Introdução teórica aos algoritmos genéticos

Summary

This lecture introduces genetic algorithms, starting with the initial population creation, where individuals are represented as chromosomes with genes corresponding to flights. The script explains the evaluation using a fitness function, aiming to minimize costs and waiting times. It covers selection techniques like percentage-based and roulette wheel selection, and genetic operators like crossover and mutation. The process iterates until a stopping criterion, often a generation number, is met, ultimately yielding optimal solutions.

Takeaways

• 🌟 Genetic algorithms are used to solve optimization problems, such as flight scheduling, by simulating the process of natural evolution.
• 🧬 The initial step involves creating a random population of potential solutions, each represented as a chromosome composed of genes (integer values representing flights).
• 🔢 Each individual in the population is evaluated using a fitness function that considers factors like cost and waiting time to determine the quality of the solution.
• 🏆 Selection of parents is based on their fitness scores, with better solutions having a higher chance of being selected for producing offspring.
• 🎯 The crossover operator combines parts of two selected parent solutions to create new offspring, simulating genetic recombination.
• 🔄 Mutation is introduced by randomly altering a gene in a chromosome, which represents a change in the flight schedule, introducing diversity into the population.
• 🔄 The mutation process is controlled by a defined probability and can adjust genes by a set step value to maintain variability.
• 🔄 After generating new individuals through crossover and mutation, the population is re-evaluated to determine their fitness.
• 🔄 A survival selection process is applied to retain the best individuals, either by setting a percentage of the population to survive or using methods like roulette selection.
• 🔁 The genetic algorithm iteratively applies selection, crossover, mutation, and evaluation until a stopping criterion, such as a set number of generations, is met.
• 🏁 The final output is a set of the best solutions found over all generations, which aim to minimize both cost and waiting time for the flight scheduling problem.

Q & A

What is the initial step in a genetic algorithm as described in the script?

The initial step in a genetic algorithm is creating an initial population, which consists of a set of solutions or individuals, represented by various possible solutions to the problem.

How are individuals in a genetic algorithm represented?

Individuals in a genetic algorithm are represented as a set of genes, where each gene corresponds to a specific part of the solution, such as a flight in the example given.

What is the significance of the number 'TR' in the context of the flights representation?

The number 'TR' represents the fourth flight of the day in the script's example, indicating the sequence of flights a person might take.

How does the genetic algorithm generate random solutions?

The genetic algorithm generates random solutions by assigning random integers between 0 and 9 to represent the flights, with 0 indicating the first flight of the day and 9 indicating the last flight.

What is the purpose of the fitness function in a genetic algorithm?

The fitness function in a genetic algorithm is used to evaluate each individual's solution, determining the cost of each solution, which includes minimizing both the flight ticket prices and the waiting time at the airport.

What is the role of the stopping criterion in a genetic algorithm?

The stopping criterion in a genetic algorithm determines when to end the algorithm's execution, which can be based on a set number of iterations or when a satisfactory solution is found.

How are the best individuals selected for the next generation in the genetic algorithm?

The best individuals are selected based on their fitness scores, either by defining a percentage of the best individuals to keep or using methods like the roulette wheel selection.

What is crossover in the context of genetic algorithms?

Crossover is a genetic operator that combines parts of two selected solutions (parents) to create new offspring, done by selecting a cut point and swapping the segments of the parent solutions.

How is mutation implemented in a genetic algorithm?

Mutation in a genetic algorithm involves randomly altering a gene in a chromosome with a certain probability, mimicking the natural mutation process to introduce variation in the gene pool.

What is the purpose of evaluating the new population after applying genetic operators?

Evaluating the new population after applying genetic operators helps in identifying which individuals are better suited to the problem, allowing the algorithm to retain the best solutions and discard the less optimal ones.

How does the genetic algorithm ensure that the best solutions are not lost over generations?

The genetic algorithm ensures that the best solutions are not lost by selecting the best individuals from the current population to create the next generation, thus preserving the best traits found so far.