Application areas of AIS: The past, the present and the future

Abstract

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the past, and ask the difficult question “was it worth it ?”. We then attempt to suggest a set of problem features that we believe will allow the true potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS.

Introduction

The artificial immune system (AIS) community has been vibrant and active for a number of years now, producing a prolific amount of research ranging from modelling the natural immune system, solving artificial or bench-mark problems, to tackling real-world applications, using an equally diverse set of immune-inspired algorithms. Whilst it is natural, and indeed healthy, for a somewhat scattergun approach to be taken in the early days of developing any new paradigm, in the sense that high-level, often naïve metaphors are selected and applied to problem areas that have often been tackled with other paradigms, there comes a point at which research effort needs to have a more coherent focus in order to more clearly define the field, and allow it to go forward and be fully exploited. We argue that this point has now been reached in the AIS community—with a solid foundation of published work to build on, the time has come to try and define the role that AIS can play and the type of applications that will really allow its potential to be realised.

Without a doubt there have been a lot of successful applications of AIS, and these should not be ignored. However, at this point, there are still few exemplars that really stand out as instances of successfully applying an AIS to a hard, real-world problems, or of AIS being used in earnest in industry. This is in contrast for example to the field of Evolutionary Algorithms, where at the most recent flagship conference in the field, GECCO 2004 [4], there were 38 papers describing the applications of EAs to real-world problems, and the EVONET repository [2] is able to list 39 examples of Evolution at Work, i.e. practical applications of EAs. On the one hand, this is somewhat of an unfair comparison, given the relative time-periods that the two fields have been active, however it illustrates the importance of focusing research effort in the next few years in order to provide hard evidence of a distinctive niche for AIS.

For any new paradigm to prove itself is always a difficult task—there is a lot of good competition from existing tried and tested algorithms. There has perhaps been a natural tendency for AIS to be compared to other biologically inspired paradigms such as Evolutionary Algorithms, Neural-networks, and to other more traditional classification or clustering algorithms. Scientifically, it is essential that such comparisons to be made; however, we argue that it is not sufficient for AIS simply to outperform other algorithms on any given set of problem instances to be declared useful. For a start, test instances (particularly benchmarks) are not necessarily difficult, and any number of other problem instances can be generated on which performance will be unknown. Secondly, in the light of the no-free lunch theorem [86], we cannot expect any one algorithm to outperform all others given all possible problem instances. We argue that for a paradigm to be truly successful, it should contain features that are not present in other paradigms and/or it should give rise to emergent system properties which are not obtained through other paradigms. Thus, the algorithm can be described as distinctive. This is in contrast to work in [34] which suggests that an appropriate manner in which to assess AIS algorithms is by their distinctiveness and effectiveness. Garrett [34] defines distinctiveness in terms of an algorithm containing novel symbols, expressions or processes. We counter that this definition is not strong enough; some of the essential characteristics of immune algorithms are emergent properties that arise from the interaction of a number of processes which in themselves may not be described as distinct. In addition, whilst it is important that an algorithm is effective, we disagree with the definition proposed in [34] which measures effectiveness in terms of an algorithm performing better than other algorithms on shared benchmarks tests, quicker than other algorithms or providing a unique method of obtaining results. The problems associated with benchmarking data-sets have already been outlined and hence we do not believe that only being better or quicker will allow AIS to prove itself. Furthermore, in a climate in which there are any number of algorithms (biological and otherwise) inspired by an equally diverse range of processes, it seems critical that an algorithm must achieve something that cannot be achieved by any other means in order to earn its place in history.

In this position paper, we hope to extract some general features of problems that we believe will allow AIS to really bring some benefit, and thus distinguish it from other techniques. We suggest that the way forward for AIS is in part a focussed attempt to carefully select application areas based on mapping problem features to mechanisms exhibited by the IS, taking the problem-oriented perspective outlined by example in [73], [32], [13], and discussed further in Section 4.2. However, we emphasise that application development needs to be under-pinned with a continuing line of research into the theoretical basis of AIS and with the overriding need for extraction of novel and accurate metaphors from immunology.²