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

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’

Charged Higgs bosons from the 3-3-1 models and the R(D(∗))\mathcal{R}(D^{(*)}) anomalies

Several anomalies in the semileptonic B-meson decays such as $\mathcal{R}(D^{(*)})$ have been reported by $BABAR$, Belle, and LHCb collaborations recently. In this paper, we investigate the contributions of the charged Higgs bosons from the 3-3-1 models to the $\mathcal{R}(D^{(*)})$ anomalies. We find that, in a wide range of parameter space, the 3-3-1 models might give reasonable explanations to the $\mathcal{R}(D^{(*)})$ anomalies and other analogous anomalies of the B meson's semileptonic decays.Comment: Accpeted by Physical Review

The Imitation Game

This issue of the Kybernetes journal is concerned with the philosophical question- Can a Machine Think? Famously, in his 1950 paper `Computing Machinery andIntelligence' [9], the British mathematician Alan Turing suggested replacing this question - which he found \too meaningless to deserve discussion" - with a simple -behavioural - test based on an imagined `Victorianesque' pastime he entitled the`imitation game'. In this special issue of Kybernetes a selection of authors with a special interest in Turing's work (including those who participated in the 2008 AISB Turing Symposium) have been invited to explore and clarify issues arising from Turing's 1950 paper on the Imitation Game; now more widely known as the Turing test

AISB50: Artificial Intelligence at a new branch point

It should be noted that from now on ‘the system’ means not the nervous system but the whole complex of the organism and the environment. Thus, if it should be shown that ‘the system’ has some property, it must not be assumed that this property is attributed to the nervous system: it belongs to the whole; and detailed examination may be necessary to ascertain the contributions of the separate parts. W. Ross Ashby, 1952 [1] An oft repeated aphorism is that the universe is constantly changing and hence that our world is in a perpetual state of flux. In order to behave intelligently within this varying natural environment, any system be it man, machine or animal faces the problem of perceiving invariant aspects of a world in which no two situations are ever exactly the same. Cartesian theories of perception can be broken down into what Chalmers [5] calls the ‘easy problem’ of perception the classification and identification of sense stimuli and a corresponding ‘hard problem’ the realisation of the associated phenomenal state. The difference between the ‘easy’ and the ‘hard’ problems and an apparent lack of link between theories of the former and an account of the latter has been termed the ‘explanatory gap’ [10] and this [unbridgeable] gap is symptomatic of the underlying dualism. Many current theories of natural visual processes are grounded upon the idea that when we perceive, sense data is processed by the brain to form an internal representation of the world. The act of perception thus involves the activation of an appropriate representation. The easy problem reduces to forming a correct internal representation of the world and the hard problem reduces to answering how the activation of a representation gives rise to a sensory experience. In machine perception progress in solving even the ‘easy’ problem has so far been unexpectedly slow; typical bottom-up (or data driven) methodologies involve the processing of raw sense data to extract a set of features; the binding of these features into groups then classifying each group by reference to a putative set of models. Conversely, in top down methods, a typical set of hypotheses of likely perceptions is generated; these are then compared to a set of features in a search for evidence to support each hypothesis

The Singularity, or How I Learned to Stop Worrying and Love AI

Professor Stephen Hawking recently warned about the growing power of Artificial Intelligence (AI) to imbue robots with the ability to both replicate them- selves and to increase the rate at which they get smarter - leading to a tipping point or ‘technological singularity’ when they can outsmart humans. In this chapter I will argue that Hawking is essentially correct to flag up an existential danger surrounding widespread deployment of ‘autonomous machines’, but wrong to be so concerned about the singularity, wherein advances in AI effectively makes the human race redundant; in my world AI - with humans in the loop - may yet be a force for good