Bio-Inspired AI in C++
Build Bio-Inspired Optimization Solvers for Real-World Problems in C++By Zoe Becker
Length7h 10m
About this audiobook
bio-inspired AI C++ and swarm intelligence programming. Discover how to build nature-driven algorithms by hand in modern C++. Bio-Inspired AI in C++ covers ant colonies, neural networks, and swarm methods for speed. AI programming and C++ machine learning are core to this hands-on guide.
From beginner to pro, you'll implement bio-inspired AI step by step. Learn particle swarm optimization, genetic algorithms, and artificial neural networks. Nature-inspired computing and swarm intelligence come alive with practical C++ code.
This book focuses on performance and clarity. You'll master ant colony optimization for routing, neural networks for pattern recognition, and swarm methods for optimization. AI algorithms and C++ for AI are explained with real-world examples.
Build your own AI systems from scratch. No black boxes—just raw C++ and nature's wisdom. Machine learning C++ and evolutionary algorithms are demystified.
Compare with other AI books: Machine Learning System Design Interview by [placeholder] and AI for Beginners by [placeholder] focus on theory or interviews, while this book gives you working code. Bio-inspired algorithms and C++ AI programming set it apart.
Zoe Becker's approach is practical and fast. You'll write algorithms that scale. Hands-on AI and C++ algorithms for nature-driven methods are your path to mastery.
This hands-on AI/ML/Data guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
Audiobook details
GenreTechnology
Length7 hrs 10 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Preface
2Chapter 1 — Why Bio-Mimetic Algorithms Matter in Modern C++
3Chapter 2 — The Optimization Landscape: Problems, Objectives, and Search Spaces
4Chapter 3 — Metaheuristics: Exploration, Exploitation, and the No-Free-Lunch Theorem
5Chapter 4 — Genetic Algorithms: The Mechanics of Darwinian Evolution
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6Chapter 5 — Building a Genetic Algorithm Solver for the TSP in C++
7Chapter 6 — Simulated Annealing: From Hot Metal to Optimal Solutions
8Chapter 7 — Implementing Simulated Annealing for the TSP in C++
9Chapter 8 — Differential Evolution: Vector Differences as a Search Engine
10Chapter 9 — Coding a Differential Evolution Solver for the TSP
11Chapter 10 — Ant Colony Optimization: Pheromone Trails and Collective Intelligence
12Chapter 11 — Building an Ant Colony Optimizer for the TSP in C++
13Chapter 12 — Scaling Up: Benchmarking on a 50-City TSP Instance
14Chapter 13 — Hybridization: Combining Algorithms for Better Results
15Chapter 14 — Parameter Tuning and Automated Configuration
16Chapter 15 — Beyond the TSP: Applying LibBioOpt to Real-World Problems
17Chapter 16 — Performance Optimization: Profiling and Parallelizing Your Solvers
18Chapter 17 — Testing, Debugging, and Validation Strategies
19Chapter 18 — Final Challenge: Solving a 100-City TSP and Reflecting on the Journey
20About the Author
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