758 research outputs found
Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata
We apply two evolutionary search algorithms: Particle Swarm Optimization
(PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that
can perform computational tasks requiring global coordination. In particular,
we compare search efficiency for PSO and GAs applied to both the density
classification problem and to the novel generation of 'chaotic' CA. Our work
furthermore introduces a new variant of PSO, the Binary Global-Local PSO
(BGL-PSO)
Noncooperative algorithms in self-assembly
We show the first non-trivial positive algorithmic results (i.e. programs
whose output is larger than their size), in a model of self-assembly that has
so far resisted many attempts of formal analysis or programming: the planar
non-cooperative variant of Winfree's abstract Tile Assembly Model.
This model has been the center of several open problems and conjectures in
the last fifteen years, and the first fully general results on its
computational power were only proven recently (SODA 2014). These results, as
well as ours, exemplify the intricate connections between computation and
geometry that can occur in self-assembly.
In this model, tiles can stick to an existing assembly as soon as one of
their sides matches the existing assembly. This feature contrasts with the
general cooperative model, where it can be required that tiles match on
\emph{several} of their sides in order to bind.
In order to describe our algorithms, we also introduce a generalization of
regular expressions called Baggins expressions. Finally, we compare this model
to other automata-theoretic models.Comment: A few bug fixes and typo correction
SOUND SYNTHESIS WITH CELLULAR AUTOMATA
This thesis reports on new music technology research which investigates the use of cellular automata (CA) for the digital synthesis of dynamic sounds. The research addresses the problem of the sound design limitations of synthesis techniques based on CA. These limitations fundamentally stem from the unpredictable and autonomous nature of these computational models.
Therefore, the aim of this thesis is to develop a sound synthesis technique based on CA capable of allowing a sound design process. A critical analysis of previous research in this area will be presented in order to justify that this problem has not been previously solved. Also, it will be discussed why this problem is worthwhile to solve.
In order to achieve such aim, a novel approach is proposed which considers the output of CA as digital signals and uses DSP procedures to analyse them. This approach opens a large variety of possibilities for better understanding the self-organization process of CA with a view to identifying not only mapping possibilities for making the synthesis of sounds possible, but also control possibilities which enable a sound design process.
As a result of this approach, this thesis presents a technique called Histogram Mapping Synthesis (HMS), which is based on the statistical analysis of CA evolutions by histogram measurements. HMS will be studied with four different automatons, and a considerable number of control mechanisms will be presented. These will show that HMS enables a reasonable sound design process.
With these control mechanisms it is possible to design and produce in a predictable and controllable manner a variety of timbres. Some of these timbres are imitations of sounds produced by acoustic means and others are novel. All the sounds obtained present dynamic features and many of them, including some of those that are novel, retain important characteristics of sounds produced by acoustic means
Digital control networks for virtual creatures
Robot control systems evolved with genetic algorithms traditionally take the form
of floating-point neural network models. This thesis proposes that digital control systems,
such as quantised neural networks and logical networks, may also be used for
the task of robot control. The inspiration for this is the observation that the dynamics
of discrete networks may contain cyclic attractors which generate rhythmic behaviour,
and that rhythmic behaviour underlies the central pattern generators which drive lowlevel
motor activity in the biological world.
To investigate this a series of experiments were carried out in a simulated physically
realistic 3D world. The performance of evolved controllers was evaluated on two well
known control tasksâpole balancing, and locomotion of evolved morphologies. The
performance of evolved digital controllers was compared to evolved floating-point neural
networks. The results show that the digital implementations are competitive with
floating-point designs on both of the benchmark problems. In addition, the first reported
evolution from scratch of a biped walker is presented, demonstrating that when
all parameters are left open to evolutionary optimisation complex behaviour can result
from simple components
At the Biological Modeling and Simulation Frontier
We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine
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