genetic algo

applications of genetic algorithms

working of genetic algorithm

Termination

Crossover

Mutation

Selection

Initialization

genetic algorithm

• Objective Function:

The objective function (objective(x)) defines what we're trying to optimize. In this case, it's a simple function that takes a list of two variables (x) and returns their sum multiplied by 2.

• Encoding and Decoding:

The decode() function translates binary strings into numerical values within predefined bounds. In our case, it takes a binary string representing each variable, converts it into an integer, and scales it to fit within specified bounds

genetic algorithm

• Selection:

The selection() function implements tournament selection, a method to choose individuals (bitstrings) from the population based on their fitness scores. It randomly selects individuals and compares their fitness, choosing the fittest individual.

• Crossover:

The crossover() function combines genetic information from two parents to create offspring. It randomly selects a crossover point and swaps genetic material between parents to create two new individuals..

genetic algorithm

• Mutation:

The mutation() function introduces random changes in individuals with a certain mutation rate. It flips bits in the binary string representation of individuals to add diversity to the population.

genetic algorithm

• Genetic Algorithm:

The genetic_algorithm() function is where the main genetic algorithm loop occurs. It initializes a population of random bitstrings, evaluates their fitness using the objective function, selects parents, applies crossover and mutation to create the next generation, and repeats this process for a specified number of iterations.

genetic algorithm

• Parameters:

We define parameters such as the search space bounds, number of iterations, population size, crossover rate, and mutation rate to control the behavior of the genetic algorithm. Execution: Finally, we execute the genetic algorithm by calling the genetic_algorithm() function with the defined parameters. After execution, we print the best solution found and its corresponding fitness value.

Applications

Optimization

GAs are most commonly used in optimization problem where we have to maximize or minimize a given objective function value under a given set of constraints.

Neural Networks

GAs are also used to train neural network specially recurrent neural network.

Image Processing

GAs are used for various digital image processing tasks.

applications

DNA Analysis

GAs are used to determine the structure of DNA

Travelling Salesman Problem

GAs are used to solve the Travelling Salesman Probem.

Scheduling Applications

GAs are used in solving various scheduling problems.

Applications

Machine Learning

GAs are also used in GBML(Genetic Based Machine Learning).

Parametric design of Aircraft

GAs are also used to design aircrafts by varying the parameters and evolving better solutions.

Robotics

GAs are used to create learning robots which will behave as a human.

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