Computational Intelligence - An Introduction - A. P. Engelbrecht

References (1,039)

1. Selection Operators . . . . . . . . . . . . . . . . . . . . . . . .
2. 3 Strategy Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.1 Static Strategy Parameters . . . . . . . . . . . . . . . . . . . . 11.3.2 Dynamic Strategies . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.3 Self-Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. 4 Evolutionary Programming Implementations . . . . . . . . . . . . . . 11.4.1 Classical Evolutionary Programming . . . . . . . . . . . . . . . 11.4.2 Fast Evolutionary Programming . . . . . . . . . . . . . . . . .
4. 4.3 Exponential Evolutionary Programming . . . . . . . . . . . . . 11.4.4 Accelerated Evolutionary Programming . . . . . . . . . . . . .
5. 4.5 Momentum Evolutionary Programming . . . . . . . . . . . . .
6. 4.6 Evolutionary Programming with Local Search . . . . . . . . . .
7. 4.7 Evolutionary Programming with Extinction . . . . . . . . . . .
8. 4.8 Hybrid with Particle Swarm Optimization . . . . . . . . . . . .
9. Advanced Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.1 Constraint Handling Approaches . . . . . . . . . . . . . . . . . 11.5.2 Multi-Objective Optimization and Niching . . . . . . . . . . .
10. 5.3 Dynamic Environments . . . . . . . . . . . . . . . . . . . . . .
11. 6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.1 Finite-State Machines . . . . . . . . . . . . . . . . . . . . . . . 11.6.2 Function Optimization . . . . . . . . . . . . . . . . . . . . . . .
12. Training Neural Networks . . . . . . . . . . . . . . . . . . . . . 11.6.4 Real-World Applications . . . . . . . . . . . . . . . . . . . . . .
13. Assignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14. 2 Illustration of Multi-parent Center of Mass Crossover Operators . . . . 151
15. 3 Diagonal Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
16. 4 Mutation Operators for Binary Representations . . . . . . . . . . . . . 155
17. 5 An Island GA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 10.1 Tree-Representation of XOR . . . . . . . . . . . . . . . . . . . . . . . . 178 10.2 Tree-Representation for Mathematical Expressions . . . . . . . . . . . 179 10.3 Genetic Programming Crossover . . . . . . . . . . . . . . . . . . . . . 181 10.4 Genetic Programming Mutation Operators . . . . . . . . . . . . . . . 183 11.
18. Finite-State Machine [278] . . . . . . . . . . . . . . . . . . . . . . . . . 208 12.1 Illustration of Mutation Distributions for ES . . . . . . . . . . . . . . 219 12.2 Directed Mutation Operator for ES . . . . . . . . . . . . . . . . . . . . 225 12.3 Biased Mutation for Evolution Strategies . . . . . . . . . . . . . . . . 230 13.1 Differential Evolution Mutation and Crossover Illustrated . . . . . . . 243 13.2 Angle Modulation Illustrated . . . . . . . . . . . . . . . . . . . . . . . 254 14.1 Illustration of Population and Belief Spaces of Cultural Algorithms . . 263 14.2 Illustration of Belief Cells . . . . . . . . . . . . . . . . . . . . . . . . . 272 16.1 Geometrical Illustration of Velocity and Position Updates . . . . . . . 294 16.
19. Multi-particle gbest PSO Illustration . . . . . . . . . . . . . . . . . . . 295 16.3 Illustration of lbest PSO . . . . . . . . . . . . . . . . . . . . . . . . . . 296 16.4 Example Social Network Structures . . . . . . . . . . . . . . . . . . . . 302 16.5 Effects of Velocity Clamping . . . . . . . . . . . . . . . . . . . . . . . . 305 16.6 Stochastic Particle Trajectory for w = 0.9 and c 1 = c 2 = 2.0 . . . . . . 315 17.1 Binary Bridge Experiment . . . . . . . . . . . . . . . . . . . . . . . . . 361 17.2 Shortest Path Selection by Forager Ants . . . . . . . . . . . . . . . . . 362 17.3 Graph for Shortest Path Problems . . . . . . . . . . . . . . . . . . . . 365 17.
20. 2-opt and 3-opt Local Search Heuristic . . . . . . . . . . . . . . . . . . 408 18.
21. Antigen-Antibody-Complex . . . . . . . . . . . . . . . . . . . . . . . . 417 18.2 White Cell Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 18.3 Life Cycle of A Lymphocyte . . . . . . . . . . . . . . . . . . . . . . . . 418 18.
22. B-Cell Develops into Plasma Cell, Producing Antibodies . . . . . . . . 419
23. 5 Macrophage and NKTC . . . . . . . . . . . . . . . . . . . . . . . . . . 420 18.
24. Co-Stimulation of T-Cell by an APC . . . . . . . . . . . . . . . . . . . 423
25. 1 r-Continuous Matching Rule . . . . . . . . . . . . . . . . . . . . . . . 428
26. 2 Adapted Negative Selection . . . . . . . . . . . . . . . . . . . . . . . . 430 20.1 Illustration of Membership Function for Two-Valued Sets . . . . . . . 455 20.2 Illustration of tall Membership Function . . . . . . . . . . . . . . . . . 455 20.3 Example Membership Functions for Fuzzy Sets . . . . . . . . . . . . . 458 20.4 Illustration of Fuzzy Set Containment . . . . . . . . . . . . . . . . . . 458 20.5 Illustration of Fuzzy Operators . . . . . . . . . . . . . . . . . . . . . . 460 17.
27. Simple ACO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 367 17.3 Ant System Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 371 17.4 Ant Colony System Algorithm . . . . . . . . . . . . . . . . . . . . . . 374 17.5 MMAS Algorithm with Periodic Use of the Global-Best Path . . . . 377 17.6 ANTS Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 17.7 Lumer-Faieta Ant Colony Clustering Algorithm . . . . . . . . . . . . 388 17.8 Continuous Ant Colony Optimization Algorithm . . . . . . . . . . . . 397 17.
28. 9 Multiple Colony ACO Local Sharing Mechanism . . . . . . . . . . . . 400 19.
29. Basic AIS Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
30. 2 Training ALCs with Negative Selection . . . . . . . . . . . . . . . . . 429
31. 3 CLONALG Algorithm for Pattern Recognition . . . . . . . . . . . . . 432 19.
32. A Multi-layered AIS Algorithm . . . . . . . . . . . . . . . . . . . . . 435
33. 5 Artificial Immune Network (AINE) . . . . . . . . . . . . . . . . . . . 437 19.6 Resource Allocation in the Artificial Immune Network . . . . . . . . 438 19.7 Self Stabilizing AIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439
34. 8 aiNet Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 443 19.
35. 9 Initialization Phase for an Adaptive Mailbox . . . . . . . . . . . . . . 446 19.10 Running Phase for an Adaptive Mailbox . . . . . . . . . . . . . . . . 447
36. A.1 General Local Search Algorithm . . . . . . . . . . . . . . . . . . . . .
37. A.2 Simulated Annealing Algorithm . . . . . . . . . . . . . . . . . . . . . 558
38. Comment on the eligibility of Turing's test for computer intelligence, and his belief that computers with 10 9 bits of storage would pass a 5-minute version of his test with 70% probability.
39. Comment on the eligibility of the definition of artificial intelligence as given by the 1996 IEEE Neural Networks Council.
40. Based on the definition of CI given in this chapter, show that each of the paradigms (NN, EC, SI, AIS, and FS) does satisfy the definition.
41. Explain how a SOM can be used to distinguish among different hand gestures.
42. Discuss a number of ways in which the SOM can be adapted to reduce its com- putational complexity.
43. Explain how a SOM can be used as a classifier.
44. Explain how it is possible for the SOM to train on data with missing values.
45. How can a trained SOM be used to determine an appropriate value if for a given input pattern an attribute does not have a value.
46. Compare the performance of an RBFNN and a FFNN on a classification problem from the UCI machine learning repository (http://www.ics.uci.edu/~mlearn/MLRepository.html).
47. Compare the performance of the Gaussian and logistic basis functions.
48. Suggest an alternative to compute the input-to-hidden weights instead of using LVQ-I.
49. Investigate alternative methods to initialize an RBF NN.
50. Is it crucial that all w kj be initialized to small random values? Motivate your answer.
51. Develop a PSO, DE, and EP algorithm to train an RBFNN. 00000 00000 00000 11111 11111 11111 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 00000 00000 00000 11111 11111 11111 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 0000 0000 0000 1111 1111 1111 0000 0000 0000 1111 1111 1111 00000 00000 00000 11111 11111 11111 0000 0000 0000 1111 1111 1111 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 00000 00000 00000 11111 11111 11111 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 0000 0000 0000 1111 1111 1111 0000 0000 0000 1111 1111 1111 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 00000 00000 00000 11111 11111 11111 00000 00000 00000 11111 11111 11111 0000000000000000 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 1111111111111111 0000 0000 0000 0000 1111 1111 1111 1111 00000 00000 00000 11111 11111 11111 0000000000000000 0000000000000000 0000000000000000 1111111111111111 1111111111111111 1111111111111111 0000 0000 0000 1111 1111 1111 Reference to the time step t is included here to allow dynamic problems where distances may change over time.
52. Comment on the following strategy: Let the amount of pheromone deposited be a function of the best route. That is, the ant with the best route, deposits more pheromone. Propose a pheromone update rule.
53. Comment on the similarities and differences between the ant colony approach to clustering and SOMs.
54. For the ant clustering algorithm, explain why (a) the 2D-grid should have more sites than number of ants;
55. Antigen Epitope Variable region
56. Identify the main difference between the classicial view of the NIS (a) and network theory, (b) and danger theory.
57. Discuss the merit of the following statement: "The lymphocytes in the classical view perform a pattern matching function."
58. At this point, discuss how the principles of a NIS can be used to solve real-world problems where anomalies need to be detected, such as fraud.
59. Discuss the merit of the following statement: "A model of the NIS (based on the classical view) can be used as a classifier."
60. How can an AIS be used for classification problems where there is more than two classes?
61. How does the self stabilizing AIS improve on AINE?
62. How does the enhanced artificial immune network improve on AINE?
63. The enhanced artificial immune network calculates the number of clones gener- ated by an ARB as nc = l × sl. (a) Why is the number of clones a function of the stimulation level? (b) Explain the consequences of large and small values of l.
64. For the aiNet model in Algorithm 19.5, how does network suppression help to control the size of the ARB population?
65. Why should an antibody be mutated less the higher the affinity of the antibody to an antigen training pattern, considering the aiNet model?
66. Discuss the influence of different values for the danger signal threshold as applied in the adaptive mailbox problem discussed in Section 19.5.2
67. 2.2 Mamdani Fuzzy Controller Mamdani and Assilian [554] produced the first fuzzy controller. Mamdani-type con- trollers follow the following simple steps:
68. Identify and name input linguistic variables and define their numerical ranges.
69. Identify and name output linguistic variables and define their numerical ranges.
70. Define a set of fuzzy membership functions for each of the input variables, as well as the output variables.
71. Construct the rule base that represents the control strategy.
72. Perform fuzzification of input values.
73. Perform inferencing to determine firing strengths of activated rules. References
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