Moshe Sipper

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.

A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

If one considers life on Earth since its very beginning, three levels of organization can be distinguished: the phylogenetic level concerns the temporal evolution of the genetic programs within individuals and species, the ontogenetic level concerns the developmental process of a single multicellular organism, and the epigenetic level concerns the learning processes during an individual organisms lifetime. In analogy to nature, the space of bio-inspired hardware systems can be partitioned along these three axes-phylogeny, ontogeny and epigenesis (POE)-giving rise to the POE model. This paper is an exposition and examination of bio-inspired systems within the POE framework, with our goals being: (1) to present an overview of current-day research, (2) to demonstrate that the POE model can be used to classify bio-inspired systems, and (3) to identify possible directions for future research, derived from a POE outlook. We discuss each of the three axes separately, considering the systems created to date and plotting directions for continued progress along the axis in question.

Fifty years of research on self-replication: an overview

The study of artificial self-replicating structures or machines has been taking place now for almost half a century. My goal in this article is to present an overview of research carried out in the domain of self-replication over the past 50 years, starting from von Neumanns work in the late 1940s and continuing to the most recent research efforts. I shall concentrate on computational models, that is, ones that have been studied from a computer science point of view, be it theoretical or experimental. The systems are divided into four major classes, according to the model on which they are based: cellular automata, computer programs, strings (or strands), or an altogether different approach. With the advent of new materials, such as synthetic molecules and nanomachines, it is quite possible that we shall see this somewhat theoretical domain of study producing practical, real-world applications.

The emergence of cellular computing

The von Neumann architecture-which is based upon the principle of one complex processor that sequentially performs a single complex task at a given moment-has dominated computing technology for the past 50 years. Recently however, researchers have begun exploring alternative computational systems based on entirely different principles. Although emerging from disparate domains, the work behind these systems shares a common computational philosophy, which the author calls cellular computing. This philosophy promises to provide new means for doing computation more efficiently-in terms of speed, cost, power dissipation, information storage, and solution quality. Simultaneously, cellular computing offers the potential of addressing much larger problem instances than previously possible, at least for some application domains. Cellular computing has attracted increasing research interest. Work in this field has produced results that hold prospects for a bright future. Yet questions must be answered before cellular computing can become a mainstream paradigm. What classes of computational tasks are most suited to it? How do we match the specific properties and behaviors of a given model to a suitable class of problems? At its heart, cellular computing consists of three principles: simplicity, vast parallelism, and locality.

Design, observation, surprise! A test of emergence

The field of artificial life (Alife) is replete with documented instances of emergence, though debate still persists as to the meaning of this term. We contend that, in the absence of an acceptable definition, researchers in the field would be well served by adopting an emergence certification mark that would garner approval from the Alife community. Toward this end, we propose an emergence test, namely, criteria by which one can justify conferring the emergence label.

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.

On the generation of high-quality random numbers by two-dimensional cellular automata

Finding good random number generators (RNGs) is a hard problem that is of crucial import in several fields, ranging from large-scale statistical physics simulations to hardware self-test. In this paper, we employ the cellular programming evolutionary algorithm to automatically generate two-dimensional cellular automata (CA) RNGs. Applying an extensive suite of randomness tests to the evolved CAs, we demonstrate that they rapidly produce high-quality random-number sequences. Moreover, based on observations of the evolved CAs, we are able to handcraft even better RNGs, which not only outperform previously demonstrated high-quality RNGs, but can be potentially tailored to satisfy given hardware constraints.

Toward a viable, self-reproducing universal computer

Self-reproducing, cellular automata-based systems developed to date broadly fall under two categories; the first consists of machines which are capable of performing elaborate tasks, yet are too complex to simulate, while the second consists of extremely simple machines which can be entirely implemented, yet lack any additional functionality aside from self-reproduction. In this paper we present a self-reproducing system which is completely realizable, while capable of executing any desired program, thereby exhibiting universal computation. Our starting point is a simple self-reproducing loop structure onto which we “attach” an executable program (Turing machine) along with its data. The three parts of our system (loop, program, data) are all reproduced, after which the program is run on the given data. The system reported in this paper has been simulated in its entirety; thus, we attain a viable, self-reproducing machine with programmable capabilities.

Co-evolving non-uniform cellular automata to perform computations

A major impediment of cellular automata (CA) stems from the difficulty of utilizing their complex behavior to perform useful computations. Recent studies by Packard and Mitchell et al. have shown that CAs can be evolved to perform a computational task. In this paper non-uniform CAs are studied, where each cell may contain a different rule, in contrast to the original, uniform model. We describe experiments in which non-uniform CAs are evolved to perform the computational task using a local, co-evolutionary algorithm. For radius r = 3 we attain peak performance values of 0.92 comparable to those obtained for uniform CAs (0.93–0.95). This is notable considering the huge search spaces involved, much larger than the uniform case. Smaller radius CAs (previously unstudied in this context) attain performance values of 0.93–0.94. For r = 1 this is considerably higher than the maximal possible uniform CA performance of 0.83, suggesting that non-uniformity reduces connectivity requirements. We thus demonstrate that: (1) non-uniform CAs can attain high computational performance, and (2) such systems can be evolved rather than designed.