Evolutionary Computation in Dynamic and Uncertain Environment

By: Shengxiang Yang (Editor), Yew-Soon Ong (Editor), Yaochu Jin (Editor)

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Hardcover | 1 March 2007

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632 Pages

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23.5 x 15.88 x 3.81

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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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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Mathematics Applied Mathematics Probability & Statistics Engineering & Technology Technology in General Engineering in General Maths for Engineers

Optimum Tracking in Dynamic Environments
Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments	p. 3
Particle Swarm Optimization in Dynamic Environments	p. 29
Evolution Strategies in Dynamic Environments	p. 51
Orthogonal Dynamic Hill Climbing Algorithm: ODHC	p. 79
Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments	p. 105
Learning and Anticipation in Online Dynamic Optimization	p. 129
Evolutionary Online Data Mining: An Investigation in a Dynamic Environment	p. 153
Adaptive Business Intelligence: Three Case Studies Zbigniew Michalewicz	p. 179
Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks	p. 197
Approximation of Fitness Functions
Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization	p. 225
Evolutionary Shape Optimization Using Gaussian Processes	p. 251
A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer	p. 269
An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks	p. 297
Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design	p. 323
Handling Noisy Fitness Functions
Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation	p. 345
Evolving Multi Rover Systems in Dynamic and Noisy Environments	p. 371
A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions	p. 389
Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem	p. 417
Search for Robust Solutions
Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty	p. 437
Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms	p. 457
Evolutionary Robust Design of Analog Filters Using Genetic Programming	p. 479
Robust Salting Route Optimization Using Evolutionary Algorithms	p. 497
An Evolutionary Approach For Robust Layout Synthesis of MEMS	p. 519
A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs	p. 543
An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models	p. 565
Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm	p. 583
Index	p. 599
Table of Contents provided by Publisher. All Rights Reserved.

Evolutionary Computation in Dynamic and Uncertain Environment

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Hardcover

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