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An Introduction to Genetic Algorithms

Drawing its inspiration from Charles Darwin's theory of natural evolution, one of the most fascinating techniques for problem-solving is the algorithm family suitably named evolutionary computation. Within this family, the most prominent and widely used branch is known as genetic algorithms. This chapter is the beginning of your journey to mastering this extremely powerful, yet extremely simple, technique.

In this chapter, we will introduce genetic algorithms and their analogy to Darwinian evolution, and dive into their basic principles of operation as well as their underlying theory. We will then go over the differences between genetic algorithms and traditional ones and cover the advantages and limitations of genetic algorithms and their uses. We will conclude by reviewing the cases where the use of a genetic algorithm may prove beneficial.

In this introductory chapter, we will cover the following topics:

What are genetic algorithms?
The theory behind genetic algorithms
Differences between genetic algorithms and traditional algorithms
Advantages and limitations of genetic algorithms
When to use genetic algorithms

What are genetic algorithms?

Genetic algorithms are a family of search algorithms inspired by the principles of evolution in nature. By imitating the process of natural selection and reproduction, genetic algorithms can produce high-quality solutions for various problems involving search, optimization, and learning. At the same time, their analogy to natural evolution allows genetic algorithms to overcome some of the hurdles that are encountered by traditional search and optimization algorithms, especially for problems with a large number of parameters and complex mathematical representations.

In the rest of this section, we will review the basic ideas of genetic algorithms, as well as their analogy to the evolutionary processes transpiring in nature.

Darwinian evolution

Genetic algorithms implement a simplified version of the Darwinian evolution that takes place in nature. The principles of the Darwinian evolution theory can be summarized using the following principles:

The principle of variation: The traits (attributes) of individual specimens belonging to a population may vary. As a result, the specimens differ from each other to some degree; for example, in their behavior or appearance.
The principle of inheritance: Some traits are consistently passed on from specimens to their offspring. As a result, offspring resemble their parents more than they resemble unrelated specimens.
The principle of selection: Populations typically struggle for resources within their given environment. The specimens possessing traits that are better adapted to the environment will be more successful at surviving, and will also contribute more offspring to the next generation.

In other words, evolution maintains a population of individual specimens that vary from each other. Those who are better adapted to their environment have a greater chance of surviving, breeding, and passing their traits to the next generation. This way, as generations go by, species become more adapted to their environment and to the challenges presented to them.

An important enabler of evolution is crossover or recombination – where offspring are created with a mix of their parents' traits. Crossover helps in maintaining the diversity of the population and in bringing together the better traits over time. In addition, mutations – random variations in traits – can play a role in evolution by introducing changes that can result in a leap forward every once in a while.

The genetic algorithms analogy

Genetic algorithms seek to find the optimal solution for a given problem. Whereas Darwinian evolution maintains a population of individual specimens, genetic algorithms maintain a population of candidate solutions, called individuals, for that given problem. These candidate solutions are iteratively evaluated and used to create a new generation of solutions. Those who are better at solving this problem have a greater chance of being selected and passing their qualities to the next generation of candidate solutions. This way, as generations go by, candidate solutions get better at solving the problem at hand.

In the following sections, we will describe the various components of genetic algorithms that enable this analogy for Darwinian evolution.

Genotype

In nature, breeding, reproduction, and mutation are facilitated via the genotype – a collection of genes that are grouped into chromosomes. If two specimens breed to create offspring, each chromosome of the offspring will carry a mix of genes from both parents.

Mimicking this concept, in the case of genetic algorithms, each individual is represented by a chromosome representing a collection of genes. For example, a chromosome can be expressed as a binary string, where each bit represents a single gene:

Simple binary-coded chromosome

The preceding image shows an example of one such binary-coded chromosome, representing one particular individual.

Population

At any point in time, genetic algorithms maintain a population of individuals – a collection of candidate solutions for the problem at hand. Since each individual is represented by some chromosome, this population of individuals can be seen as a collection of such chromosomes:

A population of individuals represented by binary-coded chromosomes

The population continually represents the current generation and evolves over time when the current generation is replaced by a new one.

Fitness function

At each iteration of the algorithm, the individuals are evaluated using a fitness function (also called the target function). This is the function we seek to optimize or the problem we attempt to solve.

Individuals who achieve a better fitness score represent better solutions and are more likely to be chosen to reproduce and be represented in the next generation. Over time, the quality of the solutions improves, the fitness values increase, and the process can stop once a solution is found with a satisfactory fitness value.

Selection

After calculating the fitness of every individual in the population, a selection process is used to determine which of the individuals in the population will get to reproduce and create the offspring that will form the next generation.

This selection process is based on the fitness score of the individuals. Those with higher score values are more likely to be chosen and pass their genetic material to the next generation.

Individuals with low fitness values can still be chosen, but with lower probability. This way, their genetic material is not completely excluded.

Crossover

To create a pair of new individuals, two parents are usually chosen from the current generation, and parts of their chromosomes are interchanged (crossed over) to create two new chromosomes representing the offspring. This operation is called crossover, or recombination:

Crossover operation between two binary-coded chromosomes
Source: https://commons.wikimedia.org/wiki/File:Computational.science.Genetic.algorithm.Crossover.One.Point.svg.Image by Yearofthedragon. Licensed under Creative Commons CC BY-SA 3.0: https://creativecommons.org/licenses/by-sa/3.0/deed.en

The preceding image illustrates a simple crossover operation of creating two offspring from two parents.

Mutation

The purpose of the mutation operator is to periodically and randomly refresh the population, introduce new patterns into the chromosomes, and encourage search in uncharted areas of the solution space.

A mutation may manifest itself as a random change in a gene. Mutations are implemented as random changes to one or more of the chromosome values; for example, flipping a bit in a binary string:

Mutation operator applied to a binary-coded chromosome

The preceding image shows an example of the mutation operation.

Now, let's look at the theory behind genetic algorithms.

Key benefits

Explore the ins and outs of genetic algorithms with this fast-paced guide

Implement tasks such as feature selection, search optimization, and cluster analysis using Python

Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications

Description

Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.

What you will learn

Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications

Use genetic algorithms to optimize functions and solve planning and scheduling problems

Enhance the performance of machine learning models and optimize deep learning network architecture

Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym

Explore how images can be reconstructed using a set of semi-transparent shapes

Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization

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roudan Oct 11, 2020

Wow this is an awesome GA book which use DEAP to demonstrate how to solve easily various type of problems using GA. This is the best book ever in GA.It comes with complete codes which can used in my projects. I learnt a lot. Thank you!

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Arcueid Dec 08, 2020

仕事関係で購入して独学しています（QSAR分野でfeature selectionなど案外行けそうな感じがするので）。数学の内容を数式など使わずに平易な言語表現で語っており非常にやさしい本です。英語が問題なければ普通に一日1 chapter進められる。完読したら実務的にGAが使えると考えられます。追記：niching & sharingの解説と実装はこの本の肝要と思います。勉強になりました。必要な事前スキル・Pythonの基本作法・最低限の数学知識（高校生ないし学部教養レベル）・Machine learningの概念（ほんとうに概念だけ）

Mark on Amzon Mar 02, 2020

Starts with an overview of genetic algorithms (GA) then introduces the DEAP framework for evolutionary computation, which is used in the book for the various example problems/solutions. All of the code is explained and as you read about the examples and inspect the code, you can get ideas for possible applications of GA to real problems. Very gentle learning curve, but you should know some basic Python before starting this book.

A. Kashyap Mar 23, 2022

Easy to understand and extremely useful. Helped me out a great deal with a real work project.

Amazon Customer Jul 16, 2020

This was the best Genetic Algorithm book ever in my life. Due to its simplicity and pesudo-code-like nature of the Python language, the example codes does not interfere with the readers’ intellectual engagement into the beauty of evolutionary algorithms.

Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

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Hands-On Genetic Algorithms with Python

An Introduction to Genetic Algorithms

What are genetic algorithms?

Darwinian evolution

The genetic algorithms analogy

Genotype

Population

Fitness function

Selection

Crossover

Mutation

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