Hands-On Genetic Algorithms with Python (Paperback)
暫譯: 使用 Python 實作遺傳演算法

Eyal Wirsansky

Hands-On Genetic Algorithms with Python (Paperback)

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商品描述

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.

 

  • 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
  • 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

商品描述(中文翻譯)

遺傳演算法是一系列受自然進化原則啟發的搜尋、優化和學習演算法。透過模仿進化過程，遺傳演算法能克服傳統搜尋演算法所遇到的障礙，並為各種問題提供高品質的解決方案。本書將幫助您掌握一種強大而簡單的方法，使用 Python 將遺傳演算法應用於廣泛的任務，涵蓋人工智慧的最新發展。

在介紹遺傳演算法及其運作原則後，您將了解它們與傳統演算法的不同之處，以及它們能解決哪些類型的問題。接著，您將發現它們如何應用於搜尋和優化問題，例如規劃、排程、遊戲和分析。隨著學習的深入，您還將學習如何使用遺傳演算法來改善機器學習和深度學習模型，解決強化學習任務，並執行影像重建。最後，您將探討幾種相關技術，這些技術可以為未來的應用開啟新的可能性。

在本書結束時，您將擁有在人工智慧及其他多個領域應用遺傳演算法的實作經驗。

- 了解如何使用最先進的 Python 工具來創建基於遺傳演算法的應用程式
- 使用遺傳演算法來優化函數並解決規劃和排程問題
- 提升機器學習模型的性能並優化深度學習網路架構
- 使用 OpenAI Gym 將遺傳演算法應用於強化學習任務
- 探索如何使用一組半透明形狀重建影像
- 發現其他生物啟發技術，例如遺傳程式設計和粒子群優化
- 透過這本快速入門指南深入了解遺傳演算法的各個方面
- 使用 Python 實作特徵選擇、搜尋優化和聚類分析等任務
- 解決組合問題、優化函數並提升人工智慧應用的性能

作者簡介

Eyal Wirsansky is a senior software engineer, a technology community leader, and an artificial intelligence enthusiast and researcher. Eyal started his software engineering career as a pioneer in the field of voice over IP, and he now has over 20 years' experience of creating a variety of high-performing enterprise solutions. While in graduate school, he focused his research on genetic algorithms and neural networks. One outcome of his research is a novel supervised machine learning algorithm that combines the two.

Eyal leads the Jacksonville (FL) Java user group, hosts the Artificial Intelligence for Enterprise virtual user group, and writes the developer-oriented artificial intelligence blog, ai4java.

作者簡介(中文翻譯)

Eyal Wirsansky 是一位資深軟體工程師、科技社群領袖，以及人工智慧的愛好者和研究者。Eyal 的軟體工程職業生涯始於語音傳輸協定（Voice over IP）領域的先驅，至今擁有超過 20 年的經驗，創造各種高效能的企業解決方案。在研究生院期間，他專注於遺傳演算法（genetic algorithms）和神經網路（neural networks）的研究。他研究的一個成果是一種新穎的監督式機器學習演算法，結合了這兩者。

Eyal 領導著佛羅里達州傑克遜維爾的 Java 使用者群組，主持人工智慧企業虛擬使用者群組，並撰寫面向開發者的人工智慧部落格 ai4java。

目錄大綱

  1. An Introduction to Genetic Algorithms
  2. Understanding the Key Components of Genetic Algorithms
  3. Using the DEAP Framework
  4. Combinatorial Optimization
  5. Constraint Satisfaction
  6. Optimizing Continuous Functions
  7. Enhancing Machine Learning Models Using Feature Selection
  8. Hyperparameter Tuning Machine Learning Models
  9. Architecture Optimization of Deep Learning Networks
  10. Reinforcement Learning with Genetic Algorithms
  11. Genetic Image Reconstruction
  12. Other Evolutionary and Bio-Inspired Computation Techniques

目錄大綱(中文翻譯)


  1. An Introduction to Genetic Algorithms

  2. Understanding the Key Components of Genetic Algorithms

  3. Using the DEAP Framework

  4. Combinatorial Optimization

  5. Constraint Satisfaction

  6. Optimizing Continuous Functions

  7. Enhancing Machine Learning Models Using Feature Selection

  8. Hyperparameter Tuning Machine Learning Models

  9. Architecture Optimization of Deep Learning Networks

  10. Reinforcement Learning with Genetic Algorithms

  11. Genetic Image Reconstruction

  12. Other Evolutionary and Bio-Inspired Computation Techniques

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