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Total Size:
3.7 MB
Info Hash:
D3C812F2CB4E3E7F6E66A1056857DAA826010C11
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Added:
April 22, 2026, 12:19 p.m.
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(Last updated: April 22, 2026, 12:23 p.m.)
| File | Size |
|---|---|
| Parada V. Automatic Generation Of Algorithms 2025.pdf | 3.7 MB |
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262.9 MB
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2024-09-02
| Uploaded by IGGGAMESCOM | Size 262.9 MB | Health [ 0 /4 ] | Added 2024-09-02 |
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637.1 MB
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2025-03-09
| Uploaded by CorsaroNero | Size 637.1 MB | Health [ 0 /3 ] | Added 2025-03-09 |
NOTE
SOURCE: Parada V. Automatic Generation Of Algorithms 2025
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COVER
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MEDIAINFO
Textbook in PDF format In the rapidly evolving domain of computational problem-solving, this book delves into the cutting-edge Automatic Generation of Algorithms (AGA) paradigm, a groundbreaking approach poised to redefine algorithm design for optimization problems. Spanning combinatorial optimization, machine learning, genetic programming, and beyond, it investigates AGA's transformative capabilities across diverse application areas. The book initiates by introducing fundamental combinatorial optimization concepts and NPhardness significance, laying the foundation for understanding AGA's necessity and potential. It then scrutinizes the pivotal Master Problem concept in AGA and the art of modeling for algorithm generation. The exploration progresses with integrating genetic programming and synergizing AGA with evolutionary computing. Subsequent chapters delve into the AGA-Machine Learning intersection, highlighting their shared optimization foundation while contrasting divergent objectives. The automatic generation of metaheuristics is examined, aiming to develop versatile algorithmic frameworks adaptable to various optimization problems. Furthermore, the book explores applying Reinforcement Learning techniques to automatic algorithm generation. Throughout, it invites readers to reimagine algorithmic design boundaries, offering insights into AGA's conceptual underpinnings, practical applications, and future directions, serving as an invitation for researchers, practitioners, and enthusiasts in Computer Science, operations research, Artificial Intelligence, and beyond to embark on a journey toward computational excellence where algorithms are born, evolved, and adapted to meet ever-changing real-world problem landscapes. AGA is deeply rooted in the foundational principles of genetic programming. However, at its core lies a master optimization problem that extends far beyond this initial scope. By framing AGA as an overarching optimization challenge, we uncover many possibilities for creating new algorithms. This master problem involves minimizing the error between a newly generated algorithm and the optimal solution for a combinatorial optimization problem. This emerging field results from many existing scientific contributions from different views and various authors. As we solve this optimization problem, we produce a new algorithm and advance the frontier of algorithmic design, unlocking innovative approaches to tackle complex computational challenges
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