Artificial Life (hereafter ALife) is an interdisciplinary research field that has the purpose of gaining a better understanding of biological life by artificially synthesizing simple and novel forms of life, as well as reproducing lifelike properties of living systems. Synthesis of artificial cells, simulation of large-scale biological networks, intelligent use of exponentially growing amounts of biochemical data, exploitation of biological substrates for computation and control, and deployment of bio-inspired engineering are just some of the main issues of ALife and nowadays are cutting-edge topics.

ALife is at the intersection between a theoretical perspective, namely, the scientific explanations of different levels of life organization (e.g., molecules, compartments, cells, tissues, organs, organisms, societies, and collective and social phenomena) and advanced technological applications (bio-inspired algorithms and techniques for building up effective solutions such as in the fields of robotics, big data analysis, and computational medicine).

This special issue presents the top six articles—revised and extended—that were presented at ECAL 2013, the twelfth European Conference on Artificial Life, which was held in Taormina, Sicily, Italy, September 2–6, 2013 (http://www.dmi.unict.it/ecal2013/). ECAL 2013 was a grand scientific celebration with hundreds of paper and poster presentations (for a total of 267 submissions, 10 workshops, 16 tracks, and more than 350 attendants) by leading scientists in the field, which have described an impressive array of results, ideas, technologies, and applications, thus showing the current state of the art of ALife. The aim of this special issue is to give an incentive for boosting interest and new ideas in designing life and lifelike processes at different levels of complexity.

After a careful peer-review process, only the top six research manuscripts, extended and revised, of ECAL 2013 have been selected for inclusion in this special issue.

In “Multi-crease Self-folding by Global Heating,” by Shuhei Miyashita, Cagdas Onal, and Daniela Rus, the authors present a scheme for the autonomous folding of the body of a 3D robot from a 2D sheet using heat. They developed an easy, fast, and reliable fabrication technique for constructing a self-folding sheet. In particular, they exploit the thermal deformation of a constructive sheet sandwich made of rigid structural layers, and they utilize the V-fold method to attain the targeted folding angles. The authors also developed a mobile robot that uses such a self-folding body and that performs locomotion by using two vibration motors. The locomotion of the robot proves the functionality of the self-folding origami structure.

In “The Search for Candidate Relevant Subsets of Variables in Complex Systems,” by M. Villani, A. Roli, A. Filisetti, M. Fiorucci, I. Poli, and R. Serra, the authors present a method to identify relevant subsets of variables for the comprehension of the organization of a dynamical system. Thus, the dynamical cluster index (DCI)—an information-theoretic measure—is introduced; the DCI relies on observations of current values of the relationships among the system variables, and does not require any previous knowledge. The usefulness of this method was tested in different application domains, where a known dynamical model generates the data, and the aim of the DCI is to uncover significant aspects of the organization of the model. One of the main novelties of this work is in the use of truly dynamical systems.

In “Indirectly Encoding Running and Jumping Sodarace Creatures for Artificial Life,” by Paul Szerlip and Kenneth O. Stanley, the authors present a platform for evolving two-dimensional artificial creatures, whose aim is to serve for future artificial life experiments in evolving creatures. They introduce a new, indirectly encoded Sodarace (IESoR) system, which extends the original Sodarace by enabling the evolution of significantly more complex and regular creature morphologies. The capability of the system has been proved in both walking and jumping domains, in which IESoR discovered a wide breadth of strategies through the novel approach of search with local competition.

In “Experiments on and Numerical Modeling of the Capture and Concentration of Transcription-Translation Machinery inside Vesicles,” by Fabio Mavelli and Pasquale Stano, the authors present a mathematical model of the encapsulation of transcription-translation (TX-TL) coupled reactions based on a minimal protein synthesis model and on different solute partition functions. Such a model can be used to predict the time span and the amount of protein produced starting from any pure system composition. The proposed approach highlights the role of stochastic events in synthetic-cell research, and emphasizes the importance of the integration of stochastic simulations with experimental approaches. The results presented show clearly that experimental data are compatible with an entrapment model, which follows a power law rather than a Gaussian distribution.

In “Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity,” by Heiko Hamann, the author detects how methods for generating selective pressure towards diversity (SPTD) can be transferred from the domain of artificial ecology (AE) to evolutionary robotics (ER). Furthermore, he also investigates how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge. A promising finding of this work is that selective pressure towards unpopulated regions of the search space is generated for both systems. In particular, a beneficial finding is the analogy between the behavioral distances in ER and self-organizing ecology, in terms of the selective pressure generated.

In “Cell-Division Behavior in a Heterogeneous Swarm Environment,” by Adam Erskine and J. Michael Herrmann, the authors present a system of virtual particles that interact using simple kinetic rules. In particular, they present a two-species three-dimensional swarm in which the behavior that emerges resembles cell division. In addition, they have proved that behavior resembling repeated cell division emerges from the low-level kinetic interactions of a heterogeneous swarm. Such division behavior exists in a small but finite volume of the swarm chemistry parameter space. Furthermore, from the outcomes it emerges how the behaviors differ depending on whether the swarm is moving in two- or three-dimensional space.

The guest editors would like to thank all the authors of this special issue for their excellent work, constituting a valuable contribution to the state of the art and the scientific growth of ALife. We would also like to thank everyone who helped to ensure high scientific quality both of ECAL 2013 and of this special issue: the plenary speakers, Prof. Paolo Arena, Prof. Roberto Cingolani, Prof. Roberto Cipolla, Prof. Martin Hanczyc, Prof. Henrik Hautop Lund, Prof. Didier Keymeulen, Prof. Steve Oliver, Prof. Bernhard Ø. Palsson, and Prof. Rolf Pfeifer; all technical program committee members; the workshop chair; the tutorial chair; the publicity chair; and the local organizers. Special thanks go to the editor-in-chief of Artificial Life, Prof. Mark Bedau, for encouraging and accepting this special issue, and to the editorial assistant, Linda Reedijk, for her useful and important support in the realization of this volume.

Author notes

Contact author.

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Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom. E-mail: pietro.lio@cl.cam.ac.uk

Department of Humanities, University of Naples “Federico II,” via Porta di Massa 1, 80133, Naples, Italy. E-mail: orazio.miglino@unina.it

Department of Mathematics and Computer Science, University of Catania, viale Andrea Doria 6, 95125, Catania, Italy. E-mail: nicosia@dmi.unict.it (G.N.); mpavone@dmi.unict.it (M.P.)

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Institute of Cognitive Sciences and Technologies, CNR, Laboratory of Autonomous Robots and Artificial Life, Via S. Martino della Battaglia 44, 00185 Roma, Italy. E-mail: stefano.nolfi@istc.cnr.it