Cultural Algorithms. Robert G. Reynolds

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      1 Chapter 1Figure 1.1 Cultural Algorithm framework.Figure 1.2 An example of Maxwell's demon in action. The demon selectively le...

      2 Chapter 2Figure 2.1 A sample of the CAT System displaying a visualization of the Cone...Figure 2.2 The CAT system's user interface panel.Figure 2.3 The fitness of ConesWorld visualized, with the height at any give...Figure 2.4 A small ConesWorld topography update of position only.Figure 2.5 The Knowledge Source fitness of a ConesWorld simulation undergoin...Figure 2.6 ConesWorld KS Fitness with a regular topographical update at ever...Figure 2.7 The CAT System Logistics Function.Figure 2.8 A three‐dimensional visualization of successive iterations of the...Figure 2.9 The initialization stage of the static (above) and dynamic (below...Figure 2.10 Five steps of the static (left) and dynamic (right) landscape ne...Figure 2.11 The homogeneous topologies. (a) Ring topology, (b) square topolo...Figure 2.12 Five steps of the static (left) and dynamic (right) bounding box...Figure 2.13 The KS fitnesses for the static (above) and dynamic (below) land...Figure 2.14 The span of each Knowledge Source's bounding boxes.Figure 2.15 The tension spring [1].Figure 2.16 The Knowledge Source fitnesses of the tension spring problem.

      3 Chapter 3Figure 3.1 The cultural algorithm framework.Figure 3.2 Spectrum of knowledge distribution mechanisms.Figure 3.3 Cultural algorithm pseudocode.Figure 3.4 Updating the five knowledge source categories.Figure 3.5 The social fabric influence function: each of the knowledge sourc...Figure 3.6 Subcultured wheel selection process.Figure 3.7 Auction process phase 1: constructing the bidding wheels.Figure 3.8 Auction process phase 2: selecting KS for participation.Figure 3.9 Auction process phase 3: conducting the auction.Figure 3.10 Auction process phase 4: assigning influence.Figure 3.11 The cultural engine.Figure 3.12 An example landscape in two‐dimensional space (n = 2) bound by xFigure 3.13 Logistic function with characteristic A values.

      4 Chapter 4Figure 4.1 Cultural Algorithm framework [2].Figure 4.2 The big picture for all Knowledge Distribution Mechanisms that ut...Figure 4.3 Integration of multiple KSs [9].Figure 4.4 Weighted Majority win in belief space through the social network ...Figure 4.5 Conducting the Auction [13].Figure 4.6 Big picture of CAT4 Algorithms.Figure 4.7 An Example Landscape in two‐dimensional space (n = 2) bound by x ...Figure 4.8 The value for Y (on the Y‐axis as a function of A (z axis) over t...Figure 4.9 CAT4 versus CAT2 standard deviation comparison.Figure 4.10 CAT4 Regression line over 50 runs for complexity, A = 1.0.Figure 4.11 CAT2 Regression line over 50 runs for complexity, A = 1.0.Figure 4.12 CAT4 Regression line over 50 runs for complexity, A = 3.35.Figure 4.13 CAT2 Regression line over 50 runs for complexity, A = 3.35.Figure 4.14 CAT4 Regression line over 50 runs for complexity, A = 3.99.Figure 4.15 CAT2 Regression line over 50 runs for complexity, A = 3.99.

      5 Chapter 5Figure 5.1 Cultural Algorithm framework.Figure 5.2 Population network topologies.Figure 5.3 Spectrum of Knowledge Distribution mechanisms.Figure 5.4 Weighted Majority (WTD) Knowledge Distribution.Figure 5.5 Pseudocode for a general game mechanism for Knowledge Distributio...Figure 5.6 Forces guiding cooperation and defection component terms.Figure 5.7 The “hand” played by each player in IPD is set of the Degree of C...Figure 5.8 ConesWorld Landscape.Figure 5.9 KS distribution in a hexagonally networked population (a) initial...Figure 5.10 Sample processed image (base image: Udacity).Figure 5.11 Image processing, masking, and edge detection.Figure 5.12 Before and after optimization (base image: Udacity).

      6 Chapter 6Figure 6.1 Cultural Algorithm Framework.Figure 6.2 Coauthorship Social Network: Personal network of Dr. Ziad Kobti o...Figure 6.3 The experts' network and three possible teams for the project/pap...Figure 6.4 The representation of teams for the project/paper, which requires...Figure 6.5 Comparison of the algorithms using the sum of distances for vario...Figure 6.6 Comparison of the algorithms on 50k nodes network with different ...Figure 6.7 Runtimes of the algorithms with different numbers of required ski...Figure 6.8 Social circle of Palliative care.Figure 6.9 Palliative Care Social Network: The framework is to visualize the...Figure 6.10 The representation of teams for the whole patients of a palliati...Figure 6.11 Comparing result of CA with various other algorithms for finding...

      7 Chapter 7Figure 7.1 2D soccer simulation test bed.Figure 7.2 FSM for any player in the simulator.Figure 7.3 Pseudocode of basic CA.Figure 7.4 Enhanced defense through interposing the opposing player in posse...Figure 7.5 Average results for fitness value/generations.Figure 7.6 Evolution of values defining the fitness function.Figure 7.7 The average, maximum, and minimum number of goals that were recor...Figure 7.8 The average, maximum, and minimum number of goals that were recor...Figure 7.9 The average, maximum, and minimum number of goals that were recor...Figure 7.10 The average, maximum, and minimum number of goals that were reco...Figure 7.11 Difference in scoring between the different pairs of experiments...

      8 Chapter 8Figure 8.1 The Three Levels of Data Analysis.Figure 8.2 Basic Pseudocode for Cultural Algorithm [2].Figure 8.3 Schemata of Cultural Algorithms.Figure 8.4 CAPSO Pseudocode.Figure 8.5 A Decision Tree of the sample tour using HD/MRE (0/100).Figure 8.6 Pareto Front for Payout (Goal 1) and Effort (Goal 2).Figure 8.7 The Pareto Front for Payout vs. Effort for the Full week in Phase...Figure СКАЧАТЬ