- author = "Victor Parada",
- title = "Automatic Generation Of Algorithms",
- publisher = "CRC Press",
- year = "2025",
- series = "Advances in Metaheuristics",
- address = "Boca Raton, Fl, USA",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "9781032894454",
-
URL = "
https://www.routledge.com/Automatic-Generation-Of-Algorithms/Parada/p/book/9781032894454",
-
URL = "https://books.google.co.uk/books?id=ke04EQAAQBAJ",
- size = "214 pages",
-
abstract = "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 NP hardness
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 re-imagine
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.
Contents:
Overview of Optimization
The Master Problem
Modeling Problems
AGA with Genetic Programming
AGA and Machine Learning
Producing Metaheuristics Automatically
AGA with Reinforcement Learning
Conclusions and Future Trends
Bibliography
Index",
