The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the problem of analog circuit synthesis is well known and there is no previously known general automated technique for synthesizing an analog circuit from a high-level statement of the circuit's desired behavior. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the topology and sizing of a suite of eight different prototypical analog circuits, including a lowpass filter, a crossover (woofer and tweeter) filter, a source identification circuit, an amplifier, a computational circuit, a timeoptimal controller circuit, a temperature-sensing circuit, and a voltage reference circuit. The problem-specific information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the highlevel

Abstract: Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical importance when the small number of required mathematical functions does not warrant converting an analog signal into a digital signal, performing the mathematical function in the digital domain, and then converting the result back to the analog domain. The design of computational circuits is difficult even for mundane mathematical functions and often relies on the clever exploitation of some aspect of the underlying device physics of the components. Moreover, implementation of each different mathematical function typically requires an entirely different clever insight. This paper demonstrates that computational circuits can be designed without such problem-specific insights using a single uniform approach involving genetic programming. Both the circuit topology and the sizing of all circuit components are created by genetic programming. This uniform approach to the automated synthesis of computational circuits is illustrated by evolving circuits that perform the cube root function (for which no circuit was found in the published literature) as well as for the square root, square, and cube functions. 1.

. This paper reports on ongoing attempts to find an efficient and effective representation for the binary signaling of ATR's CAM-Brain Machine (CBM), using the so-called "CoDi-1Bit" model. The CBM is an Field Programmable Gate Array (FPGA) based hardware accelerator which updates 3D cellular automata (CA) cells at the rate of 100 billion a second, allowing a complete run of a genetic algorithm with tens of thousands of CA based neural net circuit growths and hardware compiled fitness evaluations, all in about 1 second. It is hoped that using such a device, it will become practical to evolve 10,000s of neural net modules and then to assemble them into humanly defined RAM based artificial brain architectures which can be run by the CBM in real time to control robots, e.g. a robot kitten. Before large numbers of modules can be assembled together, it is essential that the individual modules have a good functionality and evolvability. The "CoDi-1Bit" CA based neural network model uses 1 bit...

ential design tools, while the later emphasizes adaptation of hardware. It is worth pointing out that EHW is quite di erent from the hardware implementation of evolutionary algorithms, where hardware is used to speed up various evolutionary operations. The hardware itself does not change or adapt. There are two major aspects to EHW: simulated evolution and electronic hardware. The simulated evolution can be driven by genetic algorithms (GAs), genetic programming (GP), evolutionary programming (EP), or evolution strategies (ESs). There is no uniform answer as to which type of evolutionary algorithm would be the best for EHW. Di erent evolutionary algorithms would suit di erent EHW. The electronic hardware used in EHW can be digital, analogue or hybrid circuits. One of the advantages of evolutionary algorithms is that they impose very few constraints on the type of circuits used in EHW. Most EHW relies heavily on recon gurable hardware, such as eld programmable gate arrays (FPGAs). Th

of the work presented in this thesis has been previously published as listed below. Although some of these papers have co-authors, the work appearing in this thesis is entirely my own, with the exception of parts of chapter 3, which presents work jointly carried out by myself and Adrian Thompson. The respective contributions to this work will be explicitly stated at the beginning of the chapter. List of Previous Publications Kuntz, P., Layzell, P., & Snyers, D. (1997). A Colony of Ant-like Agents for Partitioning

Reuse will become increasingly important as larger digital and analog circuits are created by the techniques of the field of evolvable hardware. This paper discusses the ways by which genetic programming can facilitate reuse and the associated advantages of using a developmental process.

We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to significant convergence speedup and, more importantly, significantly reduces the risk of overfitting.

We consider multitask learning of visual concepts within genetic programming (GP) framework. The proposed method evolves a population of GP individuals, with each of them composed of several GP trees that process visual primitives derived from input images. The two main trees are delegated to solving two different visual tasks and are allowed to share knowledge with each other by calling the remaining GP trees (subfunctions) included in the same individual. The method is applied to the visual learning task of recognizing simple shapes, using generative approach based on visual primitives, introduced in [17]. We compare this approach to a reference method devoid of knowledge sharing, and conclude that in the worst case cross-task learning performs equally well, and in many cases it leads to significant performance improvements in one or both solved tasks.

The concept of artificial evolution has been applied to numerous real world applications in different domains. In this paper, we use this concept in the domain of virology to evolve computer viruses. We call this domain as “Evolvable Malware”. To this end, we propose an evolutionary framework that consists of three modules: (1) a code analyzer that generates a high-level genotype representation of a virus from its machine code, (2) a genetic algorithm that uses the standard selection, cross-over and mutation operators to evolve viruses, and (3) the code generator converts the genotype of a newly evolved virus to its machinelevel code. In this paper, we validate the notion of evolution in viruses on a well-known virus family, called Bagle. The results of our proof-of-concept study show that we have successfully evolved new viruses–previously unknown and known-variants of Bagle–starting from a random population of individuals. To the best of our knowledge, this is the first empirical work on evolution of computer viruses. In future, we want to improve this proof-of-concept framework into a full-blown virus evolution engine.

Automated synthesis of analog electronic circuits is recognized as a difficult problem. Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a circuit that can perform source identification by correctly classify an incoming signal into categories.