Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions

Frances Buontempo



Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.

Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.

In this book, you will:

  • Use heuristics and design fitness functions.
  • Build genetic algorithms.
  • Make nature-inspired swarms with ants, bees and particles.
  • Create Monte Carlo simulations.
  • Investigate cellular automata.
  • Find minima and maxima, using hill climbing and simulated annealing.
  • Try selection methods, including tournament and roulette wheels.
  • Learn about heuristics, fitness functions, metrics, and clusters.

Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon.

What You Need:

Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.





- 使用啟發式方法和設計適應函數。
- 建立遺傳演算法。
- 創建受自然啟發的群體,包括螞蟻、蜜蜂和粒子。
- 創建蒙地卡羅模擬。
- 探索細胞自動機。
- 使用爬山法和模擬退火法尋找極小值和極大值。
- 嘗試選擇方法,包括錦標賽和輪盤賽。
- 了解啟發式方法、適應函數、度量和集群。



使用C++(>= C++11)、Python(2.x或3.x)和JavaScript(使用HTML5 canvas)進行編碼。還使用了matplotlib和一些開源庫,包括SFML、Catch和Cosmic-Ray。這些繪圖和測試庫不是必需的,但使用它們將使你有更豐富的體驗。只需一個文本編輯器和你選擇的語言的編譯器/解釋器,你仍然可以根據一般演算法描述進行編碼。