Genetic Algorithm How Genetic Algorithm Works Evolutionary Algorithm Machine Learning Mahesh Huddar

Summary

Mahesh Huddar's video delves into the fascinating world of Genetic Algorithms, exploring this heuristic search algorithm inspired by Charles Darwin's natural selection theory. The discussion covers the five key phases: initialization, fitness assignment, selection, crossover, and termination. Each phase is explained with clarity, emphasizing how the fittest individuals are selected for reproduction to create a new generation of solutions. The video also touches upon real-world applications and provides insights into selection and mutation techniques, with an engaging visual representation of the process. Whether you're new to this concept or brushing up on your knowledge, Huddar offers a clear and concise explanation, making complex ideas easy to grasp.

Highlights

• Charles Darwin's theory inspires Genetic Algorithms, simulating natural selection. 🧬
• Initialization begins with choosing a 'population' of solutions. 🌱
• Fitness assignment evaluates how 'fit' each solution is. 🚴‍♂️
• In the selection phase, only the fittest moves forward for reproduction. 🏆
• Crossover involves swapping genetic material to create 'offspring'. 🤝
• Mutation introduces random changes, keeping populations diverse. 🔄
• The algorithm terminates when solutions converge, indicating evolution success. 🏁

Key Takeaways

• Genetic Algorithms replicate natural selection to solve problems in machine learning. 🌿
• Five phases: Initialization, Fitness Assignment, Selection, Crossover, Termination. 🎯
• Selection of fittest individuals is crucial for effective reproduction. 💪
• Applications include image processing, circuit design, and artificial creativity. 🖼️🔧
• Mutation techniques like flipping and gaussian mutation introduce variation. 🎲

Overview

Mahesh Huddar presents a thorough exploration of Genetic Algorithms, diving into how these algorithms mimic the evolutionary processes of natural selection. Inspired by Darwin's theories, these algorithms harness the concept of reproduction among the fittest to produce new generations of solutions, thus adapting and overcoming various computational problems.

The process is broken down into five distinct phases: initialization, fitness assignment, selection, crossover, and termination. Huddar clearly explains how each phase contributes to the overall function of the algorithm, utilizing both verbal explanations and visual aids to enhance understanding. This comprehensive breakdown makes the topic accessible to learners at all levels.

Beyond the theoretical foundations, Huddar highlights some real-world applications of Genetic Algorithms, from image processing and electronic circuit design to artificial creativity. Additionally, he delves into different selection methods and mutations, providing a sneak peek into advanced concepts that intrigue enthusiasts and experts alike.

Chapters

• 00:00 - 00:30: Introduction to Genetic Algorithm In the 'Introduction to Genetic Algorithm' chapter, the concept of genetic algorithms in machine learning is introduced. Genetic algorithms are heuristic search algorithms that draw inspiration from Charles Darwin's theory of evaluation. These algorithms simulate the process of natural selection, selecting the fittest individuals to reproduce and create offspring for subsequent generations. The chapter sets the context for understanding how genetic algorithms mimic this natural process for problem-solving.
• 00:30 - 01:00: Real-world Applications of Genetic Algorithms Genetic algorithms leverage the concept of natural selection to evolve solutions, by selecting the fittest individuals and using them to reproduce and generate offspring for subsequent generations. This technique has been successfully integrated into genetic algorithms, which are now employed in a variety of real-world applications. These applications include image processing, designing electronic circuit boards, code breaking, artificial creativity, among others. The chapter delves into these applications to showcase the versatility and effectiveness of genetic algorithms in solving complex problems.
• 01:00 - 01:30: Phases of Genetic Algorithm The chapter titled 'Phases of Genetic Algorithm' outlines the five main phases involved in the operation of a genetic algorithm. These phases are: initialization, fitness assignment, selection, crossover or reproduction, and termination. The chapter begins by explaining the first phase, initialization, which involves the creation of the initial population. This step marks the beginning of the genetic algorithm process. Subsequent chapters or sections presumably delve into each of the remaining phases in further detail.
• 01:30 - 02:00: Initial Population in Genetic Algorithm This chapter discusses the concept of initial population in genetic algorithms. It uses the example of selecting a set of individuals, referred to as A1, A2, A3, and A4, to form the initial population. Each individual in this initial population represents a potential solution to the given problem definition, forming the basis for further evolution in the algorithm.
• 02:00 - 02:30: Chromosomes and Genes The chapter titled 'Chromosomes and Genes' explores the analogy between problem-solving in genetic algorithms and biological concepts. It begins by describing a scenario where individuals named A1, A2, A3, and A4 solve problems independently, which is likened to chromosomes within a genetic algorithm. Each chromosome acts as an individual entity within a population in the algorithm. Additionally, the chapter briefly introduces the concept of genes, which characterize each chromosome.
• 02:30 - 03:00: Fitness Function The chapter titled 'Fitness Function' introduces the concept of genes within a chromosome structure. Each gene can have a binary value of 0 or 1. By combining these genes, a chromosome is formed, and a set of chromosomes constitutes what is called a population. The chapter explains how the initial population is defined. Once this initial population is established, the next step involves assigning a fitness value to each individual solution or chromosome within the population.
• 03:00 - 04:00: Selection Phase The chapter discusses the Selection Phase in genetic algorithms, focusing on the fitness function. It explains the role of the fitness function in determining the 'fitness' or suitability of individual solutions (chromosomes). Based on the fitness score calculated by this function, individual solutions are selected for the next reproduction phase.
• 04:00 - 05:00: Crossover and Reproduction The chapter 'Crossover and Reproduction' discusses the next steps in a genetic algorithm after the crossover phase, focusing on the selection phase. This involves selecting the fittest individuals from a group. The transcript mentions selecting the fittest individuals, such as A1 and A2, or possibly A1 and A3, for reproduction and the next crossover. The selection relies on identifying the fittest solutions to proceed with.
• 05:00 - 06:00: Mutation Techniques The chapter "Mutation Techniques" begins by discussing the various steps to select particular individuals through three main methods: relative wheel selection, tournament selection, and rank-based reselection. The chapter briefly introduces these methods and mentions that a detailed discussion on each will be provided in the next video, with a link to be included in the description. The chapter concludes by introducing the next step in the process, which is the crossover, focusing on selecting the two fittest individuals.
• 06:00 - 07:00: Termination in Genetic Algorithm The chapter focuses on the concept of termination in genetic algorithms, with specific emphasis on the significant phase known as crossover. It explains the process of identifying a crossover point, where crossover is applied to select the fittest individuals or chromosomes, using individuals labeled as A1 and A2 as examples.
• 07:00 - 09:00: Flowchart of Genetic Algorithm Process In this chapter, the focus is on the 'Flowchart of Genetic Algorithm Process'. The process of crossover within genetic algorithms is explained. Specifically, it discusses how a crossover point, which in this case is 3, dictates where the genes from parent A1 and A2 are exchanged to create new offspring. The chapter describes that the genes are exchanged between the parents until the crossover point is reached, illustrating the process through a series of gene exchanges.
• 09:00 - 10:00: Conclusion and Call to Action This chapter concludes with a discussion on genetic algorithms, focusing on mutation techniques. It explains the concept of flipping genes within offspring with a low random probability. This is part of the broader discussion on evolutionary computation and optimization strategies. The chapter emphasizes the importance of mutations in facilitating diversity and adaptation within genetic algorithms.

Genetic Algorithm How Genetic Algorithm Works Evolutionary Algorithm Machine Learning Mahesh Huddar Transcription

• 00:00 - 00:30 hi welcome back in this video I will discuss genetic algorithm in machine learning genetic algorithm is a heuristic search algorithm in machine learning that is inspired by Charles Darwin's theory of evaluation this algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce The Offspring for the Next Generation so given a set of individuals in a natural selection we
• 00:30 - 01:00 will select the fittest individual using those particular fittest individual we will reproduce the new Offspring for the Next Generation so the same technique has been Incorporated in genetic algorithm also the genetic algorithms are being widely used in different real world applications such as image processing designing electronic circuit boards code breaking artificial creativity and so on now we will understand how genetic
• 01:00 - 01:30 algorithm works there are basically five phases in genetic algorithm first one is initialization second one is Fitness assignment third one is selection fourth one is cross over or reproduction fifth one is termination now we will start with the first step of genetic algorithm the first step in genetic algorithm is initial population the process of genetic algorithm begins
• 01:30 - 02:00 with selecting a set of individuals those are known as the population for example in this case you can see here I have selected four individuals that is A1 A2 A3 A4 they will form the initial population here so a one a two a three a four are the initial population in this case each of these particular initial populations that is A1 A2 A3 A4 are the solution to the given problem definition that is
• 02:00 - 02:30 individually A1 will solve the given problem A2 will solve individually the given problem similarly A3 and A4 will individually solve that particular problem now this particular individual Solutions are known as chromosomes in terms of genetic algorithm so chromosome is an individual entity of this particular population each of these particular chromosomes are characterized by something known as Gene
• 02:30 - 03:00 here that is Gene is the individual entity of chromosome over here and this Gene can contain a binary value that is 0 or 1 so once you combine these particular genes we will form something known as chromosome and this set of chromosomes is nothing but the population over here so this is how the initial population is defined so once you get this particular initial population next we need to assign the fitness value for each of those particular individual Solutions or
• 03:00 - 03:30 the chromosomes that is done with the help of something known as the fitness function so what is the use of Fitness function the fitness function determines how fit an individual is so what is the fitness value of each of these particular individual Solutions is identified with the help of Fitness function based on this particular Fitness score we will select one of these individuals for the next reproduction or
• 03:30 - 04:00 what is that called as crossover over here so the next step in genetic algorithm is the selection so the idea of selection phase is to select the fittest individual so there are four individuals are there we need to select the fittest individual for example A1 and A2 may be the fittest individual so we will select A1 and E2 for we can say that the reproduction or the crossover in The Next Step there may be a possibility that A1 and A3 may be the fittest Solutions or the individuals we will select A1 and A3 in that case so
• 04:00 - 04:30 what are the different steps to select this particular individuals is there are basically three methods are there the first method is something called as relative wheel selection second one is known as tournament selection third one is known as rank based reselection I will discuss each of these particular selection methods in detail in the next video the link for that video I will put in the description below the next step is the crossover so once you select the two fittest individuals
• 04:30 - 05:00 they will be used as an input to this particular crossover crossover is one of the significant phase in genetic algorithm in this case first we will identify something known as the crossover Point once you identify this particular crossover point we will apply something called as crossover on those particular the individual fittest the chromosomes or the individuals so in this case you can see here A1 and A2 are selected as the fittest individuals and
• 05:00 - 05:30 the crossover point in this case is 3 over here so what we do in this case is uh we will apply the something called as crossover and then we will generate the new Offspring by exchanging the genes of the parents until the crossover is reached that is uh from A1 and A2 we will exchange these particular genes until this particular crossover point is reached here so this 0 is extended with one this zero is exchange 0 with 1 this 0 is exchanger with 1 we will get the
• 05:30 - 06:00 new 2 what is that called as individuals that is A5 and A6 and these three zeros will become once over here and these ones will become zeros over here sometimes what we need to do is we need to apply something called as a mutation with a very low uh random probability where we will can say that the flip the genes of can say that The Offspring over here so there is one mutation technique is called as flipping uh in this case a Phi
• 06:00 - 06:30 is the individual before mutation this is how actually it looks like after mutation these three bits are flipped over here one is changed to zero zero is changed to one and this 0 is changed to one over here so this is one method of mutation over here there are multiple mutation methods are there as said earlier the flipping is the first one second one is gaussian mutation third one is exchange or swap mutation so I will discuss these methods again in the next video the link for that video I
• 06:30 - 07:00 will put in the description below the last step in genetic algorithm is something known as termination the algorithm terminates if the population has converged so what is the meaning of converged here is once you generate the new Offspring this Offspring is similar to the existing individual in the generation or you can say that this is not different with respect to the existing individuals if it is not different the meaning of this one is the
• 07:00 - 07:30 solutions are converged or the population is converged we need to stop over there the same thing can be shown with the help of flowchart something like this we will start with the initial population once you get the initial population we need to find the fitness score for each of those particular individuals in the population once you find the fitness score we will check the termination criteria is reached or not if the termination criteria is reached we will stop here otherwise we will go and select the two best individuals based on
• 07:30 - 08:00 the fitness score they are known as the parents and then we will apply crossover and mutation so that we will get the new offsprings here once you get the new offsprings we will calculate something called as the fitness score again once you calculate the fitness score we will check whether the criteria or the termination criteria is reached if it reached we will stop here otherwise we will go back and then we once again we will select the two parents with the help of Fitness core we will apply crossover and mutation so that we will
• 08:00 - 08:30 get the new Offspring once you get the new Offspring we will compute the fitness score for all those particular Solutions in the population again we will check whether the termination condition is met or not the same thing will be repeated unless and until the termination condition is reached over here so this is the basic idea of how the genetic algorithm works I hope the concept is clear if you like the video do like and share with your friends press the Subscribe button for more videos press the Bell icon for regular
• 08:30 - 09:00 updates thank you for watching