Applied Evolutionary Algorithms for Engineers with Python is written for students, scientists and engineers who need to apply evolutionary algorithms to practical optimization problems. The presentation of the theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems. Cases of successful application of evolutionary algorithms to real-world like optimization problems are presented, together with source code that allows the reader to gain insight into the idiosyncrasies of the practical application of evolutionary algorithms.
- Includes detailed descriptions of evolutionary algorithm paradigms
- Provides didactic implementations of the algorithms in Python, a programming language that has been widely adopted by the AI community
- Discusses the application of evolutionary algorithms to real-world optimization problems
- Presents successful cases of the application of evolutionary algorithms to complex optimization problems, with auxiliary source code.
TABLE OF CONTENTS
part I|68 pages
part II|66 pages
Single-Objective Evolutionary Algorithms
part III|24 pages
Multi-Objective Evolutionary Algorithms
part IV|74 pages
Applying Evolutionary Algorithms