The Adaptive Computing in Design and Manufacture Conference series is now in its tenth year and has become a well-established, application-oriented meeting recognised by several UK Engineering Institutions and the International Society of Genetic and Evolutionary Computing. The main theme of the conference again relates to the integration of evolutionary and adaptive computing technologies with design and manufacturing processes whilst also taking into account complementary advanced computing technologies. Evolutionary and adaptive computing techniques continue to increase their penetration of industrial and commercial practice as their powerful search, exploration and optimisation capabilities become ever more apparent. The last two years have seen a very significant increase in the development of commercial software tools utilising adaptive computing technologies and the emergence of related commercial research and consultancy organisations supporting the introduction of best practice in terms of industrial utilisation.
Adaptive Computing in
Design and Manufacture V
is comprised of selected papers that cover a diverse set of industrial application areas including: engineering design and design environments, manufacturing process design, scheduling and control, electronic circuit design, fault detection. Various aspects of search and optimisation such as multi-objective and constrained optimisation are also investigated in the context of integration with industrial processes. In addition to evolutionary computing techniques, both neural-net and agent-based technologies play a role in a number of contributions. This collection of papers will be of particular interest to both industrial researchers and practitioners in addition to the academic research communities of engineering, operational research and computer science.
Sprache
Verlagsort
Verlagsgruppe
Illustrationen
103 s/w Abbildungen
XI, 393 p. 103 illus.
Dateigröße
ISBN-13
978-0-85729-345-9 (9780857293459)
DOI
10.1007/978-0-85729-345-9
Schweitzer Klassifikation
1.1 Pareto Solutions of Multipoint Design of Supersonic Wings Using Evolutionary Algorithms.- 1.2 Notes on Design Through Artificial Evolution: Opportunities and Algorithms.- 1.3 Testing, Evaluation and Performance of Optimization and Learning Systems.- 1.4 From Evolutionary Computation to Natural Computation.- 2.1 Experiences with Hybrid Evolutionary/Local Optimization for Process Design.- 2.2 The Development of a Grid-based Engineering Design Problem Solving Environment.- 2.3 A Hybrid Search Technique for Inverse Transient Analysis in Water Distribution Systems.- 2.4 Optimisation of Thermal Power Plant Designs: A Graph-based Adaptive Search Approach.- 2.5 Genetic Algorithm Search for Stent Design Improvements.- 2.6 A Multi-objective Optimisation Approach for the Conceptual Design of Frame Structures.- 2.7 Multi-Objective Evolutionary Topological Optimum Design.- 2.8 Better Surface Intersections by Constrained Evolution.- 2.9 Inverse Identification of Boundary Constants for Electronic Packages Using Modified Micro Genetic Algorithm and the Reduced-basis Method.- 2.10 Extrinsic Evolution of Finite State Machines.- 3.1 Neural Computing Approach to Shape Change Estimation in Hot Isostatic Pressing.- 3.2 Multi-criterion Tackling Bottleneck Machines and Exceptional Parts in Cell Formation Using Genetic Algorithms.- 3.3 A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms.- 4.1 Evolutionary Multi-criteria Optimization for Improved Design of Optimal Control Systems.- 4.2 Explorations in Fuzzy Classifier System Architectures.- 4.3 Evolving Temporal Rules with the Delayed Action Classifier System - Analysis and New Results.- 4.4 Adaptive Image Segmentation Based on Visual Interactive Feedback Learning.- 5.1Adapting Problem Specifications and Design Solutions Using Co-evolution.- 5.2 Handling Constraints in Genetic Algorithms Using Dominance- based Tournaments.- 5.3 The Optimisation of Multivariate Robust Design Criteria.- 5.4 Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits.- 5.5 Constrained Optimisation with the Fuzzy Clustering Evolution Strategy.- 5.6 Constrained Optimization Using an Evolutionary Programming- based Cultural Algorithm.- 5.7 A Data Mining Tool Using an Intelligent Processing System with a Clustering Application.- 6.1 Full Elite Sets for Multi-objective Optimisation.- 6.2 Agent-based Support within an Interactive Evolutionary Design System.- 6.3 A Multi-Agent Architecture for Business Process Management Adapts to Unreliable Performance.- 6.4 Real-time Co-ordinated Scheduling Using a Genetic Algorithm.- Author Index.
