Presents research carried out at the Engineering Design Centre in Plymouth, one of six national centres designated for university-based engineering design in the UK

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.

Prior to the early 1990s the term 'evolutionary computing' (EC) would have meant little to most practising engineers unless they had a particular interest in emerging computing technologies or were part of an organisation with significant in-house research activities. It was around this time that the first tentative utilisation of relatively simple evolutionary algorithms within engineering design began to emerge in the UK The potential was rapidly recognised especially within the aerospace sector with both Rolls Royce and British Aerospace taking a serious interest while in the USA General Electric had already developed a suite of optimisation software which included evolutionary and adaptiv,e search algorithms. Considering that the technologies were already twenty-plus years old at this point the long gestation period is perhaps indicative of the problems associated with their real-world implementation. Engineering application was evident as early as the mid-sixties when the founders of the various techniques achieved some success with computing resources that had difficulty coping with the population-based search characteristics of the evolutionary algorithms. Unlike more conventional, deterministic optimisation procedures, evolutionary algorithms search from a population of possible solutions which evolve over many generations. This largely stochastic process demands serious computing capability especially where objective functions involve complex iterative mathematical procedures.