Martin A. Keane

Automated synthesis of analog electrical circuits by means of genetic programming

Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuits components. 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 low-pass filter, a crossover filter, a source identification circuit, an amplifier, a computational circuit, a time-optimal 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 of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the high-level statement of the circuits desired behavior as a measurable mathematical quantity. The eight genetically evolved circuits constitute an instance of an evolutionary computation technique producing results on a task that is usually thought of as requiring human intelligence.

Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming

Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where genetic programming has previously produced human-competitive results. It then presents new human-competitive results involving the automatic synthesis of the design of both the parameter values (i.e., tuning) and the topology of controllers for two illustrative problems. Both genetically evolved controllers are better than controllers designed and published by experts in the field of control using the criteria established by the experts. One of these two controllers infringes on a previously issued patent. Other evolved controllers duplicate the functionality of other previously patented controllers. The results in this paper, in conjunction with previous results, reinforce the prediction that genetic programming is on the threshold of routinely producing human-competitive results and that genetic programming can potentially be used as an “invention machine” to produce patentable new inventions.

Reverse engineering of metabolic pathways from observed data using genetic programming.

Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (such as analog electrical circuits and controllers) whose behavior is modeled by linear and non-linear continuous-time differential equations and whose behavior matches prespecified output values. The concentrations of substances participating in networks of chemical reactions are also modeled by non-linear continuous-time differential equations. This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such that the concentration of the final product of the automatically created network matches the observed time-domain data. Specifically, genetic programming automatically created metabolic pathways involved in the phospholipid cycle and the synthesis and degradation of ketone bodies.

Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming

This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental biology. The design process starts with the random creation of a large population of program trees composed of circuit-constructing functions. Each program tree specifies the steps by which a fully developed circuit is to be progressively developed from a common embryonic circuit appropriate for the type of circuit that the user wishes to design. The fitness measure is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit. The population of program trees is genetically bred over a series of many generations using genetic programming. Genetic programming is driven by a fitness measure and employs genetic operations such as Darwinian reproduction, sexual recombination (crossover), and occasional mutation to create offspring. This automated evolutionary process produces both the topology of the circuit and the numerical values for each component. This paper describes how genetic programming can evolve the circuit for a difficult-to-design low-pass filter.

What's AI done for me lately? Genetic programming's human-competitive results

The automated problem-solving technique of genetic programming has generated at least 36 human-competitive results. In six cases, it automatically duplicated the functionality of inventions patented after January 2000.

Reuse, Parameterized Reuse, and Hierarchical Reuse of Substructures in Evolving Electrical Circuits Using Genetic Programming

Most practical electrical circuits contain modular substructures that are repeatedly used to create the overall circuit. Genetic programming with automatically defined functions and the recently developed architecture-altering operations provides a way to build complex structures with reused substructures. In this paper, we successfully evolved a design for a two-band crossover (woofer and tweeter) filter with a crossover frequency of 2,512 Hz. Both the topology and the sizing (numerical values) for each component of the circuit were evolved during the run. The evolved circuit contained three different noteworthy substructures. One substructure was invoked five times thereby illustrating reuse. A second substructure was invoked with different numerical arguments. This second substructure illustrates parameterized reuse because different numerical values were assigned to the components in the different instantiations of the substructure. A third substructure was invoked as part of a hierarchy, thereby illustrating hierarchical reuse.

Evolution of a 60 Decibel Op Amp Using Genetic Programming

Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier with good frequency generalization.

Routine high-return human-competitive automated problem-solving by means of genetic programming

Genetic programming is a systematic method for getting computers to automatically solve problems. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem by means of a simulated evolutionary process. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. These points are illustrated by a group of recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits, the automatic synthesis of placement and routing of circuits, and the automatic synthesis of controllers as well as references to work involving the automatic synthesis of antennas, networks of chemical reactions (metabolic pathways), genetic networks, mathematical algorithms, and protein classifiers.

Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming

It has been previously established that genetic programming can be used as an automated invention machine to synthesize designs for complex structures. In particular, genetic programming has automatically synthesized structures that infringe, improve upon, or duplicate the functionality of 21 previously patented inventions (including six 21st-century patented analog electrical circuits) and has also generated two patentable new inventions (controllers). There are seven promising factors suggesting that these previous results can be extended to deliver industrial-strength automated design of analog circuits, but two countervailing factors. This chapter explores the question of whether the seven promising factors can overcome the two countervailing factors by reviewing progress on an ongoing project in which we are employing genetic programming to synthesize an amplifier circuit. The work involves a multiobjective fitness measure consisting of 16 different elements measured by five different test fixtures. The chapter describes five ways of using general domain knowledge applicable to all analog circuits, two ways for employing problem-specific knowledge, four ways of improving on previously published genetic programming techniques, and four ways of grappling with the multi-objective fitness measures associated with real-world design problems.

Automatic synthesis using genetic programming of an improved general-purpose controller for industrially representative plants

Most real-world controllers are composed of proportional, integrative, and derivative Signal processing blocks. The so-called PID controller was invented and patented by A. Callender and A.B. Stevenson (1939). Later J.G. Ziegler and N.B. Nichols (1942) developed mathematical rules for automatically selecting the parameter values for PID controllers. In their influential book, K.J. Astrom and T. Hagglund (1995) developed a world-beating PID controller that outperforms the 1942 Ziegler-Nichols rules on an industrially representative set of plants. In this paper, we approached the problem of automatic synthesis of a controller using genetic programming without requiring in advance that the topology of the plant be the conventional PID topology. We present a genetically evolved controller that outperforms the automatic tuning rules developed by Astrom and Hagglund in 1995 for the industrially representative set of plants specified by Astrom and Hagglund.