Permutation-based genetic algorithm

Q: How deeply does Optimization Algorithms: AI techniques for design, planning, and control problems cover permutation-based genetic algorithm?

Defines valid permutation encoding and operator purpose, while deferring detailed crossover and mutation methods.

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

A genetic algorithm that represents candidate solutions as permutations of discrete elements.

A permutation-based genetic algorithm solves combinatorial optimization problems by representing each candidate as an ordered sequence of distinct elements. It is useful when order, adjacency, resources, or time constraints determine solution quality. section 8.3

Typical applications include:

Traveling salesman and vehicle routing problems, where adjacency and travel distance matter. section 8.3
Sports tournament and job scheduling, where the sequence of events affects resource or time usage. section 8.3
Ride-sharing scheduling, where ordering helps allocate vehicles and drivers efficiently. section 8.3

A tour as a permutation

For an eight-city traveling salesman problem, a candidate tour can be represented as [1, 2, 3, 4, 5, 6, 7, 8]. Specialized crossover and mutation operators preserve validity by keeping every city exactly once. section 8.3

Crossover mainly exploits useful genetic material from parent solutions, while mutation can support exploration or exploitation depending on its rate and step size. Crossover is generally assigned a relatively high rate and mutation a low rate. section 8.9

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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Related concepts

genetic algorithmkind of · 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.
mutationuses · Mutation is a change to an existing value, object, data structure, state, or representation.
crossoveruses · Crossover is an evolutionary recombination technique that combines material from parent candidate solutions to produce one or more offspring.

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