Robert E. Smith

Using genetic algorithms to explore pattern recognition in the immune system

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

Searching for diverse, cooperative populations with genetic algorithms

In typical applications, genetic algorithms (GAs) process populations of potential problem solutions to evolve a single population member that specifies an optimized solution. The majority of GA analysis has focused on these optimization applications. In other applications (notably learning classifier systems and certain connectionist learning systems), a GA searches for a population of cooperative structures that jointly perform a computational task. This paper presents an analysis of this type of GA problem. The analysis considers a simplified genetics-based machine learning system: a model of an immune system. In this model, a GA must discover a set of pattern-matching antibodies that effectively match a set of antigen patterns. Analysis shows how a GA can automatically evolve and sustain a diverse, cooperative population. The cooperation emerges as a natural part of the antigen-antibody matching procedure. This emergent effect is shown to be similar to fitness sharing, an explicit technique for multimodal GA optimization. Further analysis shows how the GA population can adapt to express various degrees of generalization. The results show how GAs can automatically and simultaneously discover effective groups of cooperative computational structures.

Fitness inheritance in genetic algorithms

GAS have proven effective on a broad range of search problems. However, when each population member’s fitness evaluation is computationally expensive, the prospect of evaluating an entire population can prohibit use of the GA. This paper examines a GA that overcomes this difficulty by evaluating only a portion of the population. The remainder of the population has its fitness assigned by inheritance. Theoretical arguments justify this approach. An application to a GA-easy problem shows that greater efficiency can be obtained by evaluating only a small portion of the population. A real-world search problem confirms these results. Implications and future directions are discussed.

Journeys in non-classical computation I: A grand challenge for computing research

1. The challengeA gateway event [35] is a change to a system that leads to the possibility of huge increases inkinds and levels of complexity. It opens up a whole new kind of phase space to the system’sdynamics.Gatewayeventsduringevolutionoflifeonearthincludetheappearanceofeukaryotes(organisms with a cell nucleus), an oxygen atmosphere, multi-cellular organisms and grass.Gatewayeventsduringthedevelopmentofmathematicsincludeeachinventionofanewclassofnumbers (negative, irrational, imaginary, ...), and dropping Euclid’s parallel postulate.A gateway event produces a profound and fundamental change to the system: Oncethrough the gateway, life is never the same again. We are currently poised on the threshold ofa significant gateway event in computation: That of breaking free from many of our current“classical computational” assumptions. Our Grand Challenge for computer science isto journey through the gateway event obtained by breaking our current classicalcomputational assumptions, and thereby develop a mature science of Non-ClassicalComputation2. Journeys versus goals

Towards a Conceptual Framework for Artificial Immune Systems

We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of AIS network models. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.

Classifier systems in combat : two-sided learning of maneuvers for advanced fighter aircraft

This paper reports the continuing results of a project where a genetics-based machine learning system acquires rules for novel fighter combat maneuvers through simulation. In this project, a genetics-based machine learning system was implemented to generate high angle-of-attack air combat tactics for advanced fighter aircraft. This system, which was based on a learning classifier system approach, employed a digital simulation model of one-versus-one air combat, and a genetic algorithm, to develop effective tactics for the X-31 experimental fighter aircraft. Previous efforts with this system showed that the resulting maneuvers allowed the X-31 to successfully exploit its post-stall capabilities against a conventional fighter opponent. This demonstrated the ability of the genetic learning system to discover novel tactics in a dynamic air combat environment. The results gained favorable evaluation from fighter aircraft test pilots. However, these pilots noted that the static strategy employed by the X-31s opponent was a limitation. In response to these comments, this paper reports new results with two-sided learning, where both aircraft in a one-versus-one combat scenario use genetics-based machine learning to adapt their strategies. The experiments successfully demonstrate both aircraft developing objectively interesting strategies. However, the results also point out the complexity of evaluating results from mutually adaptive players, due to the red queen effect. These complexities, and future directions of the project, are discussed in the papers conclusions.

Is a learning classifier system a type of neural network

This paper suggests a simple analogy between learning classifier systems (LCSs) and neural networks (NNs). By clarifying the relationship between LCSs and NNs, the paper indicates how techniques from one can be utilized in the other. The paper points out that the primary distinguishing characteristic of the LCS is its use of a co-adaptive genetic algorithm (GA), where the end product of evolution is a diverse population of individuals that cooperate to perform useful computation. This stands in contrast to typical GA/NN schemes, where a population of networks is employed to evolve a single, optimized network. To fully illustrate the LCS/NN analogy used in this paper, an LCS-like NN is implemented and tested. The test is constructed to run parallel to a similar GA/NN study that did not employ a co-adaptive GA. The test illustrates the LCS/NN analogy and suggests an interesting new method for applying GAs in NNs. Final comments discuss extensions of this work and suggest how LCS and NN studies can further benefit each other.

Automated Alphabet Reduction for Protein Datasets

BackgroundWe investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques.ResultsWe applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations.ConclusionOur automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.

Optimal clustering of power networks using genetic algorithms

Abstract This paper presents a new method for optimal network decomposition based on genetic algorithms (GAs). GAs present a powerful, globally oriented optimization method which exploits the mechanism of natural genetics, working on populations of candidate solutions in an effort to reach optima or near optima. Test results on IEEE standard networks are given and compared with those using simulated annealing. The genetic algorithm approach is found to produce significantly better solutions.

Journeys in non-classical computation II: initial journeys and waypoints

†University of York, Heslington, York YO1 5DD, UK‡University of the West of England, Frenchay Campus, Coldharbourlane, Bristol BS16 1QY, UK{University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3WE, UK§University of Kent, Canterbury, Kent CT2 7NZ, UKkUniversity of Cambridge, Madingley Road, Cambridge CB3 OHE, UK#University of Exeter, North Cote House, The Queen’s Drive, Exeter E4 4QJ, UK