Riccardo Poli

Taming the complexity of natural and artificial evolutionary dynamics

The study of complex adaptive systems is among the key modern tasks in science. Such systems show radically different behaviours at different scales and in different environments, and mathematical modelling of such emergent behaviour is very difficult, even at the conceptual level. We require a new methodology to study and understand complex, emergent macroscopic phenomena. Coarse graining, a technique that originated in statistical physics, involves taking a system with many microscopic degrees of freedom and finding an appropriate subset of collective variables that offer a compact, computationally feasible description of the system, in terms of which the dynamics looks “natural”. This paper will present the key ideas of the approach and will show how it can be applied to evolutionary dynamics.


Curriculum Vitae

Riccardo Poli is a professor in the School of Computer Science and Electronic Engineering at Essex, UK. His main research interests include genetic programming, particle swarm optimisation, the theory of evolutionary algorithms, and brain-computer interfaces. He is a Senior Fellow of The International Society for Genetic and Evolutionary Computation (now ACM SIGEVO) and a recipient of the Evo* award for outstanding contributions to the field of evolutionary computation. He has published approximately 300 refereed papers on evolutionary algorithms, biomedical engineering, neural networks and image/signal processing. He has co-authored the books Foundations of Genetic Programming, Springer, 2002 and A Field Guide to Genetic Programing, Lulu, 2008. He has been chair of numerous international conferences. He is an advisory board member of the Evolutionary Computation journal, an associate editor of the Genetic Programming and Evolvable Machines journal and a member of the editorial board of Swarm Intelligence.


Domenico Parisi    (sponsored by AWARENESS*)

Two limitations of current living artifacts and how to overcome them

Two important properties of all living things are, first, that they become what they are and, second, that they are what they are because of the environment in which they live and with which they interact. Therefore, if we want to understand living things we need to reconstruct their past history and we must know and understand their environment. For example, an individual human being is the current result of multiple past histories: the evolution of the species, the genetically determined development of the individual, his or her past experiences, and the cultural history of the community to which the individual belongs. And these past histories have resulted in that human being because of the particular environment of the species, of the individual, and of his or her social community. In the past 20-30 years a new approach to understanding living things has been inaugurated according to which we can understand living things by reproducing them in artifacts. The artifact is a theory, and how the artifact behaves are the predictions which are derived from the theory. If the artifact behaves like a living thing, in our case, like a human being, we can at least provisionally conclude that the theory incorporated in the artifact is correct and it allows us to understand human beings. But to really understand human beings we should take into consideration the two properties of living things mentioned above. We should reproduce with our artifacts the different historical processes which result in a human individual, that is, evolution, development, learning, and cultural change. This is not easy to do and, fact, it is done only partially. Evolution is reproduced by using genetic algorithms and learning by using various learning algorithms, but much less work is dedicated to development and cultural change. But the real problem is that all these different processes of change are connected together and we may not really understand them if we do not try to capture with our artifacts how they are related and how they interact with one another. For example, clearly evolution creates the preconditions for learning but most artifacts (neural networks) that learn start their learning from zero, that is, from randomly assigned connection weights. Also the other property of living things, their living in and interacting with particular environments, tends to be ignored. The reason is that it is difficult to collect empirical data on the behaviour of organisms in their real ecology and that it is in the controlled environment of an experimental laboratory that we obtain our best empirical data. As a consequence, our living artifacts live in laboratory environments and reproduce laboratory data, not ethological/ecological data. Another consequence is that while learning is an evolved adaptation, we ignore in which environments animal species tend to evolve a capacity to learn. In this Chapter we will describe what has been done and what should be done to overcome these two limitations of living artifacts.


Curriculum Vitae

Domenico Parisi is past director and, currently, research associate at the Institute of Cognitive Sciences and Technologies, National Research Council, in Rome. He is interested in constructing robots not for their practical applications but as scientific tools to better understand behaviour. He is editor of the Italian journal "Sistemi Intelligenti" (Intelligent Systems) and has recently published "Robots that have emotions" (with G. Petrosino, Adaptive Behaviour), "Robots with language" (Frontiers in Neurorobotics), and "The other half of the embodied mind" (Frontiers in Cognition, in press).

* AWARENESS (www.aware-project.eu) is a FET coordination action funded by the European Commission under FP7 and provides support for researchers interested in  Self-Awareness in Autonomic Systems.


Christian Müller-Schloer

Tutorial on Organic Computing

Organic computing is a form of biologically-inspired computing with organic properties. It has emerged recently as a challenging vision for future information processing systems. Organic Computing is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicate freely, and organise themselves in order to perform the actions and services that seem to be required.

The presence of networks of intelligent systems in our environment opens fascinating application areas but, at the same time, bears the problem of their controllability. Hence, we have to construct such systems — which we increasingly depend on — as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central. In order to achieve these goals, our technical systems will have to act more independently, flexibly, and autonomously, i.e. they will have to exhibit life-like properties. We call those systems "organic". Hence, an "Organic Computing System" is a technical system, which adapts dynamically to the current conditions of its environment. It is characterised by the self-X properties:

    * self-organization,
    * self-configuration (auto-configuration),
    * self-optimisation (automated optimization),
    * self-healing,
    * self-protection (automated computer security),
    * self-explaining,
    * and context-awareness.

The vision of Organic Computing and its fundamental concepts arose independently in different research areas like Neuroscience, Molecular Biology, and Computer Engineering.

Self-organising systems have been studied for quite some time by mathematicians, sociologists, physicists, economists, and computer scientists, but so far almost exclusively based on strongly simplified artificial models. Central aspects of Organic Computing systems have been and will be inspired by an analysis of information processing in biological systems.



Marco Tomassini   

Introduction to Evolutionary Game Theory


Evolutionary game theory has been introduced essentially by biologists in the seventies and has immediately diffused into economical and sociological circles. Today, it is a main pillar of the whole edifice of game theory and widely used both in theory and in applications. This tutorial aims at presenting evolutionary game theory in an easy, yet rigorous way, and to relate it with other approaches in game theory. The material presented does not require a previous acquaintance with standard game theory whose fundamentals will be described at the beginning. Next, the main concepts of the evolutionary and dynamical approach will be introduced, namely the concept of an evolutionarily stable strategy and the replicator dynamics. The analogies between Nash equilibria, evolutionarily stable strategies, and rest points of the dynamics will be explained. All the concepts will be illustrated using simple well known paradigmatic games such as the Prisoner's Dilemma, Hawks and Doves, and coordination games among others.

Short Bio

Marco Tomassini is a professor of Computer Science at the Information Systems Department of the University of Lausanne, Switzerland. He has been trained as a chemical physicists and has worked on condensed matter systems. His current research interests are centered around the application of biological ideas to artificial systems. He is active in evolutionary computation, especially spatially structured systems, and the structure and properties of fitness landscapes in combinatorial optimization. He is also interested in machine learning, evolutionary games, and the dynamical properties of networked complex systems. He has been Program Chairman of several international events and has published many scientific papers and several authored and edited books in these fields.