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Introduction to Evolutionary
Computing |
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List of figures in the book available to download |
Most of the figures in the book (excluding pseudocode) are available to download in either encapsulated postscript (eps) or jpeg (jpg) format. You can also download a zip file containing all of the figures for a given chapter
All of the figures by chapter as zip files:
2. What is an Evolutionary Algorithm? eps jpeg
3. Genetic Algorithms eps jpeg
4. Evolution Strategies eps jpg
5. Evolutionary Programming eps jpg
6. Genetic Programming eps jpg
7. Learning Classifier Systems eps jpg
8. Parameter Control in Evolutionary Algorithms eps jpg
9. Multi-Modal Problems and Spatial Distribution eps jpg
10. Hybridisation with Other Techniques: Memetic Algorithms eps jpg
13. Special Forms of Evolution eps jpg
14. Working with Evolutionary Algorithms eps jpg
Individual
Figures
1 Introduction
1.1 Illustration of Wright's adaptive landscape (p 4) eps jpg
1.2 Three steps in the (simplified) meiosis procedure (p 6) (left) eps jpg (middle) eps jpg (right) eps jpg
1.3 The pathway from DNA to protein (p 7) eps jpg
1.4 Optimisation problems (p 9) eps jpg
1.5 Modelling or system identification problems (p 9) eps jpg
1.6 Simulation problems (p 10) eps jpg
1.7 3-D Boom design (p 11): left eps jpg right eps jpg
2
What is an Evolutionary Algorithm?
2.2 The general scheme of an evolutionary algorithm as a flow-chart (p 17) eps jpg
2.4 Typical progress of an EA in terms of population distribution (p 30): left eps jpg middle eps jpg right eps jpg
2.5 Typical progress of an EA in terms of the best fitness (p 30) eps jpg
2.6 Illustration of why heuristic initialisation might not be worth additional effort (p 31) eps jpg
2.7 Illustration of why long runs might not be worth performing (p 31) eps jpg
2.8 1980s view of EA
performance after Goldberg (p 32) eps
jpg
3 Genetic Algorithms
3.1 Bitwise mutation for binary encodings (p 43) eps jpg
3.2 Swap mutation (p 45) eps jpg
3.3 Insert mutation (p 46) eps jpg
3.4 Scramble mutation (p 46) eps jpg
3.5 Inversion mutation (p 47) eps jpg
3.6 One-point crossover (p 48) eps jpg
3.7 n-point crossover (p 48) eps jpg
3.8 Uniform crossover (p 49) eps jpg
3.9 Simple arithmetic recombination (p 51) eps jpg
3.10 Single arithmetic recombination (p 51) eps jpg
3.11 Whole arithmetic recombination (p 51) eps jpg
3.12 PMX, step 1 (p 53) eps jpg
3.13 PMX, step 2 (p 53) eps jpg
3.14 PMX, step 3 (p 53) eps jpg
3.15 Order crossover, step 1 (p 55) eps jpg
3.16 Order crossover, step 2 (p 56) eps jpg
3.17 Cycle crossover, step 1 (p 56) eps jpg
3.18 Cycle crossover, step 2 (p 57) eps jpg
3.19 The susceptibility of fitness proportionate selection to function transposition (p 60) eps jpg
4
Evolution Strategies
4.2 Uncorrelated mutation with one step size (p 76) eps jpg
4.3 Uncorrelated mutation with n step sizes (p 77) eps jpg
4.4 Correlated mutation (p 79) eps jpg
4
Evolutionary Programming
5.1 Example of a finite state machine consisting of three states (p 90) eps jpg
5.2 Finite state machine as a predictor (p 91) eps jpg
6
Genetic Programming
6.1 Parse tree (p 103) eps jpg
6.2 Parse trees (p104): left eps jpg right eps jpg
6.3 Parse tree for program (p 104) eps jpg
6.4 GP flowchart (p 106): left eps jpg right eps jpg
6.5 GP mutation (p 107): left eps jpg right eps jpg
6.6 GP crossover (p 108): top
left eps jpg, top
right eps jpg,
bottom
left eps jpg,
bottom right eps jpg
7 Learning Classifier
Systems
7.1 Structure of a learning classifier system (p 119) eps jpg
7.2 Membership of three fuzzy
classes as a function of distance (p 125) eps jpg
8 Parameter Control in Evolutionary
Algorithms
8.1 Global taxonomy of
parameter setting in EAs (p 139) eps jpg
9 Multi-Modal Problems and
Spatial Distribution
9.1 Landscape features (p 154) eps jpg
9.2 Idealised population distributions under fitness sharing and crowding (p 165) eps jpg
9.3 Illustration of the Pareto
front (p 167) eps jpg
10 Hybridisation with Other
Techniques: Memetic Algorithms
10.1 1990s view of EA performance after Michalewicz (p 174) eps jpg
10.3 Places to incorporate knowledge or other operators within the evolutionary cycle (p 179) eps jpg
13
Special Forms of Evolution
13.1 A screenshot of the Mondriaan evolver (p 230) eps jpg
13.2 A possible representation of Mondriaan-like images (p 231) eps jpg
13.3 The main cycle of the Mondriaan evolver example (p 232) eps jpg
14
Working with Evolutionary Algorithms
14.1 Comparing algorithms by fixed termination times (p 247) eps jpg
14.2 Comparing algorithms by their scale-up behaviour (p 249) eps jpg
14.3 Comparing algorithms by histograms of the best found fitness values (p 251) eps jpg
14.4 Comparing algorithms on problem instances with a scalable parameter (p 255) eps jpg
