CONTENTS -- Preface R. Serra, M. Villani, I. Poli -- PART I: INVITED PAPERS -- Cognitive dynamics in an automata gas A. Bazzani, B. Giorgini, F. Zanlungo, S. Rambaldi -- 1. Introduction -- 2. The Automata Gas Model -- 3. Statistical Properties -- 4. Experimental Validation Problems and Virtual Experiments -- 5. Conclusions -- References -- Gene-environment interaction: the importance of omics in understanding the effect of low-dose exposure A. Colacci, P. Silingardi, M.G. Mascolo, E. Morandi, M. Vaccari -- 1. Introduction -- 2. The "exciting" hormesis theory and the low-dose toxicology -- 3. The promise and dejection of soy derivatives: when low doses are not beneficial -- 3.1. Phytoestrogens -- 3.2. The alternative tests to evaluate the risk of low-dose exposure -- 3.3. The toxicological profile of genistein in in vitro tests -- 4. Gene-environment interactions: towards a personalized exposure assessment -- 5. Conclusion -- 6. Acknowledgements -- References -- Diffusion of Shapes R.S. Shaw, N.H. Packard -- 1. Introduction -- 2. Membranes and rectification -- 3. Geometrical binding -- 4. Temperature dependence -- 5. Computation -- References -- PART II: CLASSIFICATION AND OPTIMIZATION -- Classification of colon tumor tissues using genetic programming F. Archetti, M. Castelli, I. Giordani, L. Vanneschi -- 1. Introduction -- 2. State of Art -- 3. Material and Methods -- 3.1. Dataset -- 3.2. Classification methods -- 3.2.1. Genetic programming for classification -- 3.2.2. Other machine learning methods -- 4. Experimental Results -- 4.1. The best solution found by GP -- 5. Conclusions and Future Work -- References -- FDC-based particle swarm optimization A. Azzini, S. Cagnoni, L. Vanneschi -- 1. Introduction -- 2. Previous and Related Work -- 2.1. Particle Swarm Optimization and its Variants -- 2.2. Fitness-Distance Correlation.

3. Integration of Fitness-Distance Correlation into PSO -- 4. Experimental Results -- 5. Conclusions -- References -- A clonal selection algorithm for the automatic synthesis of low-pass filters P. Conca, G. Nicosia, G. Stracquadanio -- 1. Introduction -- 2. Passive Filters Circuits -- 3. Immune Programming for Analog Circuit Design -- 3.1. Mutation operators -- 3.2. Fitness function -- 4. Experimental Results -- 5. Conclusions and Future Works -- References -- An evolutionary predictive approach to design high dimensional experiments D. De March, M. Forlin, D. Slanzi, I. Poli -- Abstract -- 1. Introduction -- 2. The predictive approach to design the experiments -- 3. A simulation study -- 4. Conclusion -- Acknowledgements -- References -- Particle swarm for pattern matching in image analysis L. Mussi, S. Cagnoni -- 1. Introduction -- 2. PM-PSO -- 2.1. Swarm "Training" -- 2.2. Fitness Function -- 2.3. Modified Velocity Equation for PM-PSO -- 2.3.1. Constriction Factor χ -- 2.3.2. Personal Best Position -- 2.3.3. Neighborhood Barycenter -- 3. Results -- 4. Conclusions and Future Directions -- References -- Admissible method for improved genetic search in cellular automata model (AMMISCA): a strategy in genetic calibration - preliminary results R. Um -- 1. Introduction -- 2. The SCIARA-R7 model -- 3. AMMISCA in detail -- 4. AMMISCA results -- 5. Conclusions and future developments -- Acknowledgments -- References -- PART III: COGNITION -- Evolving neural word sense disambiguation classifiers with a letter-count distributed encoding A. Azzini, C. Da Costa Pereira, M. Dragoni, A.G.B. Tettamanzi -- 1. Introduction -- 2. The Neuro Evolutionary Algorithm -- 2.1. Evolutionary Encoding -- 2.2. Fitness Function -- 2.3. Genetic Operators -- 3. Application to Word Sense Disambiguation -- 3.1. Dataset -- 4. Experiments and Results.

5. Discussion and Concluding Remarks -- References -- Prefrontal cortex and action sequences: a review on neural computational models I. Gaudiello, M.T. Liuzza, D. Caligiore -- 1. Introduction -- 2. The role of PFC and its functional organization -- 2.1. Involvement of PFC on hierarchical organization of behaviour and cross-modality integration -- 3. Models of PFC functioning -- 3.1. Main characteristics of PFC models -- 3.2. PFC models and action sequences -- 4. Discussion -- References -- A Neural-Network Model of the Dynamics of Hunger, Learning, and Action Vigor in Mice A. Venditti, M. Mirolli, D. Parisi, G. Baldassarre -- 1. Introduction -- 2. Target experiments -- 3. The model -- 3.1. The task -- 3.2. The actor-critic component of the model -- 3.3. The dynamics of costs, hunger, and perceived rewards -- 4. Results -- 5. Conclusion -- Acknowledgments -- References -- Bio-inspired ICT for evolutionary emotional intelligence M. Villamira, P. Cipresso -- 1. Evolutionary history of emotions -- 1.1. Living systems -- 1.2. Emotions -- 1.3. Emotional intelligence -- 2. Simulating emotion -- 2.1. Emotions and artificial agents -- 2.2. Choice/behavior processes of artificial agents -- References -- Evolution of high level recursive thinking in a collision avoiding agent model F. Zanlungo -- 1. Introduction -- 2. The Model -- 2.1. Experimental Setup -- 3. Behaviour for Different Values of l -- 4. Evolutionary Experiments -- 5. Analysis -- 6. Conclusions -- References -- PART IV: ROBOTICS -- Who is the leader? Dynamic role allocation through communication in a population of homogeneous robots O. Gigliotta, M. Mirolli, S. Nolfi -- 1. Introduction -- 2. Related Work -- 3. Experimental Setup -- 4. Results -- 5. Discussion and Conclusion -- References.

Cooperation in corvids: a simulative study with evolved robot O. Miglino, M. Ponticorvo, D. Donetto, S. Nolfi, P. Zucca -- 1. Introduction -- 2. Method -- 2.1. The loose string task -- 2.2. Experimental setup -- 2.3. The environment and the robots -- 2.4. The neural controller -- 2.5. The evolutionary algorithm -- 3. Results and Conclusions -- Acknowledgements -- References -- Carwin42: Evolution of artificial intelligence controller and aeromechanical setup in simulated race cars P. Pinto, M.D. Penna, E. Genua, M. Mercurio, P. Memmolo -- 1. Introduction -- 2. Racecar physics -- 2.1. Tyres -- 2.2. Aerodynamic setup -- 2.3. Mechanical setup -- 2.4. Aeromechanical interactions -- 3. Racecar driving -- 4. Software implementation -- 5. Physical models -- 6. Driver AI -- 7. Evolution engine -- 8. Tests and results -- 8.1. Decreasing laptimes -- 8.2. Realistic setups -- 8.3. Is it in the car or in the driver? -- 9. Future work -- Acknowledgments -- References -- PART V: SOCIAL SCIENCES -- Distributed processes in a agent-based model of innovation L. Ansaloni, M. Villani, D. Lane -- 1. Introduction -- 2. The model -- 3. The experiments -- 3.1. Stereotyped routines -- 3.2. Spatial constraints -- 3.3. Goals distributed design -- 4. Conclusions -- References -- Meet, discuss and trust each other: large versus small groups T. Carletti, D. Fanelli, A. Guarino, A. Guazzini -- 1. Introduction -- 2. The model -- 3. The social network of affinities -- 4. Results -- 5. Conclusion -- Appendix A. Computation of γeff -- References -- Shaping opinions in a social network T. Carletti, S. Righi -- 1. Introduction -- 2. The model -- 3. Results -- 4. Conclusions -- References -- A cellular automata model for highway traffic with preliminary results S. Di Gregorio, R. Umeton, A. Bicocchi, A. Evangelisti -- 1. Introduction -- 2. The STRATUNA general model.

3. The STRATUNA transition function -- 4. STRATUNA first partial implementation -- 4.1. Results of simulations with STRATUNA 4 -- 5. Conclusions -- Appendix A. Pseudo-code of the 4 transition function 2. A. Schadschneider, Phys. A 372, 142 (2006). 3. K. Nagel and M. Schreckenberg, J. Phys. I 2, p. 2221 (1992). -- References -- Imaginary or actual artificial worlds using a new tool in the ABM perspective P. Terna -- 1 From a "classical" protocol to a new tool -- 1.1 The Swarm protocol -- 1.2 The Swarm-Like Agent Protocol in Python (SLAPP) -- 2 Creating an imaginary artificial world: the surprising behavior of learning chameleons -- 2.1 The structure of the chameleon world -- 2.2 Reinforcement learning -- 2.3 Results in chameleon behavior -- 3 Recreating an actual world in an artificial way: interbank payments -- 3.1 The payment problem -- 3.2 Two parallel systems -- 3.3 A case of simulated behavior -- 4 Future developments -- References -- PART VI: SYSTEMS BIOLOGY AND SYNTHETIC BIOLOGY -- How critical random Boolean networks may be affected by the interaction with others C. Damiani, A. Graudenzi, M. Villani -- 1. Introduction -- 2. Random Boolean Networks -- 3. Multi Random Boolean Networks -- 4. Dynamics of MRBNs -- 5. Relationship between response to interaction and response to perturbations -- 6. Conclusions -- Acknowledgments -- References -- Dynamics of interconnected Boolean networks with scale-free topology C. Damiani, M. Villani, Ch. Darabos, M. Tomassini -- 1. Introduction -- 2. Introduction to Random Boolean Networks -- 3. Scale-Free Boolean Networks -- 3.1. Construction of Scale-Free Networks -- 4. The Multi Random Boolean Networks model -- 5. Methodology and Simulations -- 5.1. Analysis of the Results -- 6. Conclusions -- Acknowledgements -- References.

A new model of genetic network: the gene protein Boolean network A. Graudenzi, R. Serra.