Contents -- Preface -- 1 . Recreating Large-Scale Evolutionary Phenomena P.-M. Agapow -- 1.1 Introduction -- 1.2 A framework for recreating evolution -- 1.2.1 Simulation -- 1.2.2 Data analysis and manipulation -- 1.2.3 Practical issues -- 1.3 Life out of balance -- 1.3.1 Key innovations -- 1.3.2 Methods -- 1.4 Discussion -- Acknowledgement -- 2 . Neural Evolution for Collision Detection & Resolution in a 2D Free Flight Environment S . Alam. M . McPartland. M . Barlow. P . Lindsay. and H . A . Abbass -- 2.1 Background -- 2.2 Modelling the Problem -- 2.2.1 Collision Detection -- 2.3 The Neural Network Structure -- 2.4 Preliminary Experimental Setup -- 2.4.1 Preliminary Results -- 2.5 Main Experiment Setup -- 2.6 Fitness Function -- 2.7 Main Results and Analysis -- 2.8 Conclusion & Future Work -- Acknowledgements -- 3 . Cooperative Coevolution of Genotype-Phenotype Mappings to Solve Epistatic Optimization Problems L . T . Bui. H . A . Abbass, and D . Essam -- 3.1 Introduction -- 3.2 The use of co-evolution for GPM -- 3.3 The proposed algorithm -- 3.4 A comparative study -- 3.4.1 Testing scenario -- 3.4.2 Results -- 3.4.3 Fitness landscape analysis -- 3.5 Conclusion -- Acknowledgement -- 4 . Approaching Perfect Mixing in a Simple Model of the Spread of an Infectious Disease D . Chu and J . Rowe -- 4.1 Introduction -- 4.2 Description of the Model -- 4.3 Behavior of the Model in the Perfect Mixing Case -- 4.4 Beyond perfect Mixing -- 4.4.1 No Movement: The Static Case -- 4.4.2 In Between -- 4.5 Discussion -- 4.6 Conclusion & Future Work -- 5 . The Formation of Hierarchical Structures in a Pseudo- Spatial Co-Evolutionary Artificial Life Environment D . Cornforth. D . G . Green and J . Awburn -- 5.1 Introduction -- 5.2 Themodel -- 5.2.1 Genotype to phenotype mapping -- 5.2.2 Selection mechanism -- 5.2.3 Reproduction and genetic operators.

5.2.4 Memetic evolution -- 5.2.5 Global parameters -- 5.3 Experiments -- 5.4 Results -- 5.5 Discussion -- Acknowledgements -- 6 . Perturbation Analysis: A Complex Systems Pattern N . Geard. K . Willadsen and J . Wiles -- 6.1 Motivation -- 6.2 Applicability -- 6.3 Structure -- 6.4 Participants -- 6.5 Collaborations -- 6.6 Consequences -- 6.7 Implementation -- 6.8 Sample code -- Boolean network attractor stability -- Lyapunov characteristic exponents -- 6.9 Known uses -- 6.10 Summary -- 6.11 Acknowledgements -- 7 . A Simple Genetic Algorithm for Studies of Mendelian Populations C . Gondro and J.C.M. Magalhaes -- 7.1 Introduction -- 7.2 Genetic Algorithms -- 7.2.1 Search operators -- 7.3 Conceptual Model of Mendelian Populations -- 7.3.1 Virtual organisms as a simple genetic algorithm -- 7.4 Hardy-Weinberg Equilibrium in a Virtual Population -- 7.5 Conclusions and Future Work -- Acknowledgement -- 8 . Roles of Rule-Priority Evolution in Animat Models K.A. Hawick, H.A. James and C.J. Scogings -- 8.1 Introduction -- 8.2 Rule-Based Model -- 8.2.1 Our Predator-Prey Model -- 8.3 Resultant Behaviours from Prioritisation -- 8.4 Behavioural Metrics and Analysis -- 8.5 An Evolutionary Survival Experiment -- 8.5.1 Evolution Procedure -- 8.5.2 Survivability -- 8.6 Generalising the Approach -- 8.7 Conclusions -- Acknowledgements -- 9 . Gauging ALife: Emerging Complex Systems K . Kitto -- 9.1 Life and ALife -- 9.1.1 Development -- 9.1.2 ALife and Emergence -- Object Complexity: -- Simulation Framework: -- 9.1.3 Complexity and Contextuality -- 9.2 Incorporating Context into our Models -- 9.2.1 The Baas Definition of Emergence -- 9.2.2 Quantum Mechanics -- 9.2.3 Gauge Theories -- 9.3 The Recursive Gauge Principle (RGP) -- 9.3.1 Cellular Automata and Solitons -- 9.3.2 BCS Superconductivity and Nambu-Goldstone modes -- 9.3.3 Returning to Development.

10 . Localisation of Critical Transition Phenomena in Cellular Automata Rule-Space A . Lafusa and T . Bossomaier -- 10.1 Introduction -- 10.2 Automatic classify rules with input-entropy -- 10.3 Parameterisation of cellular automata rule-space -- 10.4 Experimental determination of the edge-of-chaos -- 10.5 Definition of a unique critical parameter -- 10.6 Conclusions -- 11 . Issues in the Scalability of Gate-Level Morphogenetic Evolvable Hardware J . Lee and J . Sitte -- 11.1 Introduction -- 11.2 Scaling with Morphogenesis -- 11.3 Evolving One Bit Full Adders -- 11.3.1 Experimental Setup -- 11.3.2 LUT Encoding -- 11.3.3 Fitness Evaluation -- 11.3.4 Experiment Results -- 11.3.5 Further Experiments -- 11.4 Analysing Problem Difficulty -- 11.4.1 Experiment Difficulty Comparisons -- 11.5 Conclusion -- 12 . Phenotype Diversity Objectives for Graph Grammar Evolution M . H . Luerssen -- 12.1 Introduction -- 12.2 Background -- 12.2.1 Evolution and Development -- 12.2.2 Graph Ontogenesis -- 12.2.3 Evolving a Graph Grammar -- 12.2.4 Diversity Objectives -- 12.3 Experiments -- 12.3.1 Measures of Phenotype Diversity -- 12.3.2 Evaluation -- 12.3.3 Results and Discussion -- 12.4 Conclusions -- 13. An ALife Investigation on the Origins of Dimorphic Parental Investments S . Mascaro. K . B . Korb and A . E . Nicholson -- 13.1 Introduction -- 13.2 ALife Simulation -- Environment. -- Agents. -- Parental investment. -- Statistics. -- 13.3 Prior investment hypothesis -- Method. -- Results. -- 13.4 Desertion hypothesis -- Method. -- Results. -- 13.5 Paternal uncertainty hypothesis -- Method. -- Results. -- 13.6 Association hypothesis -- Method. -- Results. -- 13.7 Chance dimorphism hypothesis -- Method. -- Results. -- 13.8 Conclusion -- 14. Local Structure and Stability of Model and Real World Ecosystems D . Newth. and D . Cornforth -- 14.1 Introduction.

14.2 Ecological stability and patterns of interaction -- 14.2.1 Community Stability -- 14.2.2 Local patterns of interaction -- 14.3 Experiments -- 14.3.1 Stability properties of motifs -- 14.3.2 Motif frequency -- 14.3.3 Community food web data -- 14.4 Results -- 14.4.1 Stability properties of motifs -- 14.4.2 Stability and occurrence of three node motifs -- 14.4.3 Stability and occurrence of four node motifs -- 14.5 Discussion -- 14.6 Closing comments -- Acknowledgements -- 15 . Quantification of Emergent Behaviors Induced by Feedback Resonance of Chaos A . Patti. M . Lungarella. and Y . Kuniyoshi -- 15.1 Introduction -- 15.2 Model System -- 15.2.1 Dynamical Systems Approach -- 15.2.2 Feedback Resonance -- 15.2.3 Coupled Chaotic Field -- 15.3 Methods -- 15.4 Experiments -- 15.5 Analysis -- 15.5.1 Analysis 1: Body movements -- 15.5.2 Analysis 2: Neural coupling -- 15.6 Discussion and Conclusion -- 15.7 Acknowledgements -- 16 . A Dynamic Optimisation Approach for Ant Colony Optimisation Using the Multidimensional Knapsack Problem M . Randall -- 16.1 Introduction -- 16.2 Adapting ACO to Dynamic Problems -- 16.2.1 Overview -- 16.2.2 The Solution Deconstruction Process -- 16.2.2.1 Event Descriptors -- 16.3 Computational Experience -- 16.4 Conclusions -- 17 . Maintaining Explicit Diversity Within Individual Ant Colonies M . Randall -- 17.1 Introduction -- 17.2 Ant Colony System -- 17.3 Explicit Diversification Strategies for ACO -- 17.4 Maintaining Intra-Colony Diversity -- 17.5 Computational Experience -- 17.5.1 Experimental Design -- Stage 1: -- Stage 2: -- 17.5.2 Implementation Details -- 17.5.3 Problem Instances -- 17.5.4 Results -- 17.6 Conclusions -- 18 . Evolving Gene Regulatory Networks for Cellular Morphogenesis T . Rudge and N . Geard -- 18.1 Introduction -- 18.2 Background -- 18.2.1 Leaf Morphogenesis -- 18.2.2 Previous Models.

18.3 The Simulation Framework -- 18.3.1 The Genetic Component -- 18.3.2 The Cellular Component -- Cell: -- Spatio-Mechanical Model: -- 18.3.3 Genotype-Phenotype Coupling -- 18.3.4 The Evolutionary Component -- 18.4 Initial Experiments -- 18.4.1 Method -- DRGN Coupling: -- Fitness function: -- Summary: -- 18.4.2 Results -- 18.5 Discussion and Future Directions -- Acknowledgments -- 19 . Complexity of Networks R . K . Standish -- 19.1 Introduction -- 19.2 Representation Language -- 19.3 Computing w -- 19.4 Compressed complexity and Offdiagonal complexity -- 19.5 Conclusion -- Acknowledgements -- 20 . A Generalised Technique for Building 2D Structures with Robot Swarms R.L. Stewart and R.A. Russell -- 20.1 Introduction -- 20.2 Background Information -- 20.3 A New Technique for Creating Spatio-temporal Varying Templates -- 20.3.1 Calibration -- 20.3.2 Experimental Procedure -- 20.3.3 Building a Radial Wall With and Without a Gap -- 20.4 Solving the Generalised 2D Collective Construction Problem -- 20.4.1 Experimental Procedure -- 20.4.2 Building Structures of Greater Complexity -- 20.5 General Discussion -- 20.6 Conclusion -- Acknowledgement -- 21 . H-ABC: A Scalable Dynamic Routing Algorithm B . Tatomir and L . J.M. Rothkrantz -- 21.1 Introduction -- 21.2 The Hierarchical Ant Based Control algorithm -- 21.2.1 Network model -- 21.2.2 Local ants -- 21.2.3 Backward ants -- 21.2.4 Exploring ants -- 21.2.5 Data packets -- 21.2.6 Flag packets -- 21.3 Simulation environment -- 21.4 Test and results -- 21.4.1 Low traffic load -- 21.4.2 High traffic load -- 21.4.3 Hot spot -- 21.4.4 Overhead -- 21.5 Conclusions and future work -- 22 . Describing DNA Automata Using an Artificial Chemistry Based on Pattern Matching and Recombination T . Watanabe. K . Kobayashi. M . Nakamura. K . Kishi. M . Kazuno and K . Tominaga -- 22.1 Introduction.

22.2 An Artificial Chemistry for Stacks of Character Strings.