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Applied Evolutionary Algorithms for Engineers Using Python

Leonardo Azevedo Scardua
€73.99
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A01=Leonardo Azevedo Scardua
algorithm performance assessment
Applied evolutionary algorithms
Approximate Pareto Front
Artifical Intelligence community
Author_Leonardo Azevedo Scardua
Candidate Solution
Category=PH
Category=UMB
Category=UMX
computational intelligence
Continuous Genetic Algorithm
control systems design
Convergence Curves
Covariance Matrix Adaptation Evolution Strategies
DE Algorithm
engineering optimization
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evolutionary algorithm applications in engineering
Evolutionary Algorithms
Fitness Function
Genetic Algorithm
High-dimensional convolutional neural networks
Import Numpy
machine learning methods
Monte Carlo Runs
Multi-objective Optimization Problem
Noisy Objective Functions
Non-dominated Solutions
Optimization Problem
Pareto Front
Pareto Optimal Front
PSO Algorithm
Python Code
Python Implementation
robotics motion planning
Scalarized Cost Function
Search Space
Sphere Function
True Pareto Front
ZDT1 Function

Product details

  • ISBN 9780367711368
  • Weight: 400g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Jun 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

This book meant for those who seek to apply evolutionary algorithms to problems in engineering and science. To this end, it provides the theoretical background necessary to the understanding of the presented evolutionary algorithms and their shortcomings, while also discussing themes that are pivotal to the successful application of evolutionary algorithms to real-world problems. The theoretical descriptions are illustrated with didactical Python implementations of the algorithms, which not only allow readers to consolidate their understanding, but also provide a sound starting point for those intending to apply evolutionary algorithms to optimization problems in their working fields. Python has been chosen due to its widespread adoption in the Artificial Intelligence community. Those familiar with high level languages such as MATLAB™ will not have any difficulty in reading the Python implementations of the evolutionary algorithms provided in the book.

Instead of attempting to encompass most of the existing evolutionary algorithms, past and present, the book focuses on those algorithms that researchers have recently applied to difficult optimization problems, such as control problems with continuous action spaces and the training of high-dimensional convolutional neural-networks. The basic characteristics of real-world optimization problems are presented, together with recommendations on its proper application to evolutionary algorithms. The applied nature of the book is reinforced by the presentation of successful cases of the application of evolutionary algorithms to optimization problems. This is complemented by Python source codes, giving users an insight into the idiosyncrasies of the practical application of evolutionary algorithms.
About Leonardo Azevedo Scardua

Leonardo Azevedo Scardua received the D.Sc. degree in electrical engineering from the University of São Paulo, Brazil, in 2015. He has extensive engineering experience with mission-critical applications in the railway industry, having applied artificial intelligence and optimization algorithms in the development of software systems that control train traffic in many railways. He is now with the Control Engineering Department at the Federal Institute of Technology of Espírito Santo, Brazil.

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