Evolutionary Computation in Dynamic and Uncertain Environments

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Shengxiang Yang, Yew-Soon Ong, Yaochu Jin
Springer Science & Business Media, Mar 7, 2007 - Mathematics - 605 pages
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Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti?cial intelligence communities. As a classofstochasticoptimizationtechniques,EAscanoftenoutperformclassical optimization techniques for di?cult real world problems. Due to the ease of use and robustness, EAs have been applied to a wide variety of optimization problems. Most of these optimization problems ta- led are stationary and deterministic. However, many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For example, the ?tness function is uncertain or noisy as a result of simulation errors, measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the EA is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic en- ronments, or to ?nd a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to classical optimization te- niques and conventional EAs as well. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in - namic and uncertain environments.
 

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Contents

Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments
3
Particle Swarm Optimization in Dynamic Environments
29
Evolution Strategies in Dynamic Environments
50
Orthogonal Dynamic Hill Climbing Algorithm ODHC
79
Genetic Algorithms with SelfOrganizing Behaviour in Dynamic Environments
105
Learning and Anticipation in Online Dynamic Optimization
128
Evolutionary Online Data Mining An Investigation in a Dynamic Environment
153
Adaptive Business Intelligence Three Case Studies
179
Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation
345
Evolving Multi Rover Systems in Dynamic and Noisy Environments
370
A Memetic Algorithm Using a TrustRegion DerivativeFree Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Blackbo...
389
Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem
416
Search for Robust Solutions
435
SingleMultiobjective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty
437
Evolving the Tradeoffs between ParetoOptimality and Robustness in MultiObjective Evolutionary Algorithms
457
Evolutionary Robust Design of Analog Filters Using Genetic Programming
479

Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks
197
Approximation of Fitness Functions
223
Individualbased Management of Metamodels for Evolutionary Optimization with Application to ThreeDimensional Blade Optimization
225
Evolutionary Shape Optimization Using Gaussian Processes
251
A Study of Techniques to Improve the efficiency of a MultiObjective Particle Swarm Optimizer
268
An Evolutionary Multiobjective Adaptive Metamodeling Procedure Using Artificial Neural Networks
297
Surrogate ModelBased Optimization Framework A Case Study in Aerospace Design
323
Handling Noisy Fitness Functions
343
Robust Salting Route Optimization Using Evolutionary Algorithms
497
An Evolutionary Approach For Robust Layout Synthesis of MEMS
518
A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs
543
An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to MultiObjective Models
565
Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm
583
Index
599
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