Synopsis
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.
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