Catálogo de publicaciones - libros

In previous work [4] a framework was demonstrated that allows an autonomous robot to automatically synthesize physically-realistic models of its own body. Here it is demonstrated how the same approach can be applied to empower a robot to synthesize physically-realistic models of its surroundings. Robots which build numerical or other non-physical models of their environments are limited in the kinds of predictions they can make about the repercussions of future actions. In this paper it is shown that a robot equipped with a self-made, physically-realistic model can extrapolate: a slow-moving robot consistently predicts the much faster top speed at which it can safely drive across a terrain.

We describe two simple simulations in which artificial organisms evolve an ability to respond to inputs from within their own body and these inputs themselves can evolve. In the first simulation the organisms develop an ability to respond to a pain signal caused by body damage by stopping looking for food when they feel pain since resting while the body is damaged accelerates healing of the body and increases the individual’s survival chances. In the second simulation the pain signal itself evolves, that is, the body develops a tendency to send pain signals to the nervous system when the body is damaged. The results are discussed in terms of an internal robotics in which the robot’s body has an internal structure and not only an external morphology and the neural network that controls the robot’s behavior responds to inputs both from the external environment and from within the body.

This paper presents a new model for computational embryology that mimics the behaviour of biological cells, whose characteristics can be applied to the solution of computational problems. The presented tests apply the model to simple structure generation and provide promising results with regard to its behaviour and applicability to more complex problems.

Biological organisms employ various mechanisms to cope with the dynamic environments they live in. One recent research reported that depending on the rates of environmental variation, populations evolve toward genotypes in different regions of the neutral networks to adapt to the changes. Inspired by that work, we used a genetic programming system to study the evolution of computer programs under environmental variation. Similar to biological evolution, the genetic programming populations exploit neutrality to cope with environmental fluctuations and evolve evolvability. We hope this work sheds new light on the design of open-ended evolutionary systems which are able to provide consistent evolvability under variable conditions.

The traditional GA theory is pillared on the Building Block Hypothesis (BBH) which states that Genetic Algorithms (GAs) work by discovering, emphasizing and recombining low order schemata in high-quality strings, in a strongly parallel manner. Historically, attempts to capture the topological fitness landscape features which exemplify this intuitively straight-forward process, have been mostly unsuccessful. Population-based recombinative methods had been repeatedly outperformed on the special designed abstract test suites, by different variants of mutation-based algorithms. Departing from the BBH, in this paper we seek to exemplify the utility of crossover from a different point of view, emphasizing the creative potential of the crossover operator. We design a special class of abstract test suites, called Trident functions, which exploits the ability of modern GAs to mix good but solutions. This approach has been so far neglected as it is widely believed that disruption caused by mating individuals that are too dissimilar may be harmful. We anticipate that hybridizing different designs induces a complex neighborhood structure unattainable by trajectory-based methods which can conceal novel solutions. Empirical results confirm that the proposed class of problems can be solved efficiently only by population-based panmictic recombinative methods, employing diversity maintaining mechanisms.

The ease with which the genetic algorithm can generate good solutions to challenging optimization problems has resulted in a tendency for researchers to overlook the easily gathered and largely untapped raw data present in the ancestral relationships that guide a population to convergence. This article introduces the notion of a lineage tree structure and associated ancestor count measure that reveals unexpected regularities when studying instances of a simple genetic algorithm applied to three disparate problems. Consequently, a series of explanatory models was constructed to identify the components of the underlying evolutionary mechanism that may be responsible for the commonalities observed. These components (exponential growth of member parentage and inbreeding caused by the appearance of duplicates and shared ancestries) place constraints on the number of ancestors that a solution may have; an insight that may prove valuable for future analysis of the behavior of the genetic algorithm.

In this paper, we consider asynchronous update scheme for a variant of graph rewriting systems called graph-rewriting automata, and show that synchronous update can be simulated by asynchronous update using a constructed rule set from the one for synchronous update. It is well known that such rule construction is possible on cellular automata or other automata networks whose structures are fixed, but graph rewriting automata induce structural changes and additional mechanism of communication and local synchronization is required. Some simple examples are given by simulation.

We study a new variant of the dissipative particle dynamics (DPD) model that includes the possibility of dynamically forming and breaking strong bonds. The emergent reaction kinetics may then interact with self-assembly processes. We observe that self-assembled amphiphilic aggregations such as micelles have a catalytic effect on chemical reaction networks, changing both equilibrium concentrations and reaction frequencies. These simulation results are in accordance with experimental results on the so-called “concentration effect”.

Scientific researchers from computer science, communication and as well from sociology and epidemiology reveal a strong interest in the study of networks. One important feature studied in complex network is the community structure. A new evolutionary technique for community detection in complex networks is proposed in this paper. The new algorithm is based on an information sharing mechanism between the individuals of a population. A real-world network is considered for numerical experiments.

We quantify the local information dynamics at each spatiotemporal point in a complex system in terms of each element of computation: information storage, transfer and modification. Our formulation demonstrates that information modification (or non-trivial information processing) events can be locally identified where “the whole is greater than the sum of the parts”. We apply these measures to cellular automata, providing the first quantitative evidence that collisions between particles therein are the dominant information modification events.