Introdução teórica aos algoritmos genéticos

IA Expert Academy

31 Mar 202219:16

Summary

TLDRThis 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.

Outlines

00:00

🧬 Introduction to Genetic Algorithms

This paragraph introduces the concept of genetic algorithms, focusing on their application to a flight scheduling problem. It explains the initial step of creating an initial population, which is a set of potential solutions represented by individuals or chromosomes. Each chromosome is a set of genes, where genes correspond to flights or integer numbers. The representation of the problem is done using integers, ranging from 0 to 9, representing the flights available in the database. The process of generating random solutions is emphasized, highlighting the importance of the initial population in genetic algorithms.

05:02

📊 Evaluating the Population

The second paragraph delves into the evaluation of the initial population using a fitness function. It discusses how each chromosome's cost is calculated by considering both the flight prices and waiting times at the airport. The aim is to minimize these costs. The paragraph also introduces the concept of a stopping criterion for the genetic algorithm, which could be a set number of iterations. If the stopping criterion is not met, the algorithm enters a repetitive process that includes selection of parents, application of genetic operators, and evaluation of the new population.

10:03

🔄 Selection and Genetic Operators

This section explains the process of selecting parent chromosomes for the next generation. It describes two common selection methods: percentage-based selection and roulette wheel selection. The paragraph then moves on to discuss genetic operators, specifically crossover, which combines parts of two parent chromosomes to create offspring. The crossover process is described in detail, emphasizing the random selection of a cut point and the swapping of gene segments between parents. The paragraph also touches on mutation, which is the alteration of a gene in a chromosome, simulating natural genetic mutation.

15:06

🔄 Crossover and Mutation in Action

The fourth paragraph continues the discussion on genetic operators, providing a detailed example of how crossover is performed with specific chromosome sequences. It then explains the mutation operator, which involves randomly selecting a gene and altering it based on a predefined mutation probability. The paragraph also discusses how mutation can be controlled by setting parameters such as the step size for gene alteration. The paragraph concludes by emphasizing that genetic operators are used to create new individuals with characteristics inherited from their parents, simulating natural genetic processes.

Mindmap

Keywords

💡Genetic Algorithm

A genetic algorithm is a type of evolutionary algorithm used to find optimal solutions to complex problems. It is inspired by the process of natural selection, where the strongest individuals are selected for breeding to produce offspring. In the context of the video, genetic algorithms are used to find the best solution for flight scheduling, aiming to minimize both cost and waiting time.

💡Population

In the script, 'population' refers to a set of possible solutions or individuals, each representing a potential answer to the problem at hand. The initial population is randomly generated, and each individual within this population is evaluated for its fitness. The video uses the example of a population of five individuals, each with different flight arrangements, to illustrate this concept.

💡Chromosome

A chromosome, in the context of genetic algorithms, is an array of genes that represents a single solution to the problem. Each chromosome is a vector of integers, where each integer represents a flight option. The video explains that chromosomes are composed of genes, and together they form a complete solution, such as a set of flights for a person.

💡Gene

A gene is the basic unit of遗传 information in a chromosome. In the video, each gene represents a specific flight option, such as the second flight of the day to Rome or the fourth flight back. Genes are the integers within the chromosome array that define the solution.

💡Fitness Function

The fitness function is a key component of genetic algorithms that evaluates the quality of each solution. It measures how well an individual meets the problem's objectives. In the video, the fitness function is used to assess the cost of each flight arrangement, with the goal of minimizing both flight cost and waiting time at the airport.

💡Selection

Selection is the process of choosing the best individuals from the current population to be parents and pass their genes to the next generation. The video describes two common selection methods: selecting a percentage of the best individuals or using a roulette wheel selection based on their fitness scores.

💡Crossover

Crossover, also known as recombination, is a genetic operator used to combine the genes of two parents to create offspring. The video explains that a random cut point is selected, and genes from each parent are swapped to form new chromosomes, which are then evaluated for their fitness.

💡Mutation

Mutation is a genetic operator that introduces random changes in a gene to maintain diversity within the population. The video describes how a gene might be altered, for example, changing a flight number from 6 to 7, to ensure that the population does not become too similar and to explore new potential solutions.

💡Termination Criterion

A termination criterion is a condition that stops the genetic algorithm once a certain goal is met or a certain number of generations have passed. The video mentions running the algorithm for a set number of iterations, such as 50 or 100 times, as a possible termination criterion.

💡Generation

In genetic algorithms, a generation refers to a complete cycle of the algorithm, including selection, crossover, mutation, and evaluation. The video discusses how each generation produces new individuals based on the parents, and the process is repeated until the termination criterion is met.

💡Survivor Selection

Survivor selection is the process of choosing which individuals from the current generation will continue to the next generation. The video explains that this can be done by maintaining a certain percentage of the best individuals, as determined by their fitness scores.

Highlights

Introduction to genetic algorithms theory

Representation of flight scheduling problem

Explanation of initial population composition

Random generation of solutions for the initial population

Importance of individual and chromosome concepts

Role of genes in representing flights or integers

Evaluation of the initial population using fitness function

Minimizing cost and waiting time as the fitness goal

Criteria for stopping the genetic algorithm

Selection of parents from the population

Techniques for selecting the best individuals

Roulette selection method explained

Crossover operator to combine parents

Mutation operator to introduce gene changes

Evaluation of new population and selection process

Survivor selection method by percentage or roulette

Execution of genetic algorithm processes iteratively

Termination of algorithm after reaching a set number of generations

Listing the best individuals from all generations

Practical implementation of genetic algorithms in the next lesson