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Files
  1. Get Bonus Downloads Here.url182 bytes
  2. 001. Part 1. Deterministic search algorithms.mp46.3 MB
  3. 002. Chapter 1. Introduction to search and optimization.mp424.2 MB
  4. 003. Chapter 1. Going from toy problems to the real world.mp47.9 MB
  5. 004. Chapter 1. Basic ingredients of optimization problems.mp426.8 MB
  6. 005. Chapter 1. Well-structured problems vs. ill-structured problems.mp422.4 MB
  7. 006. Chapter 1. Search algorithms and the search dilemma.mp47.5 MB
  8. 007. Chapter 1. Summary.mp45.5 MB
  9. 008. Chapter 2. A deeper look at search and optimization.mp4168.4 MB
  10. 009. Chapter 2. Classifying search and optimization algorithms.mp421.8 MB
  11. 010. Chapter 2. Heuristics and metaheuristics.mp435.1 MB
  12. 011. Chapter 2. Nature-inspired algorithms.mp418.4 MB
  13. 012. Chapter 2. Summary.mp45.5 MB
  14. 013. Chapter 3. Blind search algorithms.mp444.7 MB
  15. 014. Chapter 3. Graph search.mp411.6 MB
  16. 015. Chapter 3. Graph traversal algorithms.mp449.4 MB
  17. 016. Chapter 3. Shortest path algorithms.mp428.5 MB
  18. 017. Chapter 3. Applying blind search to the routing problem.mp423.4 MB
  19. 018. Chapter 3. Summary.mp46.5 MB
  20. 019. Chapter 4. Informed search algorithms.mp412.3 MB
  21. 020. Chapter 4. Minimum spanning tree algorithms.mp423.9 MB
  22. 021. Chapter 4. Shortest path algorithms.mp4126.4 MB
  23. 022. Chapter 4. Applying informed search to a routing problem.mp442.3 MB
  24. 023. Chapter 4. Summary.mp46.7 MB
  25. 024. Part 2. Trajectory-based algorithms.mp44.9 MB
  26. 025. Chapter 5. Simulated annealing.mp410.7 MB
  27. 026. Chapter 5. The simulated annealing algorithm.mp475.3 MB
  28. 027. Chapter 5. Function optimization.mp435.7 MB
  29. 028. Chapter 5. Solving Sudoku.mp422.5 MB
  30. 029. Chapter 5. Solving TSP.mp426.8 MB
  31. 030. Chapter 5. Solving a delivery semi-truck routing problem.mp420.3 MB
  32. 031. Chapter 5. Summary.mp47.2 MB
  33. 032. Chapter 6. Tabu search.mp417.0 MB
  34. 033. Chapter 6. Tabu search algorithm.mp449.7 MB
  35. 034. Chapter 6. Solving constraint satisfaction problems.mp415.1 MB
  36. 035. Chapter 6. Solving continuous problems.mp49.7 MB
  37. 036. Chapter 6. Solving TSP and routing problems.mp436.1 MB
  38. 037. Chapter 6. Assembly line balancing problem.mp445.9 MB
  39. 038. Chapter 6. Summary.mp44.0 MB
  40. 039. Part 3. Evolutionary computing algorithms.mp44.9 MB
  41. 040. Chapter 7. Genetic algorithms.mp438.7 MB
  42. 041. Chapter 7. Introducing evolutionary computation.mp428.5 MB
  43. 042. Chapter 7. Genetic algorithm building blocks.mp451.4 MB
  44. 043. Chapter 7. Implementing genetic algorithms in Python.mp440.2 MB
  45. 044. Chapter 7. Summary.mp49.6 MB
  46. 045. Chapter 8. Genetic algorithm variants.mp418.5 MB
  47. 046. Chapter 8. Real-valued GA.mp448.0 MB
  48. 047. Chapter 8. Permutation-based GA.mp427.5 MB
  49. 048. Chapter 8. Multi-objective optimization.mp420.3 MB
  50. 049. Chapter 8. Adaptive GA.mp411.0 MB
  51. 050. Chapter 8. Solving the traveling salesman problem.mp48.4 MB
  52. 051. Chapter 8. PID tuning problem.mp424.3 MB
  53. 052. Chapter 8. Political districting problem.mp427.3 MB
  54. 053. Chapter 8. Summary.mp47.8 MB
  55. 054. Part 4. Swarm intelligence algorithms.mp45.4 MB
  56. 055. Chapter 9. Particle swarm optimization.mp440.0 MB
  57. 056. Chapter 9. Continuous PSO.mp481.3 MB
  58. 057. Chapter 9. Binary PSO.mp411.8 MB
  59. 058. Chapter 9. Permutation-based PSO.mp47.3 MB
  60. 059. Chapter 9. Adaptive PSO.mp414.9 MB
  61. 060. Chapter 9. Solving the traveling salesman problem.mp414.9 MB
  62. 061. Chapter 9. Neural network training using PSO.mp471.1 MB
  63. 062. Chapter 9. Summary.mp43.8 MB
  64. 063. Chapter 10. Other swarm intelligence algorithms to explore.mp429.9 MB
  65. 064. Chapter 10. ACO metaheuristics.mp421.4 MB
  66. 065. Chapter 10. ACO variants.mp467.7 MB
  67. 066. Chapter 10. From hive to optimization.mp431.0 MB
  68. 067. Chapter 10. Exploring the artificial bee colony algorithm.mp438.7 MB
  69. 068. Chapter 10. Summary.mp48.4 MB
  70. 069. Part 5. Machine learning-based methods.mp45.4 MB
  71. 070. Chapter 11. Supervised and unsupervised learning.mp418.0 MB
  72. 071. Chapter 11. Demystifying machine learning.mp426.7 MB
  73. 072. Chapter 11. Machine learning with graphs.mp4122.7 MB
  74. 073. Chapter 11. Self-organizing maps.mp412.3 MB
  75. 074. Chapter 11. Machine learning for optimization problems.mp419.0 MB
  76. 075. Chapter 11. Solving function optimization using supervised machine learning.mp418.0 MB
  77. 076. Chapter 11. Solving TSP using supervised graph machine learning.mp439.2 MB
  78. 077. Chapter 11. Solving TSP using unsupervised machine learning.mp48.1 MB
  79. 078. Chapter 11. Finding a convex hull.mp436.5 MB
  80. 079. Chapter 11. Summary.mp46.9 MB
  81. 080. Chapter 12. Reinforcement learning.mp4135.9 MB
  82. 081. Chapter 12. Optimization with reinforcement learning.mp423.3 MB
  83. 082. Chapter 12. Balancing CartPole using A2C and PPO.mp450.3 MB
  84. 083. Chapter 12. Autonomous coordination in mobile networks using PPO.mp419.9 MB
  85. 084. Chapter 12. Solving the truck selection problem using contextual bandits.mp432.0 MB
  86. 085. Chapter 12. Journey s end A final reflection.mp46.1 MB
  87. 086. Chapter 12. Summary.mp410.2 MB
  88. 087. Appendix A. Search and optimization libraries in Python.mp416.7 MB
  89. 088. Appendix A. Mathematical programming solvers.mp427.7 MB
  90. 089. Appendix A. Graph and mapping libraries.mp449.2 MB
  91. 090. Appendix A. Metaheuristics optimization libraries.mp434.8 MB
  92. 091. Appendix A. Machine learning libraries.mp434.0 MB
  93. 092. Appendix A. Projects.mp45.7 MB
  94. 093. Appendix B. Benchmarks and datasets.mp47.9 MB
  95. 094. Appendix B. Combinatorial optimization benchmark datasets.mp410.5 MB
  96. 095. Appendix B. Geospatial datasets.mp43.8 MB
  97. 096. Appendix B. Machine learning datasets.mp44.9 MB
  98. 097. Appendix B. Data folder.mp46.2 MB
  99. 098. Appendix C. Exercises and solutions.mp434.4 MB
  100. 099. Appendix C. Chapter 3 Blind search algorithms.mp417.9 MB
  101. 100. Appendix C. Chapter 4 Informed search algorithms.mp419.7 MB
  102. 101. Appendix C. Chapter 5 Simulated annealing.mp425.9 MB
  103. 102. Appendix C. Chapter 6 Tabu search.mp426.5 MB
  104. 103. Appendix C. Chapter 7 Genetic algorithm.mp427.2 MB
  105. 104. Appendix C. Chapter 8 Genetic algorithm variants.mp434.4 MB
  106. 105. Appendix C. Chapter 9 Particle swarm optimization.mp459.3 MB
  107. 106. Appendix C. Chapter 10 Other swarm intelligence algorithms to explore.mp432.8 MB
  108. 107. Appendix C. Chapter 11 Supervised and unsupervised learning.mp420.4 MB
  109. 108. Appendix C. Chapter 12 Reinforcement learning.mp461.9 MB
  110. Bonus Resources.txt386 bytes

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