Introduction to Evolutionary Computing
by 
A.E. Eiben and J.E. Smith


 

List of figures in the book available to download

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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:

1. Introduction eps  jpeg

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  

 

5  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