Implementation specification for IIDLE

The mammalian acquired immune system is a robust and powerful information processing system that demonstrates features such as decentralised control, parallel processing adaptation and learning through experience. Artificial Immune Systems (AIS) are a class of machine-learning algorithms that are imbued with some principles of the immune system and attempt to take advantage of some of the benefits of the immune system to tackle difficult problem domains. A novel artificial immune system called IIDLE -- the Immunological Inspired Distributed Learning Environment -- has been introduced previously concerning the techniques inspiration conceptualisation rudimentary architecture and processes. IIDLE is inspired by the spatially distributed nature of the acquired immune system and the clonal selection principle that describes how the immune system learns and adapts in response to stimulation from its environment. The platform is new and untested thus can be considered immature a concern this implication specification attempts to address. This work provides a discussion of an implementation of the IIDLE platform in Java as well as an implementation specification for the processes of IIDLE. Particular attention is paid as to how to embed popular biologically inspired search and optimisation procedures in IIDLE such as genetic algorithms (GA) a local search procedure and an ant colony optimisation (ACO) algorithm. Comment is provided as to how to implement two additional and different algorithms; the learning vector quantisation (LVQ) algorithm and particle swarm optimisation (PSO). The work is rounded off with a discussion of two very interesting extensions of the platform -- IIDLE as a platform for interactive and collaborative search and IIDLE as a distributed adaptive hybrid search system.