Roulette-wheel selection

As explained in Classic Computer Science Problems in Swift: Essential techniques for practicing programmers

A genetic algorithm selection technique that chooses chromosomes with probabilities proportional to their fitness.

How Classic Computer Science Problems in Swift: Essential techniques for practicing programmers covers it

How deep it goes

Defines the method and shows its Swift picker, probability construction, and integration with parent reproduction.

Examples

Demonstrates normalized fitness shares, cumulative random selection, and configurable roulette-based parent picking.

What it assumes

Assumes chromosomes expose fitness and participate in reproduction through crossover and mutation.

What you can do after

Build it from scratch as a configurable parent-selection strategy in a generic genetic algorithm.

How it connects

Connects fitness, genetic algorithms, chromosomes, reproduction, crossover, mutation, and tournament selection.

liveBookmanning.com

Read Classic Computer Science Problems in Swift: Essential techniques for practicing programmers on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Optimization Algorithms: AI techniques for design, planning, and control problemsA fitness-proportionate selection method that gives higher-selection-probability individuals with better fitness values.
• 

  liveBookmanning.com
  Advanced Algorithms and Data StructuresRoulette wheel selection is a genetic-algorithm technique that assigns each individual a selection probability proportional to its fitness.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionRoulette-wheel selection is a probabilistic evolutionary selection technique that assigns larger selection probabilities to higher-fitness candidates while retaining nonzero probabilities for lower-fitness candidates.

Compare how 4 books explain roulette-wheel selection →

Related concepts

• tournament selectionalternative to · Tournament selection is an evolutionary technique that randomly samples groups of candidate solutions and selects the highest-fitness candidate from each group.
• selectionkind of · Selection is an evolutionary optimization operation that chooses candidate solutions for continuation or reproduction according to their fitness or quality.
• ant colony optimizationused by · Ant colony optimization is a population-based probabilistic optimization technique in which artificial agents use simulated pheromone trails to guide the search for good solutions.
• genetic algorithmused by · A genetic algorithm is an evolutionary optimization method that improves a population of candidate solutions through fitness-based selection and variation, often including recombination and mutation.
• rank selectioncontrasts with · Rank selection is an evolutionary selection technique that assigns selection probabilities according to individuals’ relative fitness rankings rather than raw fitness values.
• fitnessuses · Fitness is a numerical score indicating how well a candidate solution performs relative to an optimization objective.

This explainer was generated by Manning's knowledge graph from the text of Classic Computer Science Problems in Swift: Essential techniques for practicing programmers. The section links point to the parts of the book it draws on.