Merge branch 'develop'

Author: pkalivas

Cargo.toml

@@ -50,7 +50,7 @@ exclude = [
 ]
 
 [workspace.package]
-version = "1.2.16"
+version = "1.2.17"
 edition = "2024"
 authors = ["pkalivas <peterkalivas@gmail.com>"]
 description = "A Rust library for genetic algorithms and artificial evolution."
@@ -70,12 +70,12 @@ serde = { version = "1.0.219", features = ["derive"] }
 serde_json = { version = "1.0.143" }
 tracing = "0.1"
 tracing-subscriber = { version = "0.3.20", features = ["env-filter", "fmt"] }
-radiate = { version = "1.2.16", path = "crates/radiate", default-features = false }
-radiate-core = { version = "1.2.16", path = "crates/radiate-core", default-features = false }
-radiate-alters = { version = "1.2.16", path = "crates/radiate-alters", default-features = false }
-radiate-selectors = { version = "1.2.16", path = "crates/radiate-selectors", default-features = false }
-radiate-engines = { version = "1.2.16", path = "crates/radiate-engines", default-features = false }
-radiate-gp = { version = "1.2.16", path = "crates/radiate-gp", default-features = false }
-radiate-error = { version = "1.2.16", path = "crates/radiate-error", default-features = false }
-radiate-python = { version = "1.2.16", path = "crates/radiate-python", default-features = false }
+radiate = { version = "1.2.17", path = "crates/radiate", default-features = false }
+radiate-core = { version = "1.2.17", path = "crates/radiate-core", default-features = false }
+radiate-alters = { version = "1.2.17", path = "crates/radiate-alters", default-features = false }
+radiate-selectors = { version = "1.2.17", path = "crates/radiate-selectors", default-features = false }
+radiate-engines = { version = "1.2.17", path = "crates/radiate-engines", default-features = false }
+radiate-gp = { version = "1.2.17", path = "crates/radiate-gp", default-features = false }
+radiate-error = { version = "1.2.17", path = "crates/radiate-error", default-features = false }
+radiate-python = { version = "1.2.17", path = "crates/radiate-python", default-features = false }
 

crates/radiate-core/src/genome/chromosomes/float.rs

@@ -1,8 +1,8 @@
 use super::{
     Chromosome,
-    gene::{ArithmeticGene, Gene, Valid},
+    gene::{ArithmeticGene, BoundedGene, Gene, Valid},
 };
-use crate::{chromosomes::gene::BoundedGene, random_provider};
+use crate::random_provider;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 use std::{

docs/source/alterers.md

@@ -645,8 +645,8 @@ Continuing with our example from the previous two sections - evolving a simple f
     # Create a codec for two parameters (a and b)
     codec = rd.FloatCodec.vector(
         length=2,                   # We need two parameters: a and b
-        value_range=(-1.0, 1.0),    # Start with values between -1 and 1
-        bound_range=(-10.0, 10.0)   # Allow evolution to modify the values between -10 and 10
+        init_range=(-1.0, 1.0),    # Start with values between -1 and 1
+        bounds=(-10.0, 10.0)       # Allow evolution to modify the values between -10 and 10
     )
 
     # Use Boltzmann selection for offspring - individuals which

docs/source/codec.md

@@ -55,7 +55,7 @@ Radiate provides several codec types out of the box that should be able to cover
     - Continuous optimization problems
     - Real-valued parameters
 
-    In all `FloatCodec` varients, the `bound_range` is optional and defaults to the `value_range` if not specified.
+    In all `FloatCodec` varients, the `bounds` is optional and defaults to the `init_range` if not specified.
 
 
     === ":fontawesome-brands-python: Python"
@@ -64,16 +64,16 @@ Radiate provides several codec types out of the box that should be able to cover
         import radiate as rd
 
         # For a single parameter
-        codec = rd.FloatCodec.scalar(value_range=(0.0, 1.0), bound_range=(-10.0, 10.0))
+        codec = rd.FloatCodec.scalar(init_range=(0.0, 1.0), bounds=(-10.0, 10.0))
 
         # For a list of parameters
-        codec = rd.FloatCodec.vector(length=5, value_range=(-1.0, 1.0), bound_range=(-10.0, 10.0))
+        codec = rd.FloatCodec.vector(length=5, init_range=(-1.0, 1.0), bounds=(-10.0, 10.0))
 
         # For a matrix of parameters (like neural network weights)
-        codec = rd.FloatCodec.matrix(shape=(3, 2), value_range=(-0.1, 0.1), bound_range=(-1.0, 1.0))
+        codec = rd.FloatCodec.matrix(shape=(3, 2), init_range=(-0.1, 0.1), bounds=(-1.0, 1.0))
         # -- or --
         # supply a list of shapes for jagged matrices e.g. matrix with three rows (chromosomes) and two columns (genes)
-        codec = rd.FloatCodec.matrix([2, 2, 2], value_range=(-0.1, 0.1), bound_range=(-1.0, 1.0))
+        codec = rd.FloatCodec.matrix([2, 2, 2], init_range=(-0.1, 0.1), bounds=(-1.0, 1.0))
         ```
 
     === ":fontawesome-brands-rust: Rust"
@@ -107,24 +107,24 @@ Radiate provides several codec types out of the box that should be able to cover
     - Array indices
     - Configuration parameters that must be whole numbers
 
-    In all `IntCodec` varients, the `bound_range` is optional and defaults to the `value_range` if not specified. 
+    In all `IntCodec` varients, the `bounds` is optional and defaults to the `init_range` if not specified.
 
     === ":fontawesome-brands-python: Python"
 
         ```python
         import radiate as rd
 
         # For a single parameter
-        codec = rd.IntCodec.scalar(value_range=(0, 1), bound_range=(-10, 10))
+        codec = rd.IntCodec.scalar(init_range=(0, 1), bounds=(-10, 10))
 
         # For a list of parameters
-        codec = rd.IntCodec.vector(length=5, value_range=(-1, 1), bound_range=(-10, 10))
+        codec = rd.IntCodec.vector(length=5, init_range=(-1, 1), bounds=(-10, 10))
 
         # For a matrix of ints
-        codec = rd.IntCodec.matrix(shape=(3, 2), value_range=(-1, 1), bound_range=(-10, 10))
+        codec = rd.IntCodec.matrix(shape=(3, 2), init_range=(-1, 1), bounds=(-10, 10))
         # -- or --
         # supply a list of shapes for jagged matrices e.g. matrix with three rows (chromosomes) and two columns (genes)
-        codec = rd.IntCodec.matrix([2, 2, 2], value_range=(-1, 1), bound_range=(-10, 10))
+        codec = rd.IntCodec.matrix([2, 2, 2], init_range=(-1, 1), bounds=(-10, 10))
         ```
 
     === ":fontawesome-brands-rust: Rust"
@@ -415,8 +415,8 @@ Let's look at a basic example of how to use the `Codec` for evolving a simple fu
     # Create a codec for two parameters (a and b)
     codec = rd.FloatCodec.vector(
         length=2,                   # We need two parameters: a and b
-        value_range=(-1.0, 1.0),    # Start with values between -1 and 1
-        bound_range=(-10.0, 10.0)   # Allow evolution to modify the values between -10 and 10
+        init_range=(-1.0, 1.0),    # Start with values between -1 and 1
+        bounds=(-10.0, 10.0)       # Allow evolution to modify the values between -10 and 10
     )
 
     # Create the evolution engine

docs/source/diversity.md

@@ -228,9 +228,9 @@ Lets add on to our example - evolving a simple function: finding the best values
 
     # Create a codec for two parameters (a and b)
     codec = rd.FloatCodec.vector(
-        length=2,                   # We need two parameters: a and b
-        value_range=(-1.0, 1.0),    # Start with values between -1 and 1
-        bound_range=(-10.0, 10.0)   # Allow evolution to modify the values between -10 and 10
+        length=2,                  # We need two parameters: a and b
+        init_range=(-1.0, 1.0),    # Start with values between -1 and 1
+        bounds=(-10.0, 10.0)       # Allow evolution to modify the values between -10 and 10
     )
 
     # Use Boltzmann selection for offspring - individuals which

docs/source/executors.md

@@ -30,9 +30,9 @@ Continuing with our example from the preious sections - evolving a simple functi
 
     # Create a codec for two parameters (a and b)
     codec = rd.FloatCodec.vector(
-        length=2,                   # We need two parameters: a and b
-        value_range=(-1.0, 1.0),    # Start with values between -1 and 1
-        bound_range=(-10.0, 10.0)   # Allow evolution to modify the values between -10 and 10
+        length=2,                  # We need two parameters: a and b
+        init_range=(-1.0, 1.0),    # Start with values between -1 and 1
+        bounds=(-10.0, 10.0)       # Allow evolution to modify the values between -10 and 10
     )
 
     # Use Boltzmann selection for offspring - individuals which

docs/source/fitness.md

@@ -45,7 +45,7 @@ Simple fitness functions are the most common type - they take a phenotype and re
             value += x[i]**2 - A * math.cos((2.0 * 3.141592653589793 * x[i]))
         return value
 
-    codec = rd.FloatCodec.vector(N_GENES, (-RANGE, RANGE))
+    codec = rd.FloatCodec.vector(N_GENES, init_range=(-RANGE, RANGE))
     engine = rd.GeneticEngine(codec, fitness_fn, objectives="min")
     ```
 

docs/source/genome.md

@@ -45,10 +45,10 @@ Certain `Genes` have additional functionality that allows them to be manipulated
 
         # Create a float gene that can evolve between -1.0 and 1.0 but 
         # must stay within -10.0 to 10.0 during evolution
-        gene = rd.FloatGene(
+        gene = rd.gene.float(
             allele=0.5,                   # Current value
-            value_range=(-1.0, 1.0),      # Initial range
-            bound_range=(-10.0, 10.0)     # Evolution bounds
+            init_range=(-1.0, 1.0),      # Initial range
+            bounds=(-10.0, 10.0)     # Evolution bounds
         )
         ```
 
@@ -83,10 +83,10 @@ Certain `Genes` have additional functionality that allows them to be manipulated
         import radiate as rd
 
         # Create an integer gene that can evolve between -100 and 100
-        gene = rd.IntGene(
+        gene = rd.gene.int(
             allele=42,                     # Current value
-            value_range=(-10, 10),        # Initial range
-            bound_range=(-100, 100)       # Evolution bounds
+            init_range=(-10, 10),        # Initial range
+            bounds=(-100, 100)       # Evolution bounds
         )
         ```
 
@@ -119,7 +119,7 @@ Certain `Genes` have additional functionality that allows them to be manipulated
 
         # Create an bit gene with an allele of True - if the allele isn't specified, it will 
         # be randomly initialized to True or False
-        gene = rd.BitGene(allele=True)
+        gene = rd.gene.bit(allele=True)
         ```
 
     === ":fontawesome-brands-rust: Rust"
@@ -144,10 +144,10 @@ Certain `Genes` have additional functionality that allows them to be manipulated
         import radiate as rd
 
         # Create a character gene with an allele of 'A'
-        gene = rd.CharGene(allele='A')
+        gene = rd.gene.char(allele='A')
 
         # Create a character gene with a randomly generated allele from the set 'abc'
-        gene = rd.CharGene(char_set='abc')  
+        gene = rd.gene.char(char_set='abc')  
         ```
 
     === ":fontawesome-brands-rust: Rust"
@@ -214,10 +214,10 @@ Because each `Chromosome` has an associated `Gene`, the built int chromosomes ar
         import radiate as rd
 
         # Create a float chromosome with 5 genes, each initialized to a random value between -1.0 and 1.0
-        chromosome = rd.Chromosome.float(
+        chromosome = rd.chromosome.float(
             length=5, 
-            value_range=(-1.0, 1.0), 
-            bound_range=(-10.0, 10.0)
+            init_range=(-1.0, 1.0), 
+            bounds=(-10.0, 10.0)
         )
         ```
 
@@ -242,10 +242,10 @@ Because each `Chromosome` has an associated `Gene`, the built int chromosomes ar
         import radiate as rd
 
         # Create an integer chromosome with 5 genes, each initialized to a random value between -10 and 10
-        chromosome = rd.Chromosome.int(
-            length=5, 
-            value_range=(-10, 10), 
-            bound_range=(-100, 100)
+        chromosome = rd.chromosome.int(
+            length=5,
+            init_range=(-10, 10),
+            bounds=(-100, 100)
         )
         ```
 
@@ -270,7 +270,7 @@ Because each `Chromosome` has an associated `Gene`, the built int chromosomes ar
         import radiate as rd
 
         # Create a bit chromosome with 5 genes, each initialized to a random value of True or False
-        chromosome = rd.Chromosome.bit(length=5)
+        chromosome = rd.chromosome.bit(length=5)
         ```
 
     === ":fontawesome-brands-rust: Rust"
@@ -292,9 +292,9 @@ Because each `Chromosome` has an associated `Gene`, the built int chromosomes ar
         import radiate as rd
 
         # Create a character chromosome with 5 genes, each initialized to a random character from the ASCII printable characters
-        chromosome = rd.Chromosome.char(length=5)
+        chromosome = rd.chromosome.char(length=5)
 
-        chromosome_with_set = rd.Chromosome.char(length=5, char_set='abc')
+        chromosome_with_set = rd.chromosome.char(length=5, char_set='abc')
         ```
 
     === ":fontawesome-brands-rust: Rust"
@@ -345,20 +345,20 @@ Because of the typed nature of the `Genotype`, it can only hold a collection of
 
     # Create a genotype with a single FloatChromosome and a 5 FloatGenes
     genotype = rd.Genotype(
-        rd.Chromosome.float(length=5, value_range=(-1.0, 1.0))
+        rd.chromosome.float(length=5, init_range=(-1.0, 1.0))
     )
 
     # Create a genotype with a single FloatChromosome and a single FloatGene with a 
     # randomly generated allele between -1.0 and 1.0
     genotype = rd.Genotype(
-        rd.Chromosome.float(genes=rd.Gene.float(value_range=(-1.0, 1.0)))
+        rd.Chromosome([rd.gene.float(init_range=(-1.0, 1.0))])
     )
 
     # Create a genotype with multiple chromosomes of lengths 5, 15, and 3
     three_chromosome_genotype = rd.Genotype([
-        rd.Chromosome.float(length=5, value_range=(-1.0, 1.0)),
-        rd.Chromosome.float(length=15, value_range=(-1.0, 1.0)),
-        rd.Chromosome.float(length=3, value_range=(-1.0, 1.0))
+        rd.chromosome.float(length=5, init_range=(-1.0, 1.0)),
+        rd.chromosome.float(length=15, init_range=(-1.0, 1.0)),
+        rd.chromosome.float(length=3, init_range=(-1.0, 1.0))
     ])
     ```
 

docs/source/installation.md

@@ -51,7 +51,7 @@ Python's radiate package does not have any optional features - it is a single pa
 
 ```toml
 [dependencies]
-radiate = { version = "1.2.16", features = ["gp", "serde", "rayon"] }
+radiate = { version = "1.2.17", features = ["gp", "serde", "rayon"] }
 ```
 
 opt-in features include:

docs/source/objectives.md

@@ -37,7 +37,7 @@ The `minimizing()` method configures the genetic algorithm to find the minimum v
     ```python
     import radiate as rd
 
-    codec = rd.FloatCodec.vector(10, (0.0, 1.0))  # Example codec
+    codec = rd.FloatCodec.vector(10, init_range=(0.0, 1.0))  # Example codec
 
     engine = rd.GeneticEngine(
         codec=codec,