Genetic Algorithm Phd Thesis

Genetic Algorithm Phd Thesis-55
Agents could use many techniques for placement optimization.Currently it uses the class to implement a genetic algorithm.

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These assumptions leave out only the guided random search techniques.

Their use of additional information to guide the search reduces the search space to manageable sizes.

Even though agent objects use knowledge to reduce search time, a great deal of searching is still necessary.

A good proportion of this search time will be spent optimizing the components' placement in the layout.

Guided random search techniques are based on enumerative techniques but use additional information to guide the search.

Two major subclasses are simulated annealing and evolutionary algorithms.Although evolution manifests itself as changes in the species' features, it is in the species' genetical material that those changes are controlled and stored.Specifically evolution's driving force is the combination of natural selection and the change and recombination of genetic material that occurs during reproduction [17].Enumerative techniques search every point related to the function's domain space (finite or discretized), one point at a time.They are very simple to implement but usually require significant computation.These techniques are not suitable for applications with large domain spaces.Dynamic programming is a good example of this technique.Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies.Placement and routing are two search intensive tasks.In nature, individuals best suited to competition for scanty resources survive.Evolving to keep adapted to a changing environment is essential for the members of any species.

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