Cultural Algorithms. Robert G. Reynolds
Function Dataset and Observation Summary and Conclusion References Notes
12 7 Evolving Emergent Team Strategies in Robotic Soccer using Enhanced Cultural Algorithms Introduction Related Work The 2D Soccer Simulation Test Bed Evolution of Team Strategies via Cultural Algorithm Experiments and Analysis of Results Conclusion References
13 8 The Use of Cultural Algorithms to Learn the Impact of Climate on Local Fishing Behavior in Cerro Azul, Peru Introduction An Overview of the Cerro Azul Fishing Dataset Data Mining at the Macro, Meso, and Micro Levels Cultural Algorithms and Multiobjective Optimization The Artisanal Fishing Model The Experimental Results Statistical Validation Conclusions and Future Work References Note
14 9 CAPSO: A Parallelized Multiobjective Cultural Algorithm Particle Swarm Optimizer Introduction Multiobjective Optimization Cultural Algorithms CAPSO Knowledge Structures Tracking Knowledge Source Progress (Other than Topographic) CAPSO Algorithm Pseudocode Multiple Runs Comparison of Benchmark Problems Overall Summary of Results Other Applications References Note
15 10 Exploring Virtual Worlds with Cultural Algorithms Archaeological Challenges Generalized Framework The Land Bridge Hypothesis Origin and Form Putting Data to Work Pathfinding and Planning Identifying Good Locations: The Hotspot Finder Cultural Algorithms Cultural Algorithm Mechanisms The Composition of the Belief Space Future Work Path Planning Strategy Local Tactics Detailed Locational Information Extending the CA Human Presence in the Virtual World Increasing the Complexity Updated Path‐Planning Results in Unity The Fully Rendered Land Bridge Pathfinder Mechanisms Results Conclusions References Note
16 Index
List of Tables
1 Chapter 3Table 3.1 Performance comparison for A = 1.01.Table 3.2 Performance comparison for A = 2.0.Table 3.3 Performance comparison for A = 3.3.Table 3.4 Performance comparison for A = 3.35.Table 3.5 Performance comparison for A = 3.4.Table 3.6 Performance comparison for A = 3.5.Table 3.7 Performance comparison for A = 3.99.
2 Chapter 4Table 4.1 Experimental framework parameters.Table 4.2 Comparing CAT4 and CAT2 regression through differentA‐value complex...
3 Chapter 5Table 5.1 Example of KS association structure for an individual in Game‐based...Table 5.2 Component terms of Degree of Cooperation score.Table 5.3 Mean generations to solution: CATGAME versus WTD Majority.Table 5.4 CATGAME versus WTD on Computer Vision Problem.
4 Chapter 6Table 6.1 The result of the various ratio of patient to care provider and wha...
5 Chapter 7Table 7.1 Performance of the algorithm against all other teams.Table 7.2 Sample encoding for an arbitrary individual.
6 Chapter 8Table 8.1 Data dictionary for the Peru database.Table 8.2 Details about the Decision Tree of the sample run in Figure 8.5.Table 8.3 Critical value for F‐test at different significant levels of alpha....Table