Roulette-wheel selection

As explained in Advanced Algorithms and Data Structures

Roulette wheel selection is a genetic-algorithm technique that assigns each individual a selection probability proportional to its fitness.

How Advanced Algorithms and Data Structures covers it

How deep it goes

Explains cumulative-wheel construction and fitness ratios, but does not provide the complete selection loop.

Examples

Applies the technique to a 0-1 knapsack population and illustrates its cumulative-percentage array.

What it assumes

Assumes familiarity with genetic-algorithm populations, fitness, and mating selection.

What you can do after

Apply it by constructing cumulative fitness boundaries for weighted population selection.

How it connects

Connects genetic algorithms with fitness, populations, mating, elitism, thresholding, and tournament selection.

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Also explained in

• 

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

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  Classic Computer Science Problems in Swift: Essential techniques for practicing programmersA genetic algorithm selection technique that chooses chromosomes with probabilities proportional to their fitness.

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 Advanced Algorithms and Data Structures. The section links point to the parts of the book it draws on.