Computational intelligence in optimization : applications and implementations
Author(s)
Bibliographic Information
Computational intelligence in optimization : applications and implementations
（Adaptation, learning, and optimization / series editors in chief, Meng-Hiot Lin, Yew-Soon Ong, v. 7）
Springer, c2010
- : hardcover
Available at 2 libraries
Note
Includes bibliographical references
Description and Table of Contents
Description
Optimization is an integral part to science and engineering. Most real-world applications involve complex optimization processes, which are di?cult to solve without advanced computational tools. With the increasing challenges of ful?lling optimization goals of current applications there is a strong drive to advancethe developmentofe?cientoptimizers. The challengesintroduced by emerging problems include: * objective functions which are prohibitively expensive to evaluate, so ty- callysoonlyasmallnumber ofobjectivefunctionevaluationscanbemade during the entire search, * objective functions which are highly multimodal or discontinuous, and * non-stationary problems which may change in time (dynamic). Classical optimizers may perform poorly or even may fail to produce any improvement over the starting vector in the face of such challenges. This has motivated researchers to explore the use computational intelligence (CI) to augment classical methods in tackling such challenging problems. Such methods include population-based search methods such as: a) evolutionary algorithms and particle swarm optimization and b) non-linear mapping and knowledgeembedding approachessuchasarti?cialneuralnetworksandfuzzy logic, to name a few.
Such approaches have been shown to perform well in challenging settings. Speci?cally, CI are powerful tools which o?er several potential bene?ts such as: a) robustness (impose little or no requirements on the objective function) b) versatility (handle highly non-linear mappings) c) self-adaptionto improveperformance and d) operationin parallel(making it easy to decompose complex tasks). However, the successful application of CI methods to real-world problems is not straightforward and requires both expert knowledge and trial-and-error experiments.
Table of Contents
- New Hybrid Intelligent Systems to Solve Linear and Quadratic Optimization Problems and Increase Guaranteed Optimal Convergence Speed of Recurrent ANN.- A Novel Optimization Algorithm Based on Reinforcement Learning.- The Use of Opposition for Decreasing Function Evaluations in Population-Based Search.- Search Procedure Exploiting Locally Regularized Objective Approximation. A Convergence Theorem for Direct Search Algorithms.- Optimization Problems with Cardinality Constraints.- Learning Global Optimization Through a Support Vector Machine Based Adaptive Multistart Strategy.- Multi-Objective Optimization Using Surrogates.- A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization.- A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems.- Optimization with Clifford Support Vector Machines and applications.- A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets.- An Integrated Approach to Speed Up GA-SVM Feature Selection Model.- Computation in Complex Environments
- .- Project Scheduling: Time-Cost Tradeoff Problems.- Systolic VLSI and FPGA Realization of Artificial Neural Networks.- Application of Coarse-Coding Techniques for Evolvable Multirobot Controllers.
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