Class: Musa::Darwin::Darwin

Inherits:
Object show all
Defined in:
lib/musa-dsl/generative/darwin.rb

Overview

Evolutionary selector for population-based optimization.

Evaluates population using measures and weights, returning sorted population by fitness score.

Defined Under Namespace

Classes: MainContext, Measure, MeasuresEvalContext

Instance Method Summary collapse

Constructor Details

#initialize { ... } ⇒ void

Creates Darwin selector with evaluation rules.

Examples:

darwin = Darwin.new do
  measures { |obj| dimension :value, obj[:score] }
  weight value: 1.0
end

Yields:

  • evaluation DSL block

Yield Returns:

  • (void)

Raises:

  • (ArgumentError) —

    if no block given



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# File 'lib/musa-dsl/generative/darwin.rb', line 112

def initialize(&block)
  raise ArgumentError, 'block is needed' unless block

  main_context = MainContext.new &block

  @measures = main_context._measures
  @weights = main_context._weights
end

Instance Method Details

#evaluate_weights(measure_a, measure_b) ⇒ Integer

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Compares two measures by their weighted fitness.

Parameters:

  • measure_a (Measure) —

    first measure to compare

  • measure_b (Measure) —

    second measure to compare

Returns:

  • (Integer) —

    comparison result (-1, 0, 1)



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# File 'lib/musa-dsl/generative/darwin.rb', line 195

def evaluate_weights(measure_a, measure_b)
  measure_b.evaluate_weight(@weights) <=> measure_a.evaluate_weight(@weights)
end

#select(population) ⇒ Array

Selects and ranks population by fitness.

Evaluates each object with measures, normalizes dimensions across population, applies weights, and returns population sorted by fitness (highest first). Objects marked as died are excluded.

Examples:

simple = Darwin.new do
  measures { |o| dimension :complexity, -o[:complexity].to_f }
  weight complexity: 1.0
end

ranked = simple.select([{ complexity: 3 }, { complexity: 1 }, { complexity: 5 }])

ranked.first  # => { complexity: 1 }
ranked.last   # => { complexity: 5 }

Parameters:

  • population (Array) —

    objects to evaluate

Returns:

  • (Array) —

    population sorted by fitness (descending)



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# File 'lib/musa-dsl/generative/darwin.rb', line 141

def select(population)
  measured_objects = []

  population.each do |object|
    context = MeasuresEvalContext.new

    context.with object, **{}, &@measures
    measure = context._measure

    measured_objects << { object: object, measure: context._measure } unless measure.died?
  end

  limits = {}

  measured_objects.each do |measured_object|
    measure = measured_object[:measure]

    measure.dimensions.each do |measure_name, value|
      limit = limits[measure_name] ||= { min: nil, max: nil }

      limit[:min] = value.to_f if limit[:min].nil? || limit[:min] > value
      limit[:max] = value.to_f if limit[:max].nil? || limit[:max] < value

      limit[:range] = limit[:max] - limit[:min]
    end
  end

  # warn "Darwin.select: weights #{@weights}"

  measured_objects.each do |measured_object|
    measure = measured_object[:measure]

    measure.dimensions.each do |dimension_name, value|
      limit = limits[dimension_name]
      measure.normalized_dimensions[dimension_name] =
        limit[:range].zero? ? 0.5 : (value - limit[:min]) / limit[:range]
    end

    # warn "Darwin.select: #{measured_object[:object]} #{measured_object[:measure]} weight=#{measured_object[:measure].evaluate_weight(@weights).round(2)}"
  end

  measured_objects.sort! { |a, b| evaluate_weights a[:measure], b[:measure] }

  measured_objects.collect { |measured_object| measured_object[:object] }
end