Silvano P. Colombano

A circuit representation technique for automated circuit design

We present a method of automatically generating circuit designs using evolutionary search and a set of circuit constructing primitives arranged in a linear sequence. This representation has the desirable property that virtually all sets of circuit-constructing primitives result in valid circuit graphs. While this representation excludes certain circuit topologies, it is capable of generating a rich set of them including many of the useful topologies seen in hand-designed circuits. Our system allows circuit size (number of devices), circuit topology, and device values to he evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. In all tasks, our system is able to generate circuits that achieve the target specifications. Although the evolved circuits exist as software models, detailed examinations of each suggest that they are electrically well behaved and thus suitable for physical implementation. The modest computational requirements suggest that the ability to evolve complex analog circuit representations in software is becoming more approachable on a single engineering workstation.

Automated Analog Circuit Sythesis Using a Linear Representation

We present a method of evolving analog electronic circuits using a linear representation and a simple unfolding technique. While this representation excludes a large number of circuit topologies, it is capable of constructing many of the useful topologies seen in hand-designed circuits. Our system allows circuit size, circuit topology, and device values to be evolved. Using a parallel genetic algorithm we present initial results of our system as applied to two analog filter design problems. The modest computational requirements of our system suggest that the ability to evolve complex analog circuit representations in software is becoming more approachable on a single engineering workstation.

Evolutionary Optimization of Yagi-Uda Antennas

Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

A comparison of dynamic fitness schedules for evolutionary design of amplifiers

High-level analog circuit design is a complex problem domain in which evolutionary search has recently produced encouraging results. However, little is known about how to best structure evolution far these tasks. The choices of circuit representation, fitness evaluation technique, and genetic operators clearly have a profound effect on the search process. In this paper, we examine fitness evaluation by comparing the effectiveness of four fitness schedules. Three fitness schedules are dynamic-the evaluation function changes over the course of the run, and one is static. Coevolutionary search is included, and we present a method of evaluating the problem population that is conducive to multiobjective optimization. Twenty-five runs of an analog amplifier design task using each fitness schedule are presented. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.

Towards evolving electronic circuits for autonomous space applications

The relatively new field of Evolvable Hardware studies how simulated evolution can reconfigure, adapt, and design hardware structures in an automated manner. Space applications, especially those requiring autonomy, are potential beneficiaries of evolvable hardware. For example, robotic drilling from a mobile platform requires high-bandwidth controller circuits that are difficult to design. In this paper, we present automated design techniques based on evolutionary search that could potentially be used in such applications. First, we present a method of automatically generating analog circuit designs using evolutionary search and a circuit-construction language. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm, we present experimental results for five design tasks. Second, we investigate the use of coevolution in automated circuit design. We examine fitness evaluation by comparing the effectiveness of four fitness schedules. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.

Goal directed model inversion

A new neural network technique for model inversion called goal directed model inversion (GDM) is presented. It allows the system to produce an inverse model in a goal directed manner. The major advantage of an inverse model created in this matter is that it can adapt to unexpected changes in the system with which it must interact. As an example of the GDMI technique, a simple kinematic controller was built for a simulated robotic arm with three degrees of freedom. The system was trained by presenting a sequence of goals of increasing difficulty in some required region of space. As the controller was trained, its ability to extrapolate correct control actions to new distant goals increased.<<ETX>>

Identifying DNA splice sites using hypernetworks with artificial molecular evolution

Identifying DNA splice sites is a main task of gene hunting. We introduce the hyper-network architecture as a novel method for finding DNA splice sites. The hypernetwork architecture is a biologically inspired information processing system composed of networks of molecules forming cells, and a number of cells forming a tissue or organism. Its learning is based on molecular evolution. DNA examples taken from GenBank were translated into binary strings and fed into a hypernetwork for training. We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation. The hypernetwork generalization performance was comparable to well known classification algorithms. With the best hypernetwork obtained, including local information and heuristic rules, we built a system (HyperExon) to obtain splice site candidates. The HyperExon system outperformed leading splice recognition systems in the list of sequences tested.

Pi-in-a-box: a knowledge-based system for space science experimentation

The principal investigator (PI)-IN-A-BOX knowledge based system helps astronauts perform science experiments in space. These experiments are typically costly to devise and build and often are difficult to perform. Further, the space laboratory environment is unique; ever changing; hectic; and, therefore, stressful. The environment requires quick, correct reactions to events over a wide range of experiments and disciplines, including ones distant from an astronauts main science specialty. This environment suggests the use of advanced techniques for data collection, analysis, and decision making to maximize the value of the research performed. PI-IN-A-BOX aids astronauts with quick-look data collection, reduction, and analysis as well as equipment diagnosis and troubleshooting, procedural reminders, and suggestions for high-value departures from the preplanned experiment protocol. The astronauts have direct access to the system, which is hosted on a portable computer in the Space Lab module. The system is in use on the ground for mission training and was used in flight during the October 1993 space life sciences 2 (SLS-2) shuttle mission.

Active stabilization of images acquired on a walking robotic platform

To increase the quality of scientific data collected from autonomous mobile agents such as rovers and walking robotic devices, biological methods can be mimicked for better navigation and balance control of both the agent itself and the manipulation of scientific instruments. Drawing on the design of the neuro-vestibular control system, the EarBot controller is designed to stabilize a multi-axis camera system mounted atop a moving agent. An eight-legged robot called the SCORPION, designed to navigate and explore rough terrain considered inhospitable to wheeled rovers, is used as the testbed to analyze the EarBots functionality and behavior. Eventually, the EarBot will be used to control the balance the robot itself through expanded modelling of the vestibulo-motor control loops used in postural control. This paper presents the theoretical concepts and initial controller implementations for stabilizing the camera during walking motion of the SCORPION.

Self-sustaining robotic systems

To support NASA’s new missions for sustainable and affordable space exploration, the Robosphere 2002 and 2004 workshops were held at NASA Ames Research Center. The aim of the workshops was to explore the notion of self-sustaining robotic systems as a means of achieving increased scientific returns, decreased exploration costs and reduced chances of mission failure. The fundamental propositions of both workshops were that (a) self-sustaining systems need to provide not only for agent coordination, but also for the exchange of matter (parts) and energy, that (b) increased complexity need not come at the cost of decreased stability or survivability, and that (c) InSitu Resource Utilization will need to play a key role. Nature provides examples of self-sustaining systems -ecologieswhere interactions among components (organisms) involve information, matter, and energy exchanges; and where complexity confers stability. Natural ecologies are thus a source of inspiration for the types of systems we need to build. On the other hand, many valid approaches to self-sustaining human/robotic systems are also inspired by non-biological principles. The 2004 workshop extended those interests to also encompass explicit consideration of human presence and coordination with self-sustaining robotic systems. The papers published in this special issue were based