Peper / Isokawa / Umeo Natural Computing

4th International Workshop on Natural Computing, Himeji, Japan, September 2009, Proceedings
2010
ISBN: 978-4-431-53868-4
Verlag: Springer Japan
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2, 390 Seiten

Reihe: Proceedings in Information and Communications Technology

This book is the refereed proceedings of the Fourth International Workshop on Natural Computing, IWNC 2009, held in Himeji International Exchange Center, HIMEJI, JAPAN on September 2009. IWNC aims to bring together computer scientists, biologists, mathematicians, electronic engineers, physicists, and humanitarians, to critically assess present findings in the field, and to outline future developments in nature-inspired computing.

Autoren/Hrsg.

Isokawa, T.

Matsui, N.

Umeo, H.

Peper, F.

Peper, Ferdinand

Umeo, Hiroshi

Matsui, Nobuyuki

Isokawa, Teijiro

Inhaltsverzeichnis

1;Title Page;2
2;Preface;5
3;Organization;7
4;Table of Contents;9
5;Invited Talks;9
5.1;Investigating Universal Computability of Conventional Cellular Automata Problems on an Organic Molecular Matrix;13
5.1.1;Introduction;13
5.1.2;Universality and Computability;14
5.1.3;Molecular Cellular Automaton Rules;15
5.1.4;Dirichlet Tessellation (DT) or Voronoi Decomposition;16
5.1.5;Directed Propagation of Information;17
5.1.6;Information Writing, Storage and Retrieval;17
5.1.7;Logic Gate;19
5.1.8;Coalescence and Giant Explosion;19
5.1.9;Density Classification Task (DCT);20
5.1.10;Multi-agent Robotics;21
5.1.11;Phase Ordering;22
5.1.12;Ant Colony;22
5.1.13;Chemotaxis;23
5.1.14;References;24
5.2;Noise-Based Logic and Computing: From Boolean Logic Gates to Brain Circuitry and Its Possible Hardware Realization;25
5.3;Models and Mechanisms for Artificial Morphogenesis;35
5.3.1;Background;35
5.3.1.1;Embodied Computation;35
5.3.1.2;Artificial Morphogenesis;37
5.3.2;Requirements for a Formalism;38
5.3.3;Approach;39
5.3.4;Example;43
5.3.5;References;44
5.4;Biologically–Inspired Network Architecture for Future Networks;46
5.4.1;Introduction;46
5.4.2;Why Bio-inspired Approaches?;47
5.4.3;Attractor Selection Principle and Its Application to Networking Problems;50
5.4.4;Concluding Remarks;52
5.4.5;References;53
5.5;Foraging Behaviors and Potential Computational Ability of Problem-Solving in an Amoeba;54
5.5.1;Introduction;54
5.5.2;Foraging Behaviors in the Case of Spatially Distributed FSs;56
5.5.2.1;Accumulation of Mass of Organism to FS in Relation to Amount of Nutrient at FS;56
5.5.2.2;Time Course of Two Connections between Two FSs in Relation to Food Amount at FS;56
5.5.2.3;Order of Tube Collapse in Five Connections with Different Length between Two FSs;57
5.5.2.4;Maze-Solving;57
5.5.2.5;Risk-Minimum Path in the Inhomogeneous Field of Risk;59
5.5.2.6;Networking of Three and More FSs;59
5.5.2.7;Not Only the Final Answer But Also Transient;59
5.5.3;Mathematical Modeling for the Foraging Behaviors to Connect the Spatially Distributed FSs through a Smart Network;60
5.5.3.1;Mathematical Formulation of Network Dynamics Adaptable to Streaming;60
5.5.3.2;Physarum Solver: A Biologically Inspired Method for Path-Finding;62
5.5.3.3;Risk-Minimum Path in an Inhomogeneous Field;62
5.5.3.4;Effect of Food Amount on Tube Selection between Two FSs;63
5.5.4;Concluding Remarks: Possibility of Physarum Computing;64
5.5.5;References;65
5.6;Two Molecular Information Processing Systems Based on Catalytic Nucleic Acids;67
5.6.1;Introduction;67
5.6.2;Systems Based on Deoxyribozyme-Based Logic Gates;67
5.6.3;Spider-Based System;72
5.6.4;Conclusions;74
5.6.5;References;74
6;Invited Papers;9
6.1;The Effect of Community on Distributed Bio-inspired Service Composition;76
6.1.1;Introduction;76
6.1.2;Bio-inspired Service Management;77
6.1.2.1;Distributed Service Composition;77
6.1.3;Community;78
6.1.3.1;Community Structure and Formation;79
6.1.4;Simulation and Results;80
6.1.5;Conclusion;82
6.1.6;References;83
6.2;Efficient Computation in Brownian Cellular Automata;84
6.2.1;Introduction;84
6.2.2;A Brownian Cellular Automaton Model;85
6.2.3;Embedding Delay-Insensitive Circuits into Brownian Cellular Automaton;89
6.2.4;Implementing Ratchets with Various Configurations;91
6.2.5;Conclusions;92
6.2.6;References;92
6.3;A Molecular Communication System;94
6.3.1;Introduction;94
6.3.2;Overview of a Molecular Communication System;94
6.3.3;System Design and Initial Experimental Results;96
6.3.3.1;Molecular Communication Interface;96
6.3.3.2;Molecular Propagation System;97
6.3.3.3;Sender and Receiver;99
6.3.4;Conclusions;100
6.3.5;References;100
6.4;Properties of Threshold Coupled Chaotic Neuronal Maps;102
6.4.1;Introduction;102
6.4.2;The Model System: Neuronal Maps;103
6.4.2.1;Relaxation Time;104
6.4.2.2;Nonlocal Coupling;105
6.4.2.3;Asynchronous Updating;106
6.4.2.4;Dynamic Logic Cell;107
6.4.3;Conclusion;108
6.4.4;References;109
7;Contributed Papers;10
7.1;Implementation of Rotary Element with Quantum Cellular Automata;111
7.1.1;Introduction;111
7.1.2;RotaryElement;112
7.1.3;Quantum Cellular Automata;113
7.1.4;Implementation of Rotary Element;113
7.1.5;Delay Element and C-JOIN;116
7.1.6;Concluding Remarks;117
7.1.7;References;118
7.2;Universal 2-State Asynchronous Cellular Automaton with Inner-Independent Transitions;119
7.2.1;Introduction;119
7.2.2;On Delay Insensitive Circuits;120
7.2.3;Implementing DI-Circuits on Asynchronous CA;121
7.2.4;Conclusions and Discussion;126
7.2.5;References;126
7.3;Effect of Population Size in Extended Parameter-Free Genetic Algorithm;129
7.3.1;Introduction;129
7.3.2;Parameter-Free Genetic Algorithm;130
7.3.3;Mutation Rate Coding;131
7.3.4;Extended Selection Rule;132
7.3.5;Experiments;133
7.3.6;Conclusion and Discussion;135
7.3.7;References;136
7.4;Temperature Effects on Olive Fruit Fly Infestation in the FlySim Cellular Automata Model;137
7.4.1;Introduction;137
7.4.2;Description of the Model;138
7.4.3;Elementary Processes and Transition Function;139
7.4.3.1;Temperature;139
7.4.3.2;Growth and Death Rate of Olive Fruit Flies;140
7.4.3.3;Diffusion of Adult Olive Fruit Flies;141
7.4.4;Results;141
7.4.5;Conclusions;143
7.4.6;References;143
7.5;Computing by Observing Changes;145
7.5.1;Computing by Observing;145
7.5.2;Preliminaries;147
7.5.3;Computing by Observing Changes;148
7.5.4;The Power of Change-Observing Acceptors;150
7.5.5;Conclusion;151
7.5.6;References;152
7.6;Robustness of the Critical Behaviour in a Discrete Stochastic Reaction-Diffusion Medium;153
7.6.1;Introduction;153
7.6.2;Model and Methods;154
7.6.2.1;The Model;154
7.6.2.2;Effect of Noise;155
7.6.3;Robustness Studies;156
7.6.3.1;Robustness to Variations of the Excitation Level;156
7.6.3.2;Holes and Missing Links;156
7.6.3.3;Changing the Topology;157
7.6.4;The Inverse Proportionality Law;158
7.6.5;Conclusion;160
7.6.6;References;160
7.7;Quantifying the Severity of the Permutation Problem in Neuroevolution;161
7.7.1;Introduction;161
7.7.2;The Permutation Problem;162
7.7.3;Calculating the Probability of Genotypic Permutations;163
7.7.4;Calculating the Probability of Phenotypic Permutations;165
7.7.5;Discussion;166
7.7.6;Conclusions and Future Work;167
7.7.7;References;168
7.8;Extending the Geometrical Design of DNA Nanostructures;169
7.8.1;Introduction;169
7.8.2;Interconnected Single-Duplex Junction (T-Junction);170
7.8.2.1;Structural Description;170
7.8.2.2;Abstraction;171
7.8.3;Designing Shapes and Patterns;171
7.8.4;Experiments;175
7.8.5;Conclusion;175
7.8.6;References;176
7.9;An Optical Solution for the Subset Sum Problem;177
7.9.1;Introduction;177
7.9.2;The Device;178
7.9.2.1;The Modified Device;179
7.9.3;Finding the Solution Subset;183
7.9.4;Conclusion and Future Work;184
7.9.5;References;184
7.10;Design of True Random One-Time Pads in DNA XOR Cryptosystem;186
7.10.1;Introduction;186
7.10.2;DNA XOR One-Time-Pad Cryptosystem with Random Hybridization;187
7.10.2.1;Message Encryption;188
7.10.2.2;Random Tiling Operation;189
7.10.2.3;Extraction of Cipher Strings and Key Strings;190
7.10.3;Analysis on Error Tolerance;193
7.10.4;Conclusion;194
7.10.5;References;194
7.11;On Designing Gliders in Three-Dimensional Larger than Life Cellular Automata;196
7.11.1;Introduction;196
7.11.2;Larger than Life Cellular Automata;197
7.11.3;Bugs in Three-Dimensional Larger than Life;197
7.11.3.1;Period 1 Bugs;197
7.11.3.2;Bugs with Longer Period;199
7.11.4;Experimental Results of Collisions of Bugs;200
7.11.5;Conclusion;201
7.11.6;References;201
7.12;Instability of Collective Flow in Two-Dimensional Optimal Velocity Model;203
7.12.1;Introduction;203
7.12.2;Two-DimensionalOVModel;203
7.12.3;Linear Analysis;204
7.12.3.1;Longitudinal Mode along the x-Axis;206
7.12.3.2;Transverse Mode along the x-Axis;206
7.12.3.3;Transverse Mode along the y-Axis;207
7.12.3.4;Longitudinal Mode along the y-Axis;207
7.12.3.5;Elliptically Polarized Mode;208
7.12.4;Phase Diagrams;209
7.12.5;Summary;210
7.12.6;References;210
7.13;A New Differential Evolution for Multiobjective Optimization by Uniform Design and Minimum Reduce Hypervolume;211
7.13.1;Introduction;211
7.13.2;UniformDesign;213
7.13.3;Minimum Reduce Hypervolume;213
7.13.4;Differential Evolution for MOPs by Uniform Design and Minimum Reduce Hypervolume;214
7.13.5;Experiment Results;215
7.13.6;Conclusion and Further Research;219
7.13.7;References;219
7.14;Noise Effects on Chaos in Chaotic Neuron Model;221
7.14.1;Introduction;221
7.14.2;Chaotic Neuron Model;222
7.14.3;Noise-Induced Order in Chaotic Neuron Model;223
7.14.4;Summary;227
7.14.5;References;228
7.15;Application of Improved Grammatical Evolution to Santa Fe Trail Problems;230
7.15.1;Introduction;230
7.15.2;Original Grammatical Evolution;231
7.15.3;Function Identification Problem;231
7.15.3.1;Problem Settings;231
7.15.3.2;Syntax and Parameters;232
7.15.3.3;Result;232
7.15.4;Improved Schemes of Grammatical Evolution;233
7.15.4.1;Difficulties of Original GE;233
7.15.4.2;Improvement of GE;234
7.15.5;Santa Fe Trail Problem;234
7.15.5.1;Problem Setting;234
7.15.5.2;Syntax and Parameters;235
7.15.5.3;Result;236
7.15.6;Conclusion;236
7.15.7;References;237
7.16;Limit Theorem for a Time-Dependent Coined Quantum Walk on the Line;238
7.16.1;Introduction;238
7.16.2;Time-Dependent QW;239
7.16.3;Two-PeriodQW;241
7.16.4;Special Cases in Time-Dependent QWs;243
7.16.4.1;Case 1;243
7.16.4.2;Case 2;245
7.16.5;Conclusion and Discussion;246
7.16.6;References;246
7.17;Top-Predator Survivor Region Is Affected by Bottom-Prey Mortality Rate on the Monte-Carlo Simulation in Lattice Model;248
7.17.1;Introduction;248
7.17.2;Model and Method;249
7.17.2.1;Model;249
7.17.2.2;Simulation Method;250
7.17.3;Mean-Field Theory;250
7.17.4;Simulation Results;251
7.17.4.1;Steady-State Density;251
7.17.4.2;Results of Simulation Experiment;252
7.17.5;Discussions and Conclusion;253
7.17.6;References;255
7.18;Simulation and Theoretical Comparison between “Zipper” and “Non-Zipper” Merging;256
7.18.1;Introduction;256
7.18.2;Modeling;257
7.18.3;Simulations and Theoretical Calculation;258
7.18.4;Conclusive Discussion;263
7.18.5;References;263
7.19;Universality of 2-State 3-Symbol Reversible Logic Elements — A Direct Simulation Method of a Rotary Element;264
7.19.1;Introduction;264
7.19.2;Preliminaries;265
7.19.3;Direct Simulation of an RE by 3-Symbol RLEMs;267
7.19.4;Concluding Remarks;271
7.19.5;References;271
7.20;Pump Current as a Signal Transformation;272
7.20.1;Introduction: Pump Current;272
7.20.2;Stochastic Model;274
7.20.2.1;Simple Model for the Pumping Phenomenon;274
7.20.2.2;Counting Statistics;275
7.20.3;Mathematical Structure Behind the Pumping Phenomenon;276
7.20.3.1;Interpretation as a Shr¨odinger-Like Equation;276
7.20.3.2;Geometrical Phase Interpretation;277
7.20.4;Discussion: Pump Current as a Signal Transformation;278
7.20.5;Concluding Remarks;279
7.20.6;References;279
7.21;Evaluation of Generation Alternation Models in Evolutionary Robotics;280
7.21.1;Introduction;280
7.21.2;Generation Alternation Models;281
7.21.3;Evaluation of Generation Alternation Models by Experiment;283
7.21.3.1;Experimental Settings;283
7.21.3.2;Experimental Results;284
7.21.3.3;Discussion;285
7.21.4;Conclusions;287
7.21.5;References;287
7.22;Photonic Switching of DNA’s Position That Represents the Internal State in Photonic DNA Automaton;288
7.22.1;Introduction;288
7.22.2;Photonic DNA Automaton;290
7.22.3;A Method for Controlling Position of DNA;292
7.22.4;Experiments;295
7.22.5;Conclusions;299
7.22.6;References;299
7.23;Fluctuation Induced Structure in Chemical Reaction with Small Number of Molecules;302
7.24;Parallel Retrieval of Nanometer-Scale Light-Matter Interactions for Nanophotonic Systems;310
7.24.1;Introduction;310
7.24.2;Nanometric Optical Processing Based on Nanophotonics;312
7.24.3;Nanophotonic Matching Utilizing Macro-scale Observation of Optical Near-Field Interactions;312
7.24.4;Transcription Based on Photoinduced Phase Transition;316
7.24.5;Conclusions;318
7.24.6;References;318
7.25;A Compressible Fluid Model for Traffic Flow and Nonlinear Saturation of Perturbation Growth;320
7.25.1;Introduction;320
7.25.2;New Compressible Fluid Model Based on Experimental Data;322
7.25.2.1;Linear Stability Analysis;324
7.25.2.2;Reductive Perturbation Analysis;324
7.25.3;Conclusion;327
7.25.4;References;327
7.26;Functional Sized Population Magnetic Optimization Algorithm;328
7.26.1;Introduction;328
7.26.2;Functional Sized Population MOA (FSMOA);329
7.26.3;Finding the Best Parameters;333
7.26.4;Experimental Results;334
7.26.5;Conclusion;335
7.26.6;References;336
7.27;Emergence and Collapse of Order in Ad Hoc Cellular Automata;337
7.27.1;Introduction;337
7.27.2;Ad Hoc Cellular Automata with Intra-agent Dynamics;337
7.27.3;Results;339
7.27.3.1;Emergence and Collapse of Complex/Periodic Patterns;339
7.27.3.2;Emergence and Collapse of Clusters in Rule-Entry Space;341
7.27.3.3;Classifying ACA;342
7.27.3.4;Power Law in Stationary State Distribution;343
7.27.4;Discussion and Conclusion;343
7.27.5;References;344
7.28;A Transition Rule Set for the First 2-D Optimum-Time Synchronization Algorithm;345
7.28.1;Introduction;345
7.28.2;Firing Squad Synchronization Problem;346
7.28.2.1;FSSP on Two-Dimensional Cellular Arrays;346
7.28.2.2;Overview of Shinahr’s Time-Optimum Algorithm;346
7.28.3;Constructionof Real-Coded Transition Rule Set for ABS;348
7.28.4;Discussions;352
7.28.5;References;352
7.29;A Two-Dimensional Optimum-Time Firing Squad Synchronization Algorithm and Its Implementation;354
7.29.1;Introduction;354
7.29.2;Firing Squad Synchronization Problem on Two-Dimensional Arrays;354
7.29.3;Delayed Synchronization Scheme for One-Dimensional Arrays;355
7.29.4;New Optimum-Time Synchronization Algorithm;357
7.29.4.1;Segmentation of Rectangular Array of Size m× n;357
7.29.4.2;Starting Synchronization Process;357
7.29.4.3;Synchronization of Lm;359
7.29.4.4;Synchronization of Li;359
7.29.4.5;Synchronization of Rectangle Longer Than Wide;360
7.29.5;Conclusions;362
7.29.6;References;363
7.30;Quaternion Based Thermal Condition Monitoring System;364
7.30.1;Introduction;364
7.30.2;Machine Condition Monitoring System Model;365
7.30.3;Log-Polar Mapping;366
7.30.4;Quaternion Based Thermal Image Correlator;366
7.30.5;Max-Product Fuzzy Neural Network Classifier;368
7.30.6;Experimental Results;370
7.30.7;Conclusion;373
7.30.8;References;374
7.31;Firing Correlation in Spiking Neurons with Watts–Strogatz Rewiring;375
7.31.1;Introduction;375
7.31.2;Izhikevich’s Neuron Model and Network Structure;376
7.31.2.1;Izhikevich’s Spiking Neuron Model;376
7.31.2.2;Network Structure and Neuron Ensembles;376
7.31.3;Simulation Experiment with a WS Network Structure;379
7.31.4;Summary;381
7.31.5;References;383
7.32;Methods for Shortening Waiting Time in Walking-Distance Introduced Queueing Systems;384
7.32.1;Introduction;384
7.32.2;Walking-Distance Introduced Queueing Theory;385
7.32.2.1;Distance Introduced Fork-Type Queueing System: D-Fork;385
7.32.2.2;Update Rules;386
7.32.2.3;Stationary Equations;387
7.32.3;Methods for Shortening Mean Waiting Time in D-Fork;388
7.32.3.1;Set the Head of the Queue at the Center: D-Fork-Center;388
7.32.3.2;Keep One Person Waiting at the Window: D-Fork-Wait;388
7.32.4;Theoretical Analysis and Simulation;389
7.32.5;Experiments;390
7.32.6;Conclusion;391
7.32.7;References;391
7.33;Effect of Mutation to Distribution of Optimum Solution in Genetic Algorithm;392
7.33.1;Introduction;392
7.33.2;Mathematical Model;393
7.33.2.1;Representations;393
7.33.2.2;Linkage Equilibrium;393
7.33.3;Stochastic Models;394
7.33.3.1;Wright-FisherModel;394
7.33.3.2;With Mutation;394
7.33.4;Calculation of Success Probability;395
7.33.5;Results;396
7.33.6;Summary and Discussion;398
7.33.7;References;399
8;Author Index;400