Add MutationType::Discrete for completeness to visualize_mutation_types

Author: basvanwesting

README.md

@@ -129,9 +129,22 @@ Run with `cargo run --example [EXAMPLE_BASENAME] --release`
 * Custom Mutate implementation
     * See [examples/evolve_milp_custom_mutate](../main/examples/evolve_milp_custom_mutate.rs)
 
+## Heterogeneous Genotype Support
+
+MultiRangeGenotype supports heterogeneous chromosomes that mix different gene
+semantics (continuous values, discrete choices, booleans) within a single
+numeric type `T`.
+
 ## Mutation Type Visualization
 
-The library supports various mutation strategies that affect how the genetic algorithm explores the search space. The visualization below shows how different mutation types explore a 2D search space when searching for a target point:
+The library supports various mutation strategies that affect how the genetic
+algorithm explores the search space. Random leads to the best results overall.
+Random is the default and is supported by all Genotypes. 
+
+But for continues genotypes (RangeGenotype and MultiRangeGenotype) there are
+several alternatives. These might converge faster, but are all more sensitive to
+local optima than Random. The visualization below shows how different mutation
+types explore a 2D search space when searching for a target point:
 
 ![Mutation Types Exploration Patterns](examples/visualize_mutation_types.png)
 
@@ -141,15 +154,10 @@ The visualization demonstrates:
 - **RangeScaled**: Adaptive exploration that starts broad and narrows down (funnel-like convergence)
 - **Step**: Fixed-step local search in cardinal directions
 - **StepScaled**: Grid-like exploration with progressively finer resolution
+- **Discrete**: ListGenotype behaviour, for categories in heterogeneous genotypes
 
 Run the example with `cargo run --example visualize_mutation_types --release` to generate this visualization.
 
-## Heterogeneous Genotype Support
-
-MultiRangeGenotype supports heterogeneous chromosomes that mix different gene
-semantics (continuous values, discrete choices, booleans) within a single
-numeric type `T`.
-
 ## Performance considerations
 
 For the Evolve strategy:

examples/visualize_mutation_types.png

@@ -309,6 +309,14 @@ fn main() {
     );
     reporters.push(("StepScaled (halving)".to_string(), reporter));
 
+    // Discrete mutation - like ListGenotype for categories
+    println!("Running Discrete mutation...");
+    let reporter = run_evolution(MutationType::Discrete, "Discrete".to_string(), 50);
+    reporters.push((
+        "Discrete (floored integers, map to categories)".to_string(),
+        reporter,
+    ));
+
     // Generate visualization
     println!("\nGenerating visualization...");
     if let Err(e) = generate_plot(reporters, "examples/visualize_mutation_types.png") {
@@ -321,5 +329,6 @@ fn main() {
     println!("- RangeScaled: Funnel-like convergence, broad exploration then fine-tuning");
     println!("- Step: Local search with fixed step, smooth but may get stuck");
     println!("- StepScaled: Grid-like exploration with progressively finer resolution");
+    println!("- Discrete: ListGenotype behaviour, for categories in heterogeneous genotypes");
     println!("Color gradient: Blue (early) → Green (late) generations");
 }

examples/visualize_mutation_types.rs

@@ -115,6 +115,28 @@
 //! different gene semantics (continuous values, discrete choices, booleans) within a single
 //! numeric type `T`.
 //!
+//! ## [MutationType](crate::genotype::MutationType) Visualization
+//!
+//! The library supports various mutation strategies that affect how the genetic
+//! algorithm explores the search space. Random leads to the best results overall.
+//! Random is the default and is supported by all Genotypes.
+//!
+//! But for continues genotypes ([RangeGenotype](crate::genotype::RangeGenotype) and
+//! [MultiRangeGenotype](crate::genotype::MultiRangeGenotype)) there are several alternatives.
+//! These might converge faster, but are all more sensitive to local optima than Random. The
+//! visualization example shows how different mutation types explore a 2D search space when searching
+//! for a target point:
+//!
+//! The visualization demonstrates:
+//! - **Random**: Chaotic exploration, can jump anywhere in search space
+//! - **Range**: Local search with fixed radius around current position
+//! - **RangeScaled**: Adaptive exploration that starts broad and narrows down (funnel-like convergence)
+//! - **Step**: Fixed-step local search in cardinal directions
+//! - **StepScaled**: Grid-like exploration with progressively finer resolution
+//! - **Discrete**: ListGenotype behaviour, for categories in heterogeneous genotypes
+//!
+//! Run the example with `cargo run --example visualize_mutation_types --release` to generate the visualization.
+//!
 //! ## Performance considerations
 //!
 //! For the [Evolve](strategy::evolve::Evolve) strategy: