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. 1999 Elsevier Science B.V. All rights reserved. Keywords: Fuzzy systems; Genetic algorithms; Breast cancer diagnosis www.elsevier.com/locate/artmed 1.

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 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. 1 Introduction In recent years the natural phenomenon of coevolution---the simultaneous, coupled evolution of two or more species--- has been explored by evolutionary-computation practitioners, who introduced the notion of coevolutionary algorithms. It has been shown that, for certain problem domains, coevolution produces better solutions while incurring a lower computational cost. We explore herein the application of coevolution to the design of fuzzy systems, int...

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 the highest classification performance shown to date, and which are also (human-)interpretable. Keywords: Fuzzy systems; Genetic algorithms; Breast cancer diagnosis 1 Introduction A major class of problems in medical science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the physician in making sense out of the welter of data. A prime target for such computerized tools is in the d...

This article provides over one hundred references to works in the medical domain using evolutionary computation

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 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. Analyzing our results, we derive guidelines for setting the algorithm's parameters given a (hard) problem to solve. We hope Fuzzy CoCo proves to be a powerful tool in the fuzzy modeler's toolkit. Keywords--- Cooperative coevolution, fuzzy modeling. I. Introduction: Evolutionary Fuzzy Modeling A FUNDAMENTAL problem in fuzzy modeling is the simultaneous design of the rules describing qualitatively a behavior and the membership functions linking this description wit...

We present an evolutionary approach for discovering fuzzy systems for breast cancer diagnosis. By judiciously designing an appropriate representation scheme (genome) and fitness function, the genetic algorithm is then able to produce successful systems. These surpass the best known systems to date in terms of combined performance and simplicity. Chapter 1 Introduction Fuzzy logic is a computational paradigm that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning [1]. A prime characteristic of fuzzy logic is its capability of expressing knowledge in a linguistic way, allowing a system, called a fuzzy system, to be described by simple, "human-like" rules. The task of finding an appropriate fuzzy system, known as fuzzy modeling, is often performed by human experts based on previous knowledge. Usually, the number of parameters to be defined is high, and fuzzy modeling becomes a non-trivial, non-immediate task for human labour. Ne...

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 coevolutionary computation with the expressive power of fuzzy systems, presenting Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the e#cacy of Fuzzy CoCo by applying it to two hard problems---plant classification and breast cancer diagnosis---obtaining the best classification performance to date, coupled with high human-interpretability; moreover, the computational cost of Fuzzy CoCo is lower than other approaches. 1 Introduction The Merriam-Webster OnLine Dictionary (www.m-w.com) defines model as "a system of postulates, data, and inferences presented as a mathematical description of an entity or state of a#airs." Designing models of complex real-world systems and processes is essential in many fields of science and engineering. The models developed can be used, among others, to explain the be...

This thesis presents Fuzzy CoCo, a novel approach for system design, conducive to explaining human decisions. Based on fuzzy logic and coevolutionary computation, Fuzzy CoCo is a methodology 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

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