This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment.
There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
| ISBN: | 9788132221838 |
| Publication date: | 30th December 2014 |
| Author: | Rituparna Datta, Kalyanmoy Deb |
| Publisher: | Springer an imprint of Springer India |
| Format: | Hardback |
| Pagination: | 319 pages |
| Series: | Infosys Science Foundation Series |
| Genres: |
Artificial intelligence Optimization Mechanical engineering |
This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment.
Evolutionary Constrained Optimization. Infosys Science Foundation Series in Applied Sciences and Engineering features in the following genres: Artificial intelligence, Optimization, Mechanical engineering
Hardback. Not Available.
Evolutionary Constrained Optimization. Infosys Science Foundation Series in Applied Sciences and Engineering was written by Rituparna Datta, Kalyanmoy Deb and published by Springer an imprint of Springer India
Evolutionary Constrained Optimization. Infosys Science Foundation Series in Applied Sciences and Engineering has 319 pages
Yes it is part of Infosys Science Foundation Series series