Evolutionary and Adaptive Computing in Engineering Design

1. 1 Setting the Scene. - 1. 2 Why Evolutionary/Adaptive Computing?. - 1. 3 The UK EPSRC Engineering Design Centres. - 1. 4 Evolutionary and Adaptive Computing Integration. - 1. 5 Generic Design Issues. - 1. 6 Moving On. - 2. Established Evolutionary Search Algorithms. - 2. 1 Introduction. - 2. 2 A Brief History of Evolutionary Search Techniques. - 2. 3 The Genetic Algorithm. - 2. 4 GA Variants. - 2. 5 Evolution Strategies. - 2. 6 Evolutionary Programming. - 2. 7 Genetic Programming. - 2. 8 Discussion. - 3. Adaptive Search and Optimisation Algorithms. - 3. 1 Introduction. - 3. 2 The Ant-colony Metaphor. - 3. 3 Population-based Incremental Learning. - 3. 4 Simulated Annealing. - 3. 5 Tabu Search. - 3. 6 Scatter Search. - 3. 7 Discussion. - 4. Initial Application. - 4. 1 Introduction. - 4. 2 Applying the GA to the Shape Optimisation of a Pneumatic Low-head Hydropower Device. - 4. 3 The Design ofGas Turbine Blade Cooling Hole Geometries. - 4. 5 Evolutionary Design of a Three-centred Concrete Arch Dam. - 4. 6 Discussion. - 5. The Development of Evolutionary and Adaptive Search Strategies for Engineering Design. - 5. 1 Introduction. - 5. 2 Cluster-oriented Genetic Algorithms. - 5. 3 The GAANT (GA-Ant) Algorithm. - 5. 4 DRAM and HDRAM Genetic Programming Variants. - 5. 5 Evolutionary and Adaptive Search Strategies for Constrained Problems. - 5. 6 Evolutionary Multi-criterion Satisfaction. - 5. 7 Designer Interaction within an Evolutionary Design Environment. - 5. 8 Dynamic Shape Refinement and Injection Island Variants. - 5. 9 Discussion. - 6. Evolutionary Design Space Decomposition. - 6. I Introduction. - 6. 2 Multi-modal Optimisation. - 6. 3 Cluster-oriented Genetic Algorithms. - 6. 4 Application of vmCOGA. - 6. 5 Alternative COGA Structures. - 6. 6 Agent-assisted Boundary Identification. - 6. 7 Discussion. - 7. Whole-system Design. - 7. 1 Introduction. - 7. 2 Previous Related Work. - 7. 3 The Hydropower System. - 7. 4 The Structured Genetic Algorithm. - 7. 5 Simplifying the Parameter Representation. - 7. 6 Results and Discussion. - 7. 7 Thermal Power System Redesign. - 7. 8 Discussion. - 8. Variable-length Hierarchies and System Identification. - 8. 1 Introduction. - 8. 2 Improving Rolls Royce Cooling Hole Geometry Models. - 8. 3 Discussion of Initial Application. - 8. 4 Further Development of the GP Paradigm. - 8. 5 Symbolic Regression with HDRAM-GP. - 8. 6 Dual-agent Integration. - 8. 7 Return to Engineering Applications. - 8. 8 Discussion. - 9. Evolutionary Constraint Satisfaction and Constrained Optimisation. - 9. 1 Introduction. - 9. 2 Dealing with Explicit Constraints. - 9. 3 Implicit Constraints. - 9. 4 Defining Feasible Space. - 9. 5 Satisfying Constraint in the Optimisation of Thermal Power Plant Design. - 9. 6 GA/Ant-colony Hybrid for the Flight Trajectory Problem. - 9. 7 Other Techniques. - 9. 8 Discussion. - 10. Multi-objective Satisfaction and Optimisation. - 10. 1 Introduction. - 10. 2 Established Multi-objective Optimisation Techniques. - 10. 3 Interactive Approaches to Multi-objective Satisfaction/Optimisation. - 10. 4 Qualitative Evaluation ofGA-generated Design Solutions. - 10. 5 Cluster-oriented Genetic Algorithms for Multi-objective Satisfaction. - 10. 6 Related Work and Further Reading. - 10. 7 Discussion. - 11. Towards Interactive Evolutionary Design Systems. - 11. 1 Introduction. - 11. 2 System Requirements. - 11. 3 The Design Environment and the IEDS. - 11. 4 The Rule-based Preference Component. - 11. 5 The Co-evolutionary Environment. - 11. 6 Combining Preferences with the Co-evolutionary Approach. - 11. 7 Cluster-oriented Genetic Algorithm s as Information Gathering Processes. - 11. 8 Machine-based Agent Support. - 11. 9 Machine-based Design Space Modification. - 11. 10 Discussion. - 12. Population-based Search Shape Optimisation and Computational Expense. - 12. 1 Introduction. - 12. 2 Parallel Distributed and Co-evolutionary Strategies. - 12. 3 Introducing the Problem and the Developed Strategies. - 12. 4 The Evaluation Model. - 12. 5 Initial Results. - 12. 6 Dynamic Shape Refinement. - 12. 7 The Injection Island GA. - 12. 8 Dynamic Injection. - 12. 9 Distributed Search Techniques. - 12. 10 Discussion. - 13. Closing Discussion. - 13. 1 Introduction. - 13. 2 Difficulties Facing Successful Integration ofEC with Engineering Design. - 13. 3 Overview of the Techniques and Strategies Introduced. - 13. 4 Final Remarks. - Appendix A. Some Basic Concepts. - References. Language: English