Evolutionary computation in dynamic and uncertain environments
Shengxiang Yang,Yew-Soon Ong,Yaochu Jin +2 more
- 01 Mar 2007
TL;DR: This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified model for evolutionary algorithms.
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Abstract: This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified ...
read more
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Figures
Figure 2: Pseudocode for the memory-enhanced GA (MEGA) and the GA with memory and random immigrants schemes (MRIGA). 
Figure 15: Experimental results of RIGA, MRIGA, EIGA, and MIGAs with different immigrants ratio on cyclic DOPs with noise with τ = 50 and ρ = 0.1 and 0.5. 
Figure 8: Experimental results of GAs in cyclic dynamic environments with noise. 
Figure 7: Experimental results of GAs in cyclic dynamic environments. 
Figure 11: Dynamic behavior of GAs on random DOPs with τ = 50 and ρ = 0.2: (a) OneMax, (b) Plateau, and (c) Knapsack. 
Figure 3: Pseudocode for the memory/search GA (MSGA).
Citations
A survey on optimization metaheuristics
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Evolutionary dynamic optimization: A survey of the state of the art
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Memetic algorithms and memetic computing optimization: A literature review
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References
Genetic algorithms in search, optimization and machine learning
David E. Goldberg
- 01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Shummet Baluja
- 01 Jun 1994
TL;DR: This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better.
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Evolutionary Optimization in Dynamic Environments
Jürgen Branke
- 31 Dec 2001
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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Memory enhanced evolutionary algorithms for changing optimization problems
Jürgen Branke
- 06 Jul 1999
TL;DR: A new way to explore the benefits of a memory while minimizing its negative side effects is derived from a number of approaches that extend the evolutionary algorithm with implicit or explicit memory.