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The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two(More)
—Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of coevo-lutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Co-evolution. We demonstrate the efficacy of Fuzzy(More)
This paper presents a genetic fuzzy system approach to control a nonlinear dynamic model of the HIV infection. The system is conceived to find mamdani fuzzy controllers that are capable of boosting the immune response while reducing the impact on the body because of the use of potentially toxic medicaments. General aspects of the used approach are described(More)
The term evolutionary computation encompasses a host of methodologies inspired by natural evolution that are used to solve hard problems. This paper provides an overview of evolutionary computation as applied to problems in the medical domains. We begin by outlining the basic workings of six types of evolutionary algorithms: genetic algorithms, genetic(More)
In this paper we present a platform for evolving spiking neural networks on FPGAs. Embedded intelligent applications require both high performance, so as to exhibit real-time behavior, and flexibility, to cope with the adaptivity requirements. While hardware solutions offer performance, and software solutions offer flexibility, reconfigurable computing(More)
In this contribution, we describe a hardware platform for evolving a fuzzy system by using Fuzzy CoCo — a cooperative coevo-lutionary methodology for fuzzy system design — in order to speed up both evolution and execution. Reconfigurable hardware arises between hardware and software solutions providing a trade-off between flexibility and performance. We(More)
In this paper we present a functional model of spiking neuron intended for hardware implementation. The model allows the design of speed-and/or area-optimized architectures. Some features of biological spiking neu-rons are abstracted, while preserving the functionality of the network, in order to define an architecture easily implementable in hardware,(More)
This paper presents an architectural proposal for a hardware-based interval type-2 fuzzy inference system. First, it presents a computational model which considers parallel inference processing and type reduction based on computing inner and outer bound sets. Taking into account this model, we conceived a hardware architecture with several pipeline stages(More)