Evolutionary and Adaptive Computing in Engineering Design

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.

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.- 1.4.1 The Design Process.- 1.4.2 Routine, Innovative and Creative Design.- 1.4.3 Complementary Computational Intelligence Techniques.- 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.- 2.3.1 The Simple Genetic Algorithm.- 2.3.2 Binary Mapping and the Schema Theorem.- 2.3.3 Real Number Representation.- 2.3.4 The Operators.- 2.3.5 Elitism and Exploitation versus Exploration.- 2.3.6 Self-adaptation.- 2.4 GA Variants.- 2.4.1 The CHC Genetic Algorithm.- 2.4.2 The EcoGA.- 2.4.3 The Structured Genetic Algorithm.- 2.4.4 The Breeder GA and the Messy GA.- 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.- 4.3.1 Introduction.- 4.3.2 Integrating the Cooling Hole Model with a Genetic Algorithm.- 4.3.3 Further Work.- 4.4 Evolutionary FIR Digital Filter Design.- 4.4.1 Introduction.- 4.4.2 Coding Using a Structured GA.- 4.4.3 Fitness Function.- 4.4.4 Results.- 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.- 6.4.1 Two-dimensional Test Functions.- 6.4.2 Engineering Design Domains.- 6.4.3 Single-objective/Continuous Design Space.- 6.4.4 Multi-level , Mixed-parameter Design Space.- 6.5 Alternative COGA Structures.- 6.5.1 Introduction.- 6.5.2 The COGA Variants.- 6.5.3 Summary of Results.- 6.5.4 Search Space Sampling.- 6.5.5 The Dynamic Adaptive Filter.- 6.6 Agent-assisted Boundary Identification.- 6.7 Discussion.- 7. Whole-system Design.- 7.1 Introduction.- 7.1.1 Whole-system Design.- 7.1.2 Designer Requirement.- 7.1.3 Design Environments.- 7.2 Previous Related Work.- 7.3 The Hydropower System.- 7.3.1 The System.- 7.3.2 The Model.- 7.4 The Structured Genetic Algorithm.- 7.4.1 The Algorithm.- 7.4.2 Dual Mutation Strategies.- 7.4.3 stGA Results.- 7.5 Simplifying the Parameter Representation.- 7.6 Results and Discussion.- 7.7 Thermal Power System Redesign.- 7.7.1 Introduction.- 7.7.2 Problem Definition.- 7.7.3 A Hybrid GA-SLP Algorithm.- 7.7.4 The Design Application.- 7.8 Discussion.- 8. Variable-length Hierarchies and System Identification.- 8.1 Introduction.- 8.2 Improving Rolls Royce Cooling Hole Geometry Models.- 8.2.1 Introduction.- 8.2.2 Simple Curve and Surface Fitting.- 8.2.3 Evolving Formulae to Determine the Friction Factor in Turbulent Pipe Flow.- 8.2.4 Eddy Correlations for Laminar Two-dimensional Sudden Expansion Flows.- 8.3 Discussion of Initial Application.- 8.4 Further Development of the GP Paradigm.- 8.4.1 Development of Node Complexity Ratings.- 8.4.2 Constrained-complexity Crossover.- 8.4.3 Steady-state GP.- 8.4.4 Injection Mutation.- 8.5 Symbolic Regression with HDRAM-GP.- 8.6 Dual-agent Integration.- 8.7 Return to Engineering Applications.- 8.7.1 Introduction.- 8.7.2 Explicit Formula for Friction Factor In Turbulent Pipe Flow.- 8.7.3 Eddy Correlations for Laminar Two-dimensional Sudden Expansion Flows.- 8.7.4 Thermal Paint Jet Turbine Blade Data.- 8.8 Discussion.- 9. Evolutionary Constraint Satisfaction and Constrained Optimisation.- 9.1 Introduction.- 9.2 Dealing with Explicit Constraints.- 9.2.1 The Fault Coverage Test Code Generation Problem.- 9.2.2 The Inductive Genetic Algorithm.- 9.2.3 Application to the Problem.- 9.3 Implicit Constraints.- 9.4 Defining Feasible Space.- 9.4.1 Introduction.- 9.4.2 The Problem Domain.- 9.4.3 Fixing a Feasible Point.- 9.4.4 Creating a Feasible Subset.- 9.4.5 Establishing the Degree of Constraint Violation.- 9.4.6 Results and Discussion.- 9.5 Satisfying Constraint in the Optimisation of Thermal Power Plant Design.- 9.6 GA/Ant-colony Hybrid for the Flight Trajectory Problem.- 9.6.1 The Problem Domain.- 9.6.2 The Ant-colony Model for Continuous-space Search.- 9.6.3 A Hybrid Search Framework.- 9.7 Other Techniques.- 9.8 Discussion.- 10. Multi-objective Satisfaction and Optimisation.- 10.1 Introduction.- 10.2 Established Multi-objective Optimisation Techniques.- 10.2.1 Weighted-sum-based Optimisation.- 10.2.2 Lexicographic Order-based Optimisation.- 10.2.3 The Pareto Method.- 10.2.4 Pareto Examples.- 10.2.5 The Vector-evaluated Genetic Algorithm.- 10.2.6 Comparison of the Various Techniques.- 10.3 Interactive Approaches to Multi-objective Satisfaction/Optimisation.- 10.4 Qualitative Evaluation ofGA-generated Design Solutions.- 10.4.1 Introduction.- 10.4.2 The Design Model.- 10.4.3 Adaptive Restricted Tournament Selection.- 10.4.4 Assessing the Qualitative Fitness of High-performance Solutions.- 10.4.5 Knowledge Representation.- 10.4.6 Typical Results.- 10.4.7 Further Work.- 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.- 11.4.1 Introduction.- 11.4.2 Preferences.- 11.4.3 Example Application.- 11.5 The Co-evolutionary Environment.- 11.5.1 Introduction.- 11.5.2 Initial Methodology.- 11.5.3 The Range Constraint Map.- 11 .5.4 Sensitivity Analysis.- 11.5.5 Results.- 11.6 Combining Preferences with the Co-evolutionary Approach.- 11.7 Cluster-oriented Genetic Algorithm s as Information Gathering Processes.- 11.7.1 Introduction.- 11.7.2 Extraction and Processing of COGA-generated Data.- 11.8 Machine-based Agent Support.- 11.8.1 Introduction.- 11.8.2 Interface Agents.- 11.8.3 Communication Agents.- 11.8.4 Search Agents.- 11.8.5 Information Processing Agents.- 11.8.6 Negotiating Agents.- 11.9 Machine-based Design Space Modification.- 11.9.1 Introduction.- 11.9.2 The Developed EcoGA Framework.- 11.9.3 Determining Direction and Extent of Design Space Extension.- 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.- 12.6.1 Introduction.- 12.6.2 Stand-alone CHC and DSR CHC.- 12.7 The Injection Island GA.- 12.8 Dynamic Injection.- 12.9 Distributed Search Techniques.- 12.9.1 Introduction.- 12.9.2 Co-operative Search.- 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.