Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems - BADS '11

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). It may also be discussed whether or not the artworks generated by nature and biologically inspired algorithms can possibly be considered as computationally creative.

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Biologically Inspired and Agent-Based Algorithms for Music

10.1093/oxfordhb/9780190226992.013.18 ◽

2018 ◽

Author(s):

Alice Eldridge ◽

Oliver Bown

Keyword(s):

Contemporary Music ◽

Adaptive Behaviour ◽

Biologically Inspired ◽

Agent Based ◽

Agent Based Modelling ◽

Music Making ◽

Algorithmic Music ◽

Biologically Inspired Algorithms ◽

Systems Art ◽

Future Work

This chapter examines a range of approaches to algorithmic music making inspired by biological systems, and considers topics at the intersection of contemporary music, computer science, and computational creativity. A summary of core precursor movements both within and beyond musical practice (A Life, cybernetics, systems art, etc.) sets the scene, before core models and algorithms are introduced and illustrated. These include evolutionary algorithms, agent-based modelling and self-organizing systems, adaptive behaviour and interactive performance systems, and ecosystemic approaches to composition and computational creative discovery. The chapter closes by reviewing themes for future work in this area: autonomy and agency, and the poetics of biologically inspired algorithms.

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Multi-scale verification of distributed synchronisation

Formal Methods in System Design ◽

10.1007/s10703-020-00347-z ◽

2020 ◽

Vol 55 (3) ◽

pp. 171-221

Author(s):

Paul Gainer ◽

Sven Linker ◽

Clare Dixon ◽

Ullrich Hustadt ◽

Michael Fisher

Keyword(s):

Distributed Systems ◽

Coupled Oscillators ◽

Population Based ◽

Levels Of Abstraction ◽

Biologically Inspired ◽

Collective Models ◽

Multi Scale ◽

Pulse Coupled Oscillators ◽

The Individual ◽

Different Levels

AbstractAlgorithms for the synchronisation of clocks across networks are both common and important within distributed systems. We here address not only the formal modelling of these algorithms, but also the formal verification of their behaviour. Of particular importance is the strong link between the very different levels of abstraction at which the algorithms may be verified. Our contribution is primarily the formalisation of this connection between individual models and population-based models, and the subsequent verification that is then possible. While the technique is applicable across a range of synchronisation algorithms, we particularly focus on the synchronisation of (biologically-inspired) pulse-coupled oscillators, a widely used approach in practical distributed systems. For this application domain, different levels of abstraction are crucial: models based on the behaviour of an individual process are able to capture the details of distinguished nodes in possibly heterogenous networks, where each node may exhibit different behaviour. On the other hand, collective models assume homogeneous sets of processes, and allow the behaviour of the network to be analysed at the global level. System-wide parameters may be easily adjusted, for example environmental factors inhibiting the reliability of the shared communication medium. This work provides a formal bridge across the “abstraction gap” separating the individual models and the population-based models for this important class of synchronisation algorithms.

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Chaotic-Based and Biologically Inspired Cryptosystems for Secure Image Communication and Storage

Improving Information Security Practices through Computational Intelligence - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-9426-2.ch004 ◽

2016 ◽

pp. 74-98

Author(s):

El-Sayed M. El-Alfy

Keyword(s):

Chaos Theory ◽

Data Encryption ◽

Sensitive Data ◽

Biologically Inspired ◽

Image Communication ◽

Work Related ◽

Biologically Inspired Algorithms ◽

And Storage ◽

Crossover And Mutation

Protecting confidentiality of sensitive data is growing in importance in many personal, commercial, governmental, medical and military applications. Data encryption remains the most prevalent mechanism for this goal in cybersecurity to store and communicate data in unintelligible form. However, images are known to have intrinsic characteristics different from text, which limit the applicability of conventional cryptographic algorithms. This chapter provides a review of the work related to image cryptosystems based on chaos theory and biologically-inspired algorithms. Then, a case study is presented using ideas from genetic crossover and mutation to confuse and diffuse images to generate secure cipher images with very low correlation between pixels.

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Adaptive Scheduling for Real-Time Distributed Systems

Advances in Data Mining and Database Management - Biologically-Inspired Techniques for Knowledge Discovery and Data Mining ◽

10.4018/978-1-4666-6078-6.ch011 ◽

2014 ◽

pp. 236-248 ◽

Cited By ~ 1

Author(s):

Apurva Shah

Keyword(s):

Data Mining ◽

Distributed Systems ◽

Real Time ◽

Scheduling Algorithm ◽

Fast Response ◽

Biologically Inspired ◽

Real Time Processing ◽

Time Systems ◽

Earliest Deadline ◽

Single Processor

Biologically inspired data mining techniques have been intensively used in different data mining applications. Ant Colony Optimization (ACO) has been applied for scheduling real-time distributed systems in the recent time. Real-time processing requires both parallel activities and fast response. It is required to complete the work and deliver services on a timely basis. In the presence of timing, a real-time system's performance does not always improve as processor and speed increases. ACO performs quite well for scheduling real-time distributed systems during overloaded conditions. Earliest Deadline First (EDF) is the optimal scheduling algorithm for single processor real-time systems during under-loaded conditions. This chapter proposes an adaptive algorithm that takes advantage of EDF- and ACO-based algorithms and overcomes their limitations.

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