How deeply does Advanced Algorithms and Data Structures cover selection?

Defines four techniques and gives pseudocode for tournament and roulette-wheel implementation, but does not develop full algorithm integration.

Selection

As explained in Advanced Algorithms and Data Structures

A selection technique is a genetic-algorithm method for choosing individuals that survive or reproduce in the next generation.

Selection chooses which genetic-algorithm individuals reproduce or survive into the next generation. In a knapsack optimization, it can preserve strong solutions, block weak ones, or probabilistically favor fitter organisms. section 18.1.6

Choose a technique according to the desired balance between preserving fitness and exploring alternatives:

Elitism copies the best individuals unchanged, ensuring that the best population fitness improves monotonically across generations, though average fitness need not improve. section 18.1.6
Thresholding excludes individuals below a fixed fitness requirement or discards a chosen number of the weakest individuals. section 18.1.6

How Advanced Algorithms and Data Structures covers it

Cover of Advanced Algorithms and Data Structures

liveBook manning.com

Read Advanced Algorithms and Data Structures on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Optimization Algorithms: AI techniques for design, planning, and control problemsA genetic-algorithm procedure that chooses individuals from a population to serve as parents for reproduction.
liveBook manning.com

Evolutionary Deep Learning: Genetic algorithms and neural networksA selection operator chooses individuals from a population for reproduction or survival according to their fitness.
liveBook manning.com

Grokking AI Algorithms, Second EditionSelection is an evolutionary algorithm operation that chooses candidate solutions for continuation or reproduction based on their quality.
liveBook manning.com

Inside AISelection is a genetic-algorithm operation that retains candidate solutions with comparatively high fitness for further evolution.

Compare how 5 books explain selection →

Related concepts

measurementequivalent to · Measurement is the systematic assessment of a property, behavior, or outcome that produces an observed value, score, or judgment.
tournament selectionincludes · Tournament selection is an evolutionary technique that randomly samples groups of candidate solutions and selects the highest-fitness candidate from each group.
elitismincludes · Elitism is an evolutionary strategy that preserves high-performing individuals unchanged when forming the next generation.
fitnessuses · Fitness is a numerical score indicating how well a candidate solution performs relative to an optimization objective.
roulette-wheel selectionincludes · Roulette-wheel selection is an evolutionary selection technique that assigns candidates probabilities proportional to their fitness, typically giving lower-fitness candidates nonzero probabilities.
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.

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.