Hands-On Genetic Algorithms with Python (Paperback)

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演化原理啟發的搜尋、優化和學習演算法。通過模仿演化過程，遺傳演算法能夠克服傳統搜尋演算法遇到的障礙，並為各種問題提供高質量的解決方案。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各種任務，並涵蓋人工智能領域的最新發展。

在介紹遺傳演算法及其運作原理後，您將了解它們與傳統演算法的區別以及它們可以解決的問題類型。然後，您將發現如何將它們應用於搜索和優化問題，例如計劃、排程、遊戲和分析。隨著您的進一步學習，您還將學習如何使用遺傳演算法改進機器學習和深度學習模型，解決強化學習任務，以及進行圖像重建。最後，您將涵蓋幾個相關技術，這些技術可以為未來的應用開啟新的可能性。

通過閱讀本書，您將親身體驗在人工智能以及其他領域中應用遺傳演算法的實踐經驗。

本書的內容包括：

- 使用最先進的Python工具創建基於遺傳演算法的應用程式
- 使用遺傳演算法優化函數並解決計劃和排程問題
- 提升機器學習模型的性能並優化深度學習網絡架構
- 使用OpenAI Gym將遺傳演算法應用於強化學習任務
- 探索使用一組半透明形狀重建圖像的方法
- 發現其他生物啟發技術，如遺傳編程和粒子群優化
- 通過這本節奏快的指南深入了解遺傳演算法的方方面面
- 使用Python實現功能選擇、搜索優化和集群分析等任務
- 解決組合問題，優化函數，提升人工智能應用的性能

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，也是一位人工智慧愛好者和研究者。Eyal 在聲音傳輸IP領域開創了他的軟體工程師生涯，現在已經擁有超過20年的經驗，創造了各種高效的企業解決方案。在研究生期間，他專注於遺傳演算法和神經網路的研究。他的研究成果之一是一種結合這兩者的新型監督式機器學習演算法。

Eyal 領導著佛羅里達州傑克遜維爾的Java使用者群組，主持著面向企業的人工智慧虛擬使用者群組，並撰寫開發者導向的人工智慧部落格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)

- 遺傳演算法入門
- 理解遺傳演算法的關鍵組件
- 使用DEAP框架
- 組合優化
- 約束滿足
- 優化連續函數
- 使用特徵選擇增強機器學習模型
- 超參數調整機器學習模型
- 深度學習網絡架構優化
- 遺傳演算法強化學習
- 遺傳圖像重建
- 其他進化和生物啟發計算技術

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