Nature-Inspired Machine Learning in C++
Build Hybrid Optimizers for Real Machine Learning PipelinesBy Marisol Halloran
Length7h 33m
About this audiobook
Machine learning C++ implementation evolutionary algorithms – build nature-inspired ML systems from scratch. This hands-on guide teaches you to code genetic algorithms, genetic programming, and swarm optimization in fast, transparent C++. No black boxes, no Python dependencies – just raw performance and full control.
Start with the basics of evolutionary computation and swarm intelligence, then progress to advanced techniques like tournament selection, crossover, mutation, and particle swarm optimization. Each chapter includes complete C++ code, benchmarks, and real-world examples. You'll learn to optimize neural networks, solve complex engineering problems, and create self-adapting algorithms.
What sets this book apart: transparency – every line of code is explained, and you can modify it freely. Speed – C++ delivers performance that Python can't match. Depth – from simple hill climbing to multi-objective optimization and neuroevolution.
Topics covered: genetic algorithm design, genetic programming trees, ant colony optimization, bee colony algorithms, differential evolution, and hybrid methods. Includes debugging tips, performance profiling, and integration with existing C++ projects.
Whether you're a data scientist, AI engineer, or hobbyist programmer, this book bridges theory and practice. By the end, you'll have a personal library of nature-inspired algorithms ready for any challenge. Compare with [placeholder] and [placeholder] for a truly hands-on C++ approach.
Audiobook details
GenreTechnology
Length7 hrs 33 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Preface
2Chapter 1 — Setting Up the Project and Defining the Target Problem
3Chapter 2 — Foundations of Optimization and Random Search
4Chapter 3 — Genetic Algorithms: Evolution in Code
5Chapter 4 — Enhancing Genetic Algorithms for Continuous Parameters
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6Chapter 5 — Handling Constraints and Multi-Objective Objectives in GA
7Chapter 6 — Simulated Annealing Basics: Cooling into Optimality
8Chapter 7 — Tuning Simulated Annealing Parameters and Reheating
9Chapter 8 — Ant Colony Optimization for Continuous Domains
10Chapter 9 — Advanced ACO with Local Search
11Chapter 10 — Differential Evolution: Precise Evolution for Continuous Spaces
12Chapter 11 — Differential Evolution Variants and Adaptive Parameters
13Chapter 12 — Performance Metrics and Statistical Benchmarking
14Chapter 13 — Hybrid Optimization: Combining GA and SA
15Chapter 14 — Hybridizing DE with ACO
16Chapter 15 — Ensemble of Optimizers and Meta-Optimization
17Chapter 16 — Integrating the Hybrid Optimizer with Real ML Pipelines
18Chapter 17 — Case Study: Evolving a Neural Network from Scratch
19Chapter 18 — Deployment, Parallelization, and Best Practices
20About the Author
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