Catálogo de publicaciones - libros

This paper adopts an interdisciplinary view of the significant elements of ecosystems and the methods by which these might be simulated to explore theoretical issues of relevance to Artificial Life and Ecology. Artificial Life has largely been concerned with evolutionary ecosystems of agents in trivial environments. Ecology commonly produces models of specific habitats and organism populations unsuited to general exploration of theoretical issues. We propose that limitations of the simulations in these disciplines can be overcome by simulating ecosystems from the level of artificial chemistry. We demonstrate the approach’s feasibility by describing several virtual organisms represented at this level. The organisms automatically adopt trophic levels, generate energy from chemical bonds and transform material elements in the process. Virtual organisms may interact with one another and their abiotic environment using the same chemistry. Biosynthesis and decay may also be simulated through this mechanism.

Energy flows in ecological systems which are determined by the structure of the ecological network influence the evolution of the network itself. The total system energy throughflow as an important indicator of the co-evolution of network and flows in the ecosystem can be maximized spontaneously according to the maximum power principle. This principle should be thought as an emergent and evolutionary property of the system. To address the problem of how this principle functioning theoretically, a simple model that exhibits the long term evolution of the ecological network determined by the fast dynamics of the energy flows was presented. Maximum power with the diffusion in the phenotype space was investigated in various settings. Accordingly, the conclusion that the total energy throughflow on the network and the diversity are always positive correlated was drawn.

We present an extremely minimal ecosystem model which takes account of thermodynamic constraints on the organisms’ metabolism. This suggests a way to test the application of a hypothesised principle of Maximum Entropy Production to ecosystems. It also puts definite physical bounds on the rates at which matter can flow through the system and paves the way for more detailed models that have thermodynamic principles built in from the start. In providing the background for this model we point out some connections between thermodynamic principles and autopoiesis.

A long standing debate within ecology is to what extent ecosystem complexity and stability are related. Landmark theoretical studies claimed that the more complex an ecosystem, the more unstable it is likely to be. Stability in an ecosystems context can be assessed in different ways. In this paper we measure stability in terms of a model ecosystem’s ability to regulate environmental conditions. We show how increasing biodiversity in this model can result in the regulation of the environment over a wider range of external perturbations. This is achieved via changes to the ecosystem’s resistance and resilience. This result crucially depends on the feedback that the organisms have on their environment.

As a step towards modeling the evolution of food webs from an individual-based perspective, here we study the evolutionary dynamics of a simple multi-resource ecosystem model at the basal level of a food web. We combine two trade-off mechanisms in resource utilization (consumption abilities) and stoichiometric constraints (consumption needs) into a minimal model, and study the evolution of niche differentiation and coexistence through the interaction. Under a broad range of circumstances the model shows the emergence of specialization. By introducing stoichiometric constraints various evolutionary trajectories become possible but in this simple model we found no evidence for the coexistence of specialists and generalists.

There is a general agreement when modelling ecosystems that the simplest solutions are generally the best. Comparing water temperature models that affect the feeding rate of starfish can show similar results when simulated under a simple scenario. When the system is modified to include environmental change, water temperature models that have similar mean temperatures but differences in variance can produce variable results that correlate to the magnitude of their variance. This paper will examine and compare the effect that four water temperature models, each with similar mean temperatures, has on the predation of mussels by starfish, and how this affects population densities over time. Results find that water temperature models with comparable variance produce similar results; models that differ in variance produce dissimilar results, especially when environmental conditions capitalise on that variance.

This paper belongs to the field of Computational Development. It describes a method that has the objective to provide an effective way of generating arbitrary shapes by using evolutionary-developmental techniques, i.e. by evolving genomes that guide the development of the organism starting from a single cell. The key feature of the method is the explicit introduction of an epigenetic memory, that is a cell variable that is modified during the development process and can take different values in different cells. This variable represents the source of differentiation, that leads different cells to read out different portions of the genome at different times. Preliminary experiments have been performed and the results appear to be quite encouraging: the proposed method was able to evolve a number of 25x25, 32x48 and 64x64 target shapes.

A coarse-coding regulatory model that facilitates neural heterogeneity through a morphogenetic process is presented. The model demonstrates cellular and tissue extensibility through ontogeny, resulting in the emergence of neural heterogeneity, use of gated memory and multistate functionality in a Artificial Neural Tissue framework. In each neuron, multiple networks of proteins compete and cooperate for representation through a coarse-coding regulatory scheme. Intracellular competition and cooperation is found to better facilitate evolutionary adaptability and result in simpler solutions than does the use of homogeneous binary neurons. The emergent use of gated memory functions within this cell model is found to be more effective than recurrent architectures for memory-dependent variants of the unlabeled sign-following robotic task.

The swimming motion of the virtually evolved creature model proposed by Karl Sims is re-investigated on the basis of hydrodynamics. In his work, physical simulation was performed, and the swimming motion of the evolved creature was presented. Animation of the creatures was stimulus, however, detailed description about the simulation condition and its results were not always well described. In this work, we perform hydrodynamic simulation to investigate the swimming motion of virtual creatures. As a result, it is found that collaborating motion of fluid is essential and indispensable for the motion of the creature in water. This mechanism also works as a constraint in constructing creature’s body. We found that the physical property of the water strictly regulate the structure of swimming creatures.

Evolutionary robotics simulations can serve as a tool to clarify counterintuitive or dynamically complex aspects of sensorimotor behaviour. We present a series of simulations that has been conducted in order to aid the interpretation of ambiguous empirical data on human adaptation to delayed tactile feedback. Agents have been evolved to catch objects falling at different velocities to investigate the behavioural impact that lengthening or shortening of sensory delays has on the strategies evolved. A detailed analysis of the evolved model agents leads to a number of hypotheses for the quantification of the existing data, as well as to ideas for possible further empirical experiments. This study confirms the utility of evolutionary robotics simulation in this kind of interdisciplinary endeavour.