The Missing Link! A New Skeleton for Evolutionary Multi-agent Systems in Erlang

Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying parallel implementation. We demonstrate the use of our skeleton on two different evolutionary computing applications: (1) computing the minimum of the Rastrigin function; and (2) solving an urban traffic optimisation problem. We show that we can obtain very good speedups (up to 142.44 ×× the sequential performance) on a variety of different parallel hardware, while requiring very little parallelisation effort.Publisher PDFPeer reviewe

Information sharing impact of stochastic diffusion search on differential evolution algorithm

This work details the research aimed at applying the powerful resource allocation mechanism deployed in stochastic diffusion search (SDS) to the differential evolution (DE), effectively merging a nature inspired swarm intelligence algorithm with a biologically inspired evolutionary algorithm. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between the population elements, has the potential to improve the optimisation capability of classical DE algorithms. This claim is verified by running several experiments using state-of-the-art benchmarks. Additionally, the significance of the frequency within which SDS introduces communication and information exchange is also investigated

A Comparative Analysis of Detecting Symmetries in Toroidal Topology

In late 1940s and with the introduction of cellular automata, various types of problems in computer science and other multidisciplinary fields have started utilising this new technique. The generative capabilities of cellular automata have been used for simulating various natural, physical and chemical phenomena. Aside from these applications, the lattice grid of cellular automata has been providing a by-product interface to generate graphical patterns for digital art creation. One notable aspect of cellular automata is symmetry, detecting of which is often a difficult task and computationally expensive. This paper uses a swarm intelligence algorithm—Stochastic Diffusion Search—to extend and generalise previous works and detect partial symmetries in cellular automata generated patterns. The newly proposed technique tailored to address the spatially-independent symmetry problem is also capable of identifying the absolute point of symmetry (where symmetry holds from all perspectives) in a given pattern. Therefore, along with partially symmetric areas, the centre of symmetry is highlighted through the convergence of the agents of the swarm intelligence algorithm. Additionally this paper proposes the use of entropy and information gain measure as a complementary tool in order to offer insight into the structure of the input cellular automata generated images. It is shown that using these technique provides a comprehensive picture about both the structure of the images as well as the presence of any complete or spatially-independent symmetries. These technique are potentially applicable in the domain of aesthetic evaluation where symmetry is one of the measures

Development Of Dialect-Specific Speech Recognizers Using Adaptation Methods

Several adaptation approaches have been proposed in an effort to improve the speech recognition performance in mismatched conditions. However, the application of these approaches had been mostly constrained to the speaker or channel adaptation tasks. In this paper, we first investigate the effect of mismatched dialects between training and testing speakers in an Automatic Speech Recognition (ASR) system. We find that a mismatch in dialects significantly influences the recognition accuracy. Consequently, we apply several adaptation approaches to develop a dialect -specific recognition system using a dialect-dependent system trained on a different dialect and a small number of training sentences from the target dialect. We show that adaptation improves recognition performance dramatically with small amounts of training sentences. We further show that, although the recognition performance of traditionally trained systems highly degrades as we decrease the number of training speakers, the ..