Jason D. Lohn

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.

An Evolved Antenna for Deployment on Nasa’s Space Technology 5 Mission

We present an evolved X-band antenna design and flight prototype currently on schedule to be deployed on NASA’s Space Technology 5 (ST5) spacecraft. Current methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity, and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that would ordinarily not be found. The ST5 antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly-polarized wave and wide bandwidth. Two evolutionary algorithms were used: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. As of this writing, one of our evolved antenna prototypes is undergoing flight qualification testing. If successful, the resulting antenna would represent the first evolved hardware in space, and the first deployed evolved antenna.

Automatic discovery of self-replicating structures in cellular automata

Previous computational models of self-replication using cellular automata (CA) have been manually designed, a difficult and time-consuming process. We show here how genetic algorithms can be applied to automatically discover rules governing self-replicating structures. The main difficulty in this problem lies in the choice of the fitness evaluation technique. The solution we present is based on a multiobjective fitness function consisting of three independent measures: growth in number of components, relative positioning of components, and the multiplicity of replicants. We introduce a new paradigm for CA models with weak rotational symmetry, called orientation-insensitive input, and hypothesize that it facilitates discovery of self-replicating structures by reducing search-space sizes. Experimental yields of self-replicating structures discovered using our technique are shown to be statistically significant. The discovered self-replicating structures compare favorably in terms of simplicity with those generated manually in the past, but differ in unexpected ways. These results suggest that further exploration in the space of possible self-replicating structures will yield additional new structures. Furthermore, this research sheds light on the process of creating self-replicating structures, opening the door to future studies on the discovery of novel self-replicating molecules and self-replicating assemblers in nanotechnology.

Comparing a coevolutionary genetic algorithm for multiobjective optimization

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA yields poor coverage across the Pareto front, yet finds a solution that dominates all the solutions produced by the eight other algorithms.

Computer-automated evolution of an x-band antenna for nasa's space technology 5 mission

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASAs Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

Automated Antenna Design with Evolutionary Algorithms

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a signican t amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically nd novel antenna designs that are more eectiv e than would otherwise be developed. Here we present automated antenna design and optimization methods based on evolutionary algorithms. We have evolved ecien t antennas for a variety of aerospace applications and here we describe one proof-of-concept study and one project that produced gh t antennas that ew on NASA’s Space Technology 5 (ST5) mission.

Evolvable hardware using evolutionary computation to design and optimize hardware systems

Evolvable hardware lies at the intersection of evolutionary computation and physical design. Through the use of evolutionary computation methods, the field seeks to develop a variety of technologies that enable automatic design, adaptation, and reconfiguration of electrical and mechanical hardware systems in ways that outperform conventional techniques. This article surveys evolvable hardware with emphasis on some of the latest developments, many of which deliver performance exceeding traditional methods. As such, the field of evolvable hardware is just now starting to emerge from the research laboratory and into mainstream hardware applications.

Automated synthesis of computational circuits using genetic programming

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.

Causally-guided evolutionary optimization and its application to antenna array design

In recent years, evolutionary computation has been successfully used to solve problems involving engineering design and invention, sometimes producing results that are qualitatively different than previous traditionally-designed solutions. However, while evolutionary methods appear to be a promising tool for supporting design, their usefulness is substantially limited by their computational expense and inability to integrate expert knowledge with evolutionary search. Here we develop and evaluate methods for causally-guided evolutionary design based on expert-supplied cause-effect relations that guide how genetic operators are applied in contrast to conventional genetic operations which are carried out blindly and randomly, using these methods for antenna array design. To our knowledge, this is the first study that biases genetic operations in response to the specific performance characteristics of the individuals to which they are applied, and the first to use explicit cause-effect relations to guide this process. Our experimental evaluation compares using evolutionary systems with and without causal guidance to design directional dipole antenna arrays that meet pre-specified performance criteria. We find that causally-guided systems produce optimal solutions with significantly greater frequency and significant computational savings, suggesting that this approach may substantially improve the use of evolutionary computation in engineering design.