Carlos Andrés Peña-Reyes

A fuzzy-genetic approach to breast cancer diagnosis

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 prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable.

Evolutionary computation in medicine: an overview

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 programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems. We then describe how evolutionary algorithms are applied to solve medical problems, including diagnosis, prognosis, imaging, signal processing, planning, and scheduling. Finally, we provide an extensive bibliography, classified both according to the medical task addressed and according to the evolutionary technique used.

Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling

Co-evolutionary 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 co-evolutionary computation with the expressive power of fuzzy systems, and introduce a novel algorithm, called Fuzzy CoCo (fuzzy cooperative coevolution). We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem - breast cancer diagnosis, obtaining the best results to date while expending less computational effort than previous processes. Analyzing our results, we derive guidelines for setting the algorithm parameters given a (hard) problem to solve. We hope Fuzzy CoCo proves to be a powerful tool in the fuzzy modeler toolkit.

An FPGA platform for on-line topology exploration of spiking neural networks

Abstract 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 arises between these two types of solutions providing a trade-off between flexibility and performance. Our platform is described as a combination of three parts: a hardware substrate, a computing engine, and an adaptation mechanism. We present, also, results about the performance and synthesis of the neural network implementation on an FPGA.

Pro-Two: a hardware based platform for real time type-2 fuzzy inference

This paper describes Pro-Two, a hardware-based platform for implementing two input-one output interval type-2 fuzzy systems. First, the paper describes a computational model for interval type-2 fuzzy systems that considers parallel inference processing and inner-outer bound sets-based type reduction. Then, taking into account this model, we present our architecture for hardware implementation with several pipeline stages for full parallel execution of fuzzy inferences. Such architecture is used to specify Pro-Two, which is implemented over FPGA technology. Experimental results show that Pro-Two performs more than 30 millions of type-2 fuzzy inferences per second for a nine-rule system. We validate the correct functioning of Pro-Two by implementing on it a type-2 fuzzy adaptive filter. The results obtained corroborate the superior performance of type-2 over type-1 fuzzy logic for this application and its increased tolerance to low arithmetic resolution.

Hardware architecture and FPGA implementation of a type-2 fuzzy system

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 for full parallel execution of type-2 fuzzy inferences. The architectural proposal is used for specifying a type-2 fuzzy processor with reconfigurable rule base, which is implemented over FPGA technology. Implementation results show that this processor performs more than 30 millions of type-2 fuzzy inferences per second.

Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology

The automatic diagnosis of breast cancer is an important, real-world medical problem. In the 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 the highest classification performance shown to date, and which are also (human-)interpretable.

Evolutionary Fuzzy Modeling Human Diagnostic Decisions

Abstract: Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision‐making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling—meaning the construction of fuzzy systems—is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web‐based tool—called COBRA—for aiding radiologists in mammography interpretation.

Applying Fuzzy CoCo to breast cancer diagnosis

Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. We combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem-breast cancer diagnosis-obtaining the best results to date while expending less computational effort than formerly.

A functional spiking neuron hardware oriented model

This work introduces a new class of neuro-fuzzy models called Hierarchical Neuro-Fuzzy BSP Systems (HNFB). These models employ the BSP partitioning (Binary Space Partitioning) of the input space and has been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure. First the paper briefly introduces the HNFB model based on supervised learning algorithm. Then it details the RL_HNFB model, which is a hierarchical neuro-fuzzy system with reinforcement learning process. The RL_HNFB model was evaluated in a benchmark application - mountain car - yielding good performance when compared with different reinforcement learning models.