Cooperative coevolution of partially heterogeneous multiagent systems

Cooperative coevolution algorithms (CCEAs) facilitate the evolution of heterogeneous, cooperating multiagent systems. Such algorithms are, however, subject to inherent scalability issues, since the number of required evaluations increases with the number of agents. A possible solution is to use partially heterogeneous (hybrid) teams: behaviourally heterogeneous teams composed of homogeneous sub-teams. By having different agents share controllers, the number of coevolving populations in the system is reduced. We propose HybCCEA, an extension of cooperative coevolution to partially heterogeneous multiagent systems. In Hyb-CCEA, both the agent controllers and the team composition are under evolutionary control. During the evolutionary process, we rely on measures of behaviour similarity for the formation of homogeneous sub-teams (merging), and propose a stochastic mechanism to increase heterogeneity (splitting). We evaluate Hyb-CCEA in multiple variants of a simulated herding task, and compare it with a fully heterogeneous CCEA. Our results show that Hyb-CCEA can achieve solutions of similar quality using significantly fewer evaluations, and in most setups, Hyb-CCEA even achieves significantly higher fitness scores than the CCEA. Overall, we show that merging and splitting populations are viable mechanisms for the cooperative coevolution of hybrid teams.info:eu-repo/semantics/publishedVersio

Fourth virial coefficients of asymmetric nonadditive hard-disc mixtures

The fourth virial coefficient of asymmetric nonadditive binary mixtures of hard disks is computed with a standard Monte Carlo method. Wide ranges of size ratio ($0.05\leq q\leq 0.95$) and nonadditivity ($-0.5\leq \Delta\leq 0.5$) are covered. A comparison is made between the numerical results and those that follow from some theoretical developments. The possible use of these data in the derivation of new equations of state for these mixtures is illustrated by considering a rescaled virial expansion truncated to fourth order. The numerical results obtained using this equation of state are compared with Monte Carlo simulation data in the case of a size ratio $q=0.7$ and two nonadditivities $\Delta=\pm 0.2$.Comment: 9 pages, 7 figures; v2: section on equation of state added; tables moved to supplementary material (http://jcp.aip.org/resource/1/jcpsa6/v136/i18/p184505_s1#artObjSF

Cancellation of vorticity in steady-state non-isentropic flows of complex fluids

In steady-state non-isentropic flows of perfect fluids there is always thermodynamic generation of vorticity when the difference between the product of the temperature with the gradient of the entropy and the gradient of total enthalpy is different from zero. We note that this property does not hold in general for complex fluids for which the prominent influence of the material substructure on the gross motion may cancel the thermodynamic vorticity. We indicate the explicit condition for this cancellation (topological transition from vortex sheet to shear flow) for general complex fluids described by coarse-grained order parameters and extended forms of Ginzburg-Landau energies. As a prominent sample case we treat first Korteweg's fluid, used commonly as a model of capillary motion or phase transitions characterized by diffused interfaces. Then we discuss general complex fluids. We show also that, when the entropy and the total enthalpy are constant throughout the flow, vorticity may be generated by the inhomogeneous character of the distribution of material substructures, and indicate the explicit condition for such a generation. We discuss also some aspects of unsteady motion and show that in two-dimensional flows of incompressible perfect complex fluids the vorticity is in general not conserved, due to a mechanism of transfer of energy between different levels.Comment: 12 page

A new Fermi smearing approach for scattering of multi-GeV electrons by nuclei

The cross section for electron scattering by nuclei at high momentum transfers is calculated within the Fermi smearing approximation (FSA), where binding effects on the struck nucleon are introduced via the relativistic Hartree approximation (RHA). The model naturally preserves current conservation, since the response tensor for an off-shell nucleon conserves the same form that for a free one but with an effective mass. Different parameterizations for the inelastic nucleon structure function, are analyzed. The smearing at the Fermi surface is introduced through a momentum distribution obtained from a perturbative nuclear matter calculation. Recent CEBAF data on inclusive scattering of 4.05 GeV electrons on $^{56}$Fe are well reproduced for all measured geometries for the first time, as is evident from the comparison with previous calculations.Comment: 8 pages in Revtex4 style, 6 eps figures, to appear in Physical Review

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

Novelty search in competitive coevolution

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.info:eu-repo/semantics/acceptedVersio

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

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