Creative or Not? Birds and Ants Draw with Muscle

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs

An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation; The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) -- a swarm intelligence algorithm -- to empower the Differential Evolution (DE) -- an evolutionary algorithm -- over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm

Hysteresis and Noise in Stripe and Clump Forming Systems

We use simulations to examine hysteresis and noise in a model system that produces heterogeneous orderings including stripe and clump phases. In the presence of a disordered substrate, these heterogeneous phases exhibit 1/f$^\alpha$ noise and hysteresis in transport. The noise fluctuations are maximal in the heterogeneous phases, while in the uniform phases the hysteresis vanishes and both $\alpha$ and the noise power decrease. We compare our results to recent experiments exhibiting noise and hysteresis in high-temperature superconductors where charge heterogeneities may occur.Comment: 4 pages, 5 postscript figure

High-Order Coupled Cluster Method (CCM) Calculations for Quantum Magnets with Valence-Bond Ground States

In this article, we prove that exact representations of dimer and plaquette valence-bond ket ground states for quantum Heisenberg antiferromagnets may be formed via the usual coupled cluster method (CCM) from independent-spin product (e.g. N\'eel) model states. We show that we are able to provide good results for both the ground-state energy and the sublattice magnetization for dimer and plaquette valence-bond phases within the CCM. As a first example, we investigate the spin-half $J_1$--$J_2$ model for the linear chain, and we show that we are able to reproduce exactly the dimerized ground (ket) state at $J_2/J_1=0.5$. The dimerized phase is stable over a range of values for $J_2/J_1$ around 0.5. We present evidence of symmetry breaking by considering the ket- and bra-state correlation coefficients as a function of $J_2/J_1$. We then consider the Shastry-Sutherland model and demonstrate that the CCM can span the correct ground states in both the N\'eel and the dimerized phases. Finally, we consider a spin-half system with nearest-neighbor bonds for an underlying lattice corresponding to the magnetic material CaV$_4$O$_9$ (CAVO). We show that we are able to provide excellent results for the ground-state energy in each of the plaquette-ordered, N\'eel-ordered, and dimerized regimes of this model. The exact plaquette and dimer ground states are reproduced by the CCM ket state in their relevant limits.Comment: 34 pages, 13 figures, 2 table

Internationalisation: cui bono?

Many universities are striving to internationalise, each with its own rationale.  To benefit students, these efforts must go beyond the recruitment of international students and the development of transnational education, even though these bring their own fiscal and cultural rewards.  Here we examine the value of the other strands of the international agenda – student and staff exchange and internationalising the curriculum – as the aspects that most directly benefit the student experience

Autopoiesis in creativity and art

The term autopoiesis, (meaning ‘self’) and ‘poiesis’ (mean- ing ‘creation, production’) defines a system capable of repro- ducing and maintaining itself. The term was introduced by the theoretical biologists, Humberto Maturana and Francisco Varela, in 1972 to define the self-maintaining chemistry of living cells. The term has subsequently also been applied to the fields of systems theory and sociology. In this paper we apply this model to characterise creativity in art practise

Autopoiesis and Dance

Este artigo oferece um compromisso conceitual com o processo de criatividade em movimento. No trabalho implantamos uma visão a partir da noção de autopoiese - um termo que engloba “autonomia”, "self" e "poiesis" (que significa "criação" e “produção”) - que foi introduzida pelos biólogos teóricos, Humberto Maturana e Francisco Varela , em 1972, para definir a química de manutenção do self de células vivas e foi, posteriormente, também aplicada aos campos da teoria dos sistemas e sociologia. Neste trabalho, empregaremos a formalização para sugerir como o processo de engajamento de um dançarino com o movimento é capaz de reproduzir e manter-se; explorando, teoricamente, como essa atenção é mantida e como, inevitavelmente, a vontade do dançarino deve esgotar, eventualmente, e como seus movimentos desaparecerem de volta à quietude