Transitions in non-conserving models of Self-Organized Criticality

We investigate a random--neighbours version of the two dimensional non-conserving earthquake model of Olami, Feder and Christensen [Phys. Rev. Lett. {\bf 68}, 1244 (1992)]. We show both analytically and numerically that criticality can be expected even in the presence of dissipation. As the critical level of conservation, $\alpha_c$, is approached, the cut--off of the avalanche size distribution scales as $\xi\sim(\alpha_c-\alpha)^{-3/2}$. The transition from non-SOC to SOC behaviour is controlled by the average branching ratio $\sigma$ of an avalanche, which can thus be regarded as an order parameter of the system. The relevance of the results are discussed in connection to the nearest-neighbours OFC model (in particular we analyse the relevance of synchronization in the latter).Comment: 8 pages in latex format; 5 figures available upon reques

Correlations and invariance of seismicity under renormalization-group transformations

The effect of transformations analogous to those of the real-space renormalization group are analyzed for the temporal occurrence of earthquakes. The distribution of recurrence times turns out to be invariant under such transformations, for which the role of the correlations between the magnitudes and the recurrence times are fundamental. A general form for the distribution is derived imposing only the self-similarity of the process, which also yields a scaling relation between the Gutenberg-Richter b-value, the exponent characterizing the correlations, and the recurrence-time exponent. This approach puts the study of the structure of seismicity in the context of critical phenomena.Comment: Short paper. I'll be grateful to get some feedbac

What Fraction of Boron-8 Solar Neutrinos arrive at the Earth as a nu_2 mass eigenstate?

We calculate the fraction of B^8 solar neutrinos that arrive at the Earth as a nu_2 mass eigenstate as a function of the neutrino energy. Weighting this fraction with the B^8 neutrino energy spectrum and the energy dependence of the cross section for the charged current interaction on deuteron with a threshold on the kinetic energy of the recoil electrons of 5.5 MeV, we find that the integrated weighted fraction of nu_2's to be 91 \pm 2 % at the 95% CL. This energy weighting procedure corresponds to the charged current response of the Sudbury Neutrino Observatory (SNO). We have used SNO's current best fit values for the solar mass squared difference and the mixing angle, obtained by combining the data from all solar neutrino experiments and the reactor data from KamLAND. The uncertainty on the nu_2 fraction comes primarily from the uncertainty on the solar delta m^2 rather than from the uncertainty on the solar mixing angle or the Standard Solar Model. Similar results for the Super-Kamiokande experiment are also given. We extend this analysis to three neutrinos and discuss how to extract the modulus of the Maki-Nakagawa-Sakata mixing matrix element U_{e2} as well as place a lower bound on the electron number density in the solar B^8 neutrino production region.Comment: 23 pages, 8 postscript figures, latex. Dedicated to the memory of John Bahcall who championed solar neutrinos for many lonely year

Boundary effects in a random neighbor model of earthquakes

We introduce spatial inhomogeneities (boundaries) in a random neighbor version of the Olami, Feder and Christensen model [Phys. Rev. Lett. 68, 1244 (1992)] and study the distributions of avalanches starting both from the bulk and from the boundaries of the system. Because of their clear geophysical interpretation, two different boundary conditions have been considered (named free and open, respectively). In both cases the bulk distribution is described by the exponent $\tau \simeq {3/2}$. Boundary distributions are instead characterized by two different exponents $\tau ' \simeq {3/2}$ and $\tau ' \simeq {7/4}$, for free and open boundary conditions, respectively. These exponents indicate that the mean-field behavior of this model is correctly described by a recently proposed inhomogeneous form of critical branching process.Comment: 6 pages, 2 figures ; to appear on PR

Novelty-driven cooperative coevolution

Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.info:eu-repo/semantics/publishedVersio

Cooperative coevolution of morphologically heterogeneous robots

Morphologically heterogeneous multirobot teams have shown significant potential in many applications. While cooperative coevolutionary algorithms can be used for synthesising controllers for heterogeneous multirobot systems, they have been almost exclusively applied to morphologically homogeneous systems. In this paper, we investigate if and how cooperative coevolutionary algorithms can be used to evolve behavioural control for a morphologically heterogeneous multirobot system. Our experiments rely on a simulated task, where a ground robot with a simple sensor-actuator configuration must cooperate tightly with a more complex aerial robot to find and collect items in the environment. We first show how differences in the number and complexity of skills each robot has to learn can impair the effectiveness of cooperative coevolution. We then show how coevolution’s effectiveness can be improved using incremental evolution or novelty-driven coevolution. Despite its limitations, we show that coevolution is a viable approach for synthesising control for morphologically heterogeneous systems.info:eu-repo/semantics/publishedVersio

Avoiding convergence in cooperative coevolution with novelty search

Cooperative coevolution is an approach for evolving solutions composed of coadapted components. Previous research has shown, however, that cooperative coevolutionary algorithms are biased towards stability: they tend to converge prematurely to equilibrium states, instead of converging to optimal or near-optimal solutions. In single-population evolutionary algorithms, novelty search has been shown capable of avoiding premature convergence to local optima — a pathology similar to convergence to equilibrium states. In this study, we demonstrate how novelty search can be applied to cooperative coevolution by proposing two new algorithms. The first algorithm promotes behavioural novelty at the team level (NS-T), while the second promotes novelty at the individual agent level (NS-I). The proposed algorithms are evaluated in two popular multiagent tasks: predator-prey pursuit and keepaway soccer. An analysis of the explored collaboration space shows that (i) fitnessbased evolution tends to quickly converge to poor equilibrium states, (ii) NS-I almost never reaches any equilibrium state due to constant change in the individual populations, while (iii) NS-T explores a variety of equilibrium states in each evolutionary run and thus significantly outperforms both fitness-based evolution and NS-I.info:eu-repo/semantics/acceptedVersio

odNEAT: an algorithm for decentralised online evolution of robotic controllers

Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-( mu + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM( mu + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.info:eu-repo/semantics/submittedVersio