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ABSTRACT: Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for real-world problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. Recently, the perceptive particle swarm optimisation (PPSO) algorithm was proposed to mimic behaviours of social animals more closely through both social interaction and environmental interaction for applications such as robot control. In this study, we investigate the PPSO algorithm on complex function optimisation problems and its ability to cope with noisy environments.
Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE; 07/2005
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ABSTRACT: Recommender systems are new types of Internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment Web sites. This paper describes a new recommender system, which employs a particle swarm optimization (PSO) algorithm to learn personal preferences of users and provide tailored suggestions. Experiments are carried out to observe the performance of the system and results are compared to those obtained from the genetic algorithm (GA) recommender system and a standard, non-adaptive system based on the Pearson algorithm.
Swarm Intelligence Symposium, 2003. SIS '03. Proceedings of the 2003 IEEE; 05/2003
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ABSTRACT: Mapping between genotype and phenotype using a model of biological development has been widely touted as a technique for evolving solutions to large, complex problems. In this paper we describe two test-bed developmental systems for evolvable hardware problems, and compare each to a naive mapping system. We find that designing evolvable developmental systems is not a trivial problem, however early analysis of the evolved structures demonstrates the potential of the generative processes behind development. We also account for the differences between the results of the two systems, highlighting the importance of search space evolvability over size.
Evolvable Hardware, 2002. Proceedings. NASA/DoD Conference on; 02/2002
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ABSTRACT: One significant feature of artificial immune systems is their ability to adapt to continuously changing environments, dynamically learning the fluid patterns of 'self' and predicting new patterns of 'non-self'. This paper introduces and investigates the behaviour of dynamiCS, a dynamic clonal selection algorithm, designed to have such properties of self-adaptation. The effects of three important system parameters: tolerisation period, activation threshold, and life span are explored. The abilities of dynamiCS to perform incremental learning on converged data, and to adapt to novel data are also demonstrated
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on; 02/2002
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ABSTRACT: In the last five years, the field of evolutionary computation (EC)
has seen a resurgence of new ideas, many stemming from new biological
inspirations. The paper outlines four of these new branches of research:
creative evolutionary systems, computational embryology, evolvable
hardware and artificial immune systems, showing how they aim to extend
the capabilities of EC. Recent, unpublished results by researchers in
each area at the Department of Computer Science, UCL are provided
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on; 02/2001
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ABSTRACT: The paper describes research towards the use of an artificial
immune system (AIS) for network intrusion detection. Specifically, we
focus on one significant component of a complete AIS, static clonal
selection with a negative selection operator, describing this system in
detail. Three different data sets from the UCI repository for machine
learning are used in the experiments. Two important factors, the
detector sample size and the antigen sample size, are investigated in
order to generate an appropriate mixture of general and specific
detectors for learning non-self antigen patterns. The results of series
of experiments suggest how to choose appropriate detector and antigen
sample sizes. These ideal sizes allow the AIS to achieve a good non-self
antigen detection rate with a very low rate of self antigen detection.
We conclude that the embedded negative selection operator plays an
important role in the AIS by helping it to maintain a low false positive
detection rate
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on; 02/2001
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ABSTRACT: The scalability problem is a major impediment to the use of hardware evolution for real-world circuit design problems. A potential solution is to model the map between genotype and phenotype on biological development. Although development has been shown to improve scalability for a few toy problems, it has not been demonstrated for any circuit design problems. This paper presents such a demonstration for two problems, the n-bit adder with carry and even n-bit parity problems, and shows that development imposes, and benefits from, fewer constraints on evolutionary innovation than other approaches to scalability.
Evolvable Hardware, 2005. Proceedings. 2005 NASA/DoD Conference on;
