Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation

Do artificial immune systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the B-cell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledged that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.

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