Multi-Objective Optimization Using Evolutionary Algorithms

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Evolutionary algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. <ol>
<li>Comrephensive coverage of this growing area of research.</li> <li>Carefully introduces each algorithm with examples and in-depth discussion.</li> <li>Includes many applications to real-world problems, including engineering design and scheduling.</li> <li>Includes discussion of advanced topics and future research.</li> <li>Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms</li> </ol>
Provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches. This integrated presentation of theory, algorithms and examples will benefit those working in the areas of optimization, optimal design and evolutionary computing.

Science & Math

Deb, Kalyanmoy

Evolution

wiley

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. 
<ul>
<li>Comprehensive coverage of this growing area of research</li> <li>Carefully introduces each algorithm with examples and in-depth discussion</li> <li>Includes many applications to real-world problems, including engineering design and scheduling</li> <li>Includes discussion of advanced topics and future research</li> <li>Can be used as a course text or for self-study</li> <li>Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms</li> </ul>
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

Computers & Technology

Kalyanmoy, Deb

Programming

Optimization for Engineering Design: Algorithms and Examples

Mathematics

Prentice-Hall of India Pvt.Ltd

Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)

Saxena, Dhish Kumar

Mittal, Sukrit

Goodman, Erik D.

Computer Science

Springer

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