Open AccessBook
Evolutionary Optimization in Dynamic Environments
Jürgen Branke
- 31 Dec 2001
891
TL;DR: This book presents a brief introduction to Evolutionary Algorithms, a methodology for enabling Continuous Adaptation in Dynamic Environments and its applications, and some of the principles behind it, as well as some of its critics.
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Abstract: Preface. 1. Brief Introduction to Evolutionary Algorithms. Part I: Enabling Continuous Adaptation. 2. Optimization in Dynamic Environments. 3. Survey: State of the Art. 4. From Memory to Self-Organization. 5. Empirical Evaluation. 6. Summary of Part I. Part II: Considering Adaptation Cost. 7. Adaptation Cost vs. Solution Quality. Part III: Robustness and Flexibility - Precaution against Changes. 8. Searching for Robust Solutions. 9. From Robustness to Flexibility. 10. Summary and Outlook. References. Index.
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Citations
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: 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.
Handling multiple objectives with particle swarm optimization
TL;DR: An approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions and indicates that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
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•Book
Metaheuristics: From Design to Implementation
El-Ghazali Talbi
- 22 Jun 2009
TL;DR: This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
Evolutionary optimization in uncertain environments-a survey
Yaochu Jin,Jürgen Branke +1 more
TL;DR: This paper attempts to provide a comprehensive overview of the related work within a unified framework on addressing different uncertainties in evolutionary computation, which has been scattered in a variety of research areas.
Robust Optimization - A Comprehensive Survey
TL;DR: The main focus will be on the different approaches to perform robust optimization in practice including the methods of mathematical programming, deterministic nonlinear optimization, and direct search methods such as stochastic approximation and evolutionary computation.
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References
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: 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.
•Book
Handbook of Genetic Algorithms
Lawrence Davis
- 01 Jan 1991
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
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•Proceedings Article
Reducing bias and inefficiency in the selection algorithm
James E. Baker
- 01 Oct 1987
TL;DR: A sheet which is a blend of water-insoluble fibers and pieces of film of a dry material which converts to a gel quickly on contact with a large amount of water.
1.7K