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GA Problems - SRERKHMKL
Genetic Algorithm And Machine Learning (SRM Institute of Science and Technology)
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GENETIC
ALGORTIHMS AND
ITS APPLICATIONS
PROBLEMS
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TSP using GA
Solving the Traveling Salesman Problem (TSP) using
Genetic Algorithms (GAs) involves representing
cities as individuals in a population, evolving
generations of solutions, and gradually improving
the tours to minimize the total distance traveled.
Here's a step-by-step guide on how to apply GAs to
solve the TSP:
1. Define the Problem:
Cities: Identify the cities you want to visit
and their coordinates.
Objective Function: Define the fitness function
to be minimized (e.g., total distance traveled).
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TSP using GA
2. Encoding:
Encode a potential solution (a tour) as a
permutation
of
city
indices.
Each
chromosome
represents a different tour.
3. Initialization:
Generate an initial population of tours randomly
or using heuristics like nearest neighbor or
insertion.
4. Fitness Evaluation:
Calculate the fitness of each tour in the
population based on the objective function (e.g.,
total distance).
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TSP using GA
5. Selection:
Use a selection mechanism (e.g., roulette wheel,
tournament selection) to choose parents for the next
generation with higher fitness being more likely to
be selected.
6. Crossover:
Apply
crossover
(e.g.,
order
crossover,
partially mapped crossover) to create offspring from
selected parents. Crossover generates new tours by
recombining the order of cities.
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TSP using GA
7. Mutation:
Apply mutation (e.g., swap mutation, inversion
mutation) to introduce small changes in the
offspring tours.
8. Offspring Generation:
Create a new population of tours by combining
the parents and offspring.
9. Termination Criteria:
Define stopping criteria, such as reaching a
maximum number of generations, a fitness threshold,
or no improvement over several generations.
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