
@@ -37,15 +37,15 @@ Fitness Function
 
   * The fitness function evaluates how well an individual solves the problem at hand. It is a critical component that guides the evolutionary process by assigning scores to individuals based on their performance.
 
-## Extensions
+## Features
 
-Radiate offers a separate crate called [radiate-gp](https://crates.io/crates/radiate-gp) which extend the core library with additional features. Mainly it provides data structures and algorithms which facilitate [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming#:~:text=In%20artificial%20intelligence%2C%20genetic%20programming,to%20the%20population%20of%20programs.) (GP) - probems that are represented as Trees or Graphs. This is a powerful extension to the core library, allowing users to tackle even more complex or unique problems. 
+Radiate offers a feature called `gp` which extend the core library with additional features. Mainly it provides data structures and algorithms which facilitate [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming#:~:text=In%20artificial%20intelligence%2C%20genetic%20programming,to%20the%20population%20of%20programs.) (GP) - probems that are represented as Trees or Graphs. This is a powerful extension to the core library, allowing users to tackle even more complex or unique problems. 
 
 To use it, add the following to your `Cargo.toml`:
 
 ```toml
 [dependencies]
-radiate-gp = "0.0.4"
+radiate = { version = "1.2.11", features = ["gp"] }
 ```
 
 !!! note

examples/graphs/regression-graph/Cargo.toml

@@ -4,4 +4,4 @@ version = "0.1.0"
 edition = "2024"
 
 [dependencies]
-radiate = { path = "../../../crates/radiate", features = ["gp"] }
+radiate = { workspace = true, features = ["gp"] }