Elitism

Q: How deeply does Optimization Algorithms: AI techniques for design, planning, and control problems cover elitism?

Explains selection from old and new populations, ranking, and diversity trade-offs, then works through a ticket-pricing table.

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

Elitist selection is a survivor-selection method that retains the best individuals from the existing population and newly generated offspring.

Elitism is a genetic-algorithm survivor-selection method that preserves the highest-fitness individuals from the existing population and newly generated offspring. It protects the best solutions from being lost, but applies the highest selective pressure and can reduce diversity. section 7.3.3 section 7.3.5

Apply elitist selection by following these steps:

Combine the old population with the new offspring. section 7.3.5
Rank the combined individuals by fitness and select the best candidates for the next population. section 7.3.5
Tune the number of preserved individuals carefully: preserving more elites lowers the succeeding population’s diversity and can encourage premature convergence. section 7.3.3

Ticket-pricing population

Among old candidates with objective values 8,820, 84,420, 90,000, 128,500, and 18,000, the best old solutions are retained. Offspring with values 20,980 and 76,420 can also enter the next population when they outrank weaker old individuals; the resulting selected set contains candidates ranked 1 through 5 from both groups. section 7.3.5

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

Cover of Optimization Algorithms: AI techniques for design, planning, and control problems

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Also explained in

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Advanced Algorithms and Data StructuresElitism is a genetic-algorithm strategy that copies selected high-quality individuals to the next population without alteration.

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Related concepts

selectionkind of · Selection is an evolutionary optimization operation that chooses candidate solutions for continuation or reproduction according to their fitness or quality.
roulette-wheel selectionuses · Roulette-wheel selection is an evolutionary selection technique that assigns candidates probabilities proportional to their fitness, typically giving lower-fitness candidates nonzero probabilities.
tournament selectionuses · Tournament selection is an evolutionary technique that randomly samples groups of candidate solutions and selects the highest-fitness candidate from each group.
rank selectionuses · Rank selection is an evolutionary selection technique that assigns selection probabilities according to individuals’ relative fitness rankings rather than raw fitness values.
crossovercontrasts with · Crossover is an evolutionary recombination technique that combines material from parent candidate solutions to produce one or more offspring.
diversitycontrasts with · Diversity is the degree of variation among members of a set or population in relevant characteristics, perspectives, behaviors, or outcomes.

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