优化算法，视频版
上次更新时间：2024-12-16
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课程内容
- 001. Part 1. Deterministic search algorithms (免费)
- 002. Chapter 1. Introduction to search and optimization (免费)
- 003. Chapter 1. Going from toy problems to the real world
- 004. Chapter 1. Basic ingredients of optimization problems
- 005. Chapter 1. Well-structured problems vs. ill-structured problems
- 006. Chapter 1. Search algorithms and the search dilemma
- 007. Chapter 1. Summary
- 008. Chapter 2. A deeper look at search and optimization
- 009. Chapter 2. Classifying search and optimization algorithms
- 010. Chapter 2. Heuristics and metaheuristics
- 011. Chapter 2. Nature-inspired algorithms
- 012. Chapter 2. Summary
- 013. Chapter 3. Blind search algorithms
- 014. Chapter 3. Graph search
- 015. Chapter 3. Graph traversal algorithms
- 016. Chapter 3. Shortest path algorithms
- 017. Chapter 3. Applying blind search to the routing problem
- 018. Chapter 3. Summary
- 019. Chapter 4. Informed search algorithms
- 020. Chapter 4. Minimum spanning tree algorithms
- 021. Chapter 4. Shortest path algorithms
- 022. Chapter 4. Applying informed search to a routing problem
- 023. Chapter 4. Summary
- 024. Part 2. Trajectory-based algorithms
- 025. Chapter 5. Simulated annealing
- 026. Chapter 5. The simulated annealing algorithm
- 027. Chapter 5. Function optimization
- 028. Chapter 5. Solving Sudoku
- 029. Chapter 5. Solving TSP
- 030. Chapter 5. Solving a delivery semi-truck routing problem
- 031. Chapter 5. Summary
- 032. Chapter 6. Tabu search
- 033. Chapter 6. Tabu search algorithm
- 034. Chapter 6. Solving constraint satisfaction problems
- 035. Chapter 6. Solving continuous problems
- 036. Chapter 6. Solving TSP and routing problems
- 037. Chapter 6. Assembly line balancing problem
- 038. Chapter 6. Summary
- 039. Part 3. Evolutionary computing algorithms
- 040. Chapter 7. Genetic algorithms
- 041. Chapter 7. Introducing evolutionary computation
- 042. Chapter 7. Genetic algorithm building blocks
- 043. Chapter 7. Implementing genetic algorithms in Python
- 044. Chapter 7. Summary
- 045. Chapter 8. Genetic algorithm variants
- 046. Chapter 8. Real-valued GA
- 047. Chapter 8. Permutation-based GA
- 048. Chapter 8. Multi-objective optimization
- 049. Chapter 8. Adaptive GA
- 050. Chapter 8. Solving the traveling salesman problem
- 051. Chapter 8. PID tuning problem
- 052. Chapter 8. Political districting problem
- 053. Chapter 8. Summary
- 054. Part 4. Swarm intelligence algorithms
- 055. Chapter 9. Particle swarm optimization
- 056. Chapter 9. Continuous PSO
- 057. Chapter 9. Binary PSO
- 058. Chapter 9. Permutation-based PSO
- 059. Chapter 9. Adaptive PSO
- 060. Chapter 9. Solving the traveling salesman problem
- 061. Chapter 9. Neural network training using PSO
- 062. Chapter 9. Summary
- 063. Chapter 10. Other swarm intelligence algorithms to explore
- 064. Chapter 10. ACO metaheuristics
- 065. Chapter 10. ACO variants
- 066. Chapter 10. From hive to optimization
- 067. Chapter 10. Exploring the artificial bee colony algorithm
- 068. Chapter 10. Summary
- 069. Part 5. Machine learning-based methods
- 070. Chapter 11. Supervised and unsupervised learning
- 071. Chapter 11. Demystifying machine learning
- 072. Chapter 11. Machine learning with graphs
- 073. Chapter 11. Self-organizing maps
- 074. Chapter 11. Machine learning for optimization problems
- 075. Chapter 11. Solving function optimization using supervised machine learning
- 076. Chapter 11. Solving TSP using supervised graph machine learning
- 077. Chapter 11. Solving TSP using unsupervised machine learning
- 078. Chapter 11. Finding a convex hull
- 079. Chapter 11. Summary
- 080. Chapter 12. Reinforcement learning
- 081. Chapter 12. Optimization with reinforcement learning
- 082. Chapter 12. Balancing CartPole using A2C and PPO
- 083. Chapter 12. Autonomous coordination in mobile networks using PPO
- 084. Chapter 12. Solving the truck selection problem using contextual bandits
- 085. Chapter 12. Journey s end A final reflection
- 086. Chapter 12. Summary
- 087. Appendix A. Search and optimization libraries in Python
- 088. Appendix A. Mathematical programming solvers
- 089. Appendix A. Graph and mapping libraries
- 090. Appendix A. Metaheuristics optimization libraries
- 091. Appendix A. Machine learning libraries
- 092. Appendix A. Projects
- 093. Appendix B. Benchmarks and datasets
- 094. Appendix B. Combinatorial optimization benchmark datasets
- 095. Appendix B. Geospatial datasets
- 096. Appendix B. Machine learning datasets
- 097. Appendix B. Data folder
- 098. Appendix C. Exercises and solutions
- 099. Appendix C. Chapter 3 Blind search algorithms
- 100. Appendix C. Chapter 4 Informed search algorithms
- 101. Appendix C. Chapter 5 Simulated annealing
- 102. Appendix C. Chapter 6 Tabu search
- 103. Appendix C. Chapter 7 Genetic algorithm
- 104. Appendix C. Chapter 8 Genetic algorithm variants
- 105. Appendix C. Chapter 9 Particle swarm optimization
- 106. Appendix C. Chapter 10 Other swarm intelligence algorithms to explore
- 107. Appendix C. Chapter 11 Supervised and unsupervised learning
- 108. Appendix C. Chapter 12 Reinforcement learning
课程内容
108个讲座
- 001. Part 1. Deterministic search algorithms (免费)
- 002. Chapter 1. Introduction to search and optimization (免费)
- 003. Chapter 1. Going from toy problems to the real world
- 004. Chapter 1. Basic ingredients of optimization problems
- 005. Chapter 1. Well-structured problems vs. ill-structured problems
- 006. Chapter 1. Search algorithms and the search dilemma
- 007. Chapter 1. Summary
- 008. Chapter 2. A deeper look at search and optimization
- 009. Chapter 2. Classifying search and optimization algorithms
- 010. Chapter 2. Heuristics and metaheuristics
- 011. Chapter 2. Nature-inspired algorithms
- 012. Chapter 2. Summary
- 013. Chapter 3. Blind search algorithms
- 014. Chapter 3. Graph search
- 015. Chapter 3. Graph traversal algorithms
- 016. Chapter 3. Shortest path algorithms
- 017. Chapter 3. Applying blind search to the routing problem
- 018. Chapter 3. Summary
- 019. Chapter 4. Informed search algorithms
- 020. Chapter 4. Minimum spanning tree algorithms
- 021. Chapter 4. Shortest path algorithms
- 022. Chapter 4. Applying informed search to a routing problem
- 023. Chapter 4. Summary
- 024. Part 2. Trajectory-based algorithms
- 025. Chapter 5. Simulated annealing
- 026. Chapter 5. The simulated annealing algorithm
- 027. Chapter 5. Function optimization
- 028. Chapter 5. Solving Sudoku
- 029. Chapter 5. Solving TSP
- 030. Chapter 5. Solving a delivery semi-truck routing problem
- 031. Chapter 5. Summary
- 032. Chapter 6. Tabu search
- 033. Chapter 6. Tabu search algorithm
- 034. Chapter 6. Solving constraint satisfaction problems
- 035. Chapter 6. Solving continuous problems
- 036. Chapter 6. Solving TSP and routing problems
- 037. Chapter 6. Assembly line balancing problem
- 038. Chapter 6. Summary
- 039. Part 3. Evolutionary computing algorithms
- 040. Chapter 7. Genetic algorithms
- 041. Chapter 7. Introducing evolutionary computation
- 042. Chapter 7. Genetic algorithm building blocks
- 043. Chapter 7. Implementing genetic algorithms in Python
- 044. Chapter 7. Summary
- 045. Chapter 8. Genetic algorithm variants
- 046. Chapter 8. Real-valued GA
- 047. Chapter 8. Permutation-based GA
- 048. Chapter 8. Multi-objective optimization
- 049. Chapter 8. Adaptive GA
- 050. Chapter 8. Solving the traveling salesman problem
- 051. Chapter 8. PID tuning problem
- 052. Chapter 8. Political districting problem
- 053. Chapter 8. Summary
- 054. Part 4. Swarm intelligence algorithms
- 055. Chapter 9. Particle swarm optimization
- 056. Chapter 9. Continuous PSO
- 057. Chapter 9. Binary PSO
- 058. Chapter 9. Permutation-based PSO
- 059. Chapter 9. Adaptive PSO
- 060. Chapter 9. Solving the traveling salesman problem
- 061. Chapter 9. Neural network training using PSO
- 062. Chapter 9. Summary
- 063. Chapter 10. Other swarm intelligence algorithms to explore
- 064. Chapter 10. ACO metaheuristics
- 065. Chapter 10. ACO variants
- 066. Chapter 10. From hive to optimization
- 067. Chapter 10. Exploring the artificial bee colony algorithm
- 068. Chapter 10. Summary
- 069. Part 5. Machine learning-based methods
- 070. Chapter 11. Supervised and unsupervised learning
- 071. Chapter 11. Demystifying machine learning
- 072. Chapter 11. Machine learning with graphs
- 073. Chapter 11. Self-organizing maps
- 074. Chapter 11. Machine learning for optimization problems
- 075. Chapter 11. Solving function optimization using supervised machine learning
- 076. Chapter 11. Solving TSP using supervised graph machine learning
- 077. Chapter 11. Solving TSP using unsupervised machine learning
- 078. Chapter 11. Finding a convex hull
- 079. Chapter 11. Summary
- 080. Chapter 12. Reinforcement learning
- 081. Chapter 12. Optimization with reinforcement learning
- 082. Chapter 12. Balancing CartPole using A2C and PPO
- 083. Chapter 12. Autonomous coordination in mobile networks using PPO
- 084. Chapter 12. Solving the truck selection problem using contextual bandits
- 085. Chapter 12. Journey s end A final reflection
- 086. Chapter 12. Summary
- 087. Appendix A. Search and optimization libraries in Python
- 088. Appendix A. Mathematical programming solvers
- 089. Appendix A. Graph and mapping libraries
- 090. Appendix A. Metaheuristics optimization libraries
- 091. Appendix A. Machine learning libraries
- 092. Appendix A. Projects
- 093. Appendix B. Benchmarks and datasets
- 094. Appendix B. Combinatorial optimization benchmark datasets
- 095. Appendix B. Geospatial datasets
- 096. Appendix B. Machine learning datasets
- 097. Appendix B. Data folder
- 098. Appendix C. Exercises and solutions
- 099. Appendix C. Chapter 3 Blind search algorithms
- 100. Appendix C. Chapter 4 Informed search algorithms
- 101. Appendix C. Chapter 5 Simulated annealing
- 102. Appendix C. Chapter 6 Tabu search
- 103. Appendix C. Chapter 7 Genetic algorithm
- 104. Appendix C. Chapter 8 Genetic algorithm variants
- 105. Appendix C. Chapter 9 Particle swarm optimization
- 106. Appendix C. Chapter 10 Other swarm intelligence algorithms to explore
- 107. Appendix C. Chapter 11 Supervised and unsupervised learning
- 108. Appendix C. Chapter 12 Reinforcement learning