Hands-On Genetic Algorithms with Python : Apply Genetic Algorithms to Solve Real-World AI and Machine Learning Problems / Eyal Wirsansky.

Format:

Book

Author/Creator:

Wirsansky, Eyal, author.

Language:

English

Subjects (All):

Genetic algorithms.

Genetic programming (Computer science).

Python (Computer program language).

Physical Description:

1 online resource (419 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing, 2024.

System Details:

Mode of access: World Wide Web.

Biography/History:

Wirsansky Eyal: Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.

Summary:

Written by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.

Contents:

Cover

Title page

Copyright and credits

Contributors

Table of Contents

Preface

Part 1: The Basics of Genetic Algorithms

Chapter 1: An Introduction to Genetic Algorithms

What are genetic algorithms?

Darwinian evolution

The genetic algorithms analogy

The theory behind genetic algorithms

The schema theorem

Differences from traditional algorithms

Population-based

Genetic representation

Fitness function

Probabilistic behavior

Advantages of genetic algorithms

Global optimization

Handling complex problems

Handling a lack of mathematical representation

Resilience to noise

Parallelism

Continuous learning

Limitations of genetic algorithms

Special definitions

Hyperparameter tuning

Computationally intensive

Premature convergence

No guaranteed solution

Use cases for genetic algorithms

Summary

Further reading

Chapter 2: Understanding the Key Components of Genetic Algorithms

The basic flow of a genetic algorithm

Creating the initial population

Calculating the fitness

Applying selection, crossover, and mutation

Checking the stopping conditions

Selection methods

Roulette wheel selection

Stochastic universal sampling

Rank-based selection

Fitness scaling

Tournament selection

Crossover methods

Single-point crossover

Two-point and k-point crossover

Uniform crossover

Crossover for ordered lists

Mutation methods

Flip-bit mutation

Swap mutation

Inversion mutation

Scramble mutation

Real-coded genetic algorithms

Blend crossover

Simulated binary crossover

Real mutation

Understanding elitism

Niching and sharing

Serial niching versus parallel niching

The art of solving problems using genetic algorithms

Part 2: Solving Problems with Genetic Algorithms.

Chapter 3: Using the DEAP Framework

Technical requirements

Python version

Using a virtual environment

Installing the necessary libraries

Introduction to DEAP

Using the creator module

Creating the Fitness class

Creating the Individual class

Using the Toolbox class

Creating genetic operators

Creating the population

The OneMax problem

Solving the OneMax problem with DEAP

Choosing the chromosome

Choosing the genetic operators

Setting the stopping condition

Implementing with DEAP

Using built-in algorithms

The Statistics object

The algorithm

The logbook

Running the program

Adding the hall of fame feature

Experimenting with the algorithm's settings

Population size and number of generations

Crossover operator

Mutation operator

Selection operator

Chapter 4: Combinatorial Optimization

Search problems and combinatorial optimization

Solving the knapsack problem

The Rosetta Code knapsack 0-1 problem

Solution representation

Python problem representation

Genetic algorithm solution

Solving the TSP

TSPLIB benchmark files

Improving the results with enhanced exploration and elitism

Solving the VRP

Chapter 5: Constraint Satisfaction

Constraint satisfaction in search problems

Solving the N-Queens problem

Genetic algorithms solution

Solving the nurse scheduling problem

Hard constraints versus soft constraints.

Python problem representation

Solving the graph coloring problem

Using hard and soft constraints for the graph coloring problem

Chapter 6: Optimizing Continuous Functions

Chromosomes and genetic operators for real numbers

Using DEAP with continuous functions

Optimizing the Eggholder function

Optimizing the Eggholder function with genetic algorithms

Improving the speed with an increased mutation rate

Optimizing Himmelblau's function

Optimizing Himmelblau's function with genetic algorithms

Using niching and sharing to find multiple solutions

Simionescu's function and constrained optimization

Constrained optimization with genetic algorithms

Optimizing Simionescu's function using genetic algorithms

Using constraints to find multiple solutions

Part 3: Artificial Intelligence Applications of Genetic Algorithms

Chapter 7: Enhancing Machine Learning Models Using Feature Selection

Supervised machine learning

Classification

Regression

Supervised learning algorithms

Feature selection in supervised learning

Selecting the features for the Friedman-1 regression problem

Selecting the features for classifying the Zoo dataset

Chapter 8: Hyperparameter Tuning of Machine Learning Models

Hyperparameters in machine learning

The Wine dataset

The adaptive boosting classifier.

Tuning the hyperparameters using conventional versus genetic grid search

Testing the classifier's default performance

Running the conventional grid search

Running the genetic-algorithm-driven grid search

Tuning the hyperparameters using a direct genetic approach

Hyperparameter representation

Evaluating the classifier accuracy

Tuning the hyperparameters using genetic algorithms

Dedicated libraries

Chapter 9: Architecture Optimization of Deep Learning Networks

ANNs and DL

MLP

DL and convolutional NNs

Optimizing the architecture of a DL classifier

The Iris flower dataset

Representing the hidden layer configuration

Evaluating the classifier's accuracy

Optimizing the MLP architecture using genetic algorithms

Combining architecture optimization with hyperparameter tuning

Optimizing the MLP's combined configuration using genetic algorithms

Chapter 10: Reinforcement Learning with Genetic Algorithms

Reinforcement learning

Genetic algorithms and reinforcement learning

Gymnasium

The env interface

Solving the MountainCar environment

Evaluating the solution

The Python problem representation

Solving the CartPole environment

Controlling the CartPole with a neural network

Solution representation and evaluation

A genetic algorithm solution

Chapter 11: Natural Language Processing

Understanding NLP

Word embeddings

Word embeddings and genetic algorithms

Finding the mystery word using genetic algorithms

Python implementation.

Document classification

N-grams

Selecting a subset of n-grams

Using genetic algorithms to search for a fixed-size subset

Python implementation

Chapter 12: Explainable AI, Causality, and Counterfactuals with Genetic Algorithms

Unlocking the black box - XAI

Unraveling cause and effect - causality in AI

What-if scenarios - counterfactuals

Genetic algorithms in counterfactual analysis - navigating alternative scenarios

The German Credit Risk dataset

Exploring counterfactual scenarios for credit risk prediction

The Applicant class

The CreditRiskData class

Counterfactuals with genetic algorithms

The genetic algorithm solution

More "what-if" scenarios

Extending to other datasets

Part 4: Enhancing Performance with Concurrency and Cloud Strategies

Chapter 13: Accelerating Genetic Algorithms - the Power of Concurrency

Long runtimes in real-world genetic algorithms

Parallelizing genetic algorithms

Multithreading

Multiprocessing

Back to the OneMax problem

A baseline benchmark program

Simulating computational intensity

Multiprocessing using the Pool class

Increasing the number of processes

Multiprocessing using the SCOOP library

Distributed computing with SCOOP

Chapter 14: Beyond Local Resources - Scaling Genetic Algorithms in the Cloud

The next level in genetic algorithm performance -embracing a client-server architecture

Implementing a client-server model

Using a separate environment

Revisiting the One-Max problem, yet again

Creating the server component

Creating the client component

Running the asynchronous client

Using a production-grade app server.

Using the Gunicorn server.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781805121572

180512157X

OCLC:

1449569980