The alchemy of computation: designing with the unknown

Proc. Genetic and …, 1999

Many potential inventions are never discovered because the thought processes of scientists and engineers are channeled along well-traveled paths. In contrast, the evolutionary process tends to opportunistically solve problems without considering whether the evolved solution comports with human preconceptions about whether the goal is impossible. This paper demonstrates how genetic programming can be used to automate the process of exploring queries, conjectures, and challenges concerning the existence of seemingly impossible entities. The paper suggests a way by which genetic programming can be used to automate the invention process. We illustrate the concept using a challenge posed by a leading analog electrical engineer concerning whether it is possible to design a circuit composed of only resistors and capacitors that delivers a gain of greater than one. The paper contains a circuit evolved by genetic programming that satisfies the requirement of this challenge as well a related more difficult challenge. The original challenge was motivated by a circuit patented in 1956 for preprocessing inputs to oscilloscopes. The paper also contains an evolved circuit satisfying (and exceeding) the original design requirements of the circuit patented in 1956. This evolved circuit is another example of a result produced by genetic programming that is competitive with a humanproduced result that was considered to be creative and inventive at the time it was first discovered.

Genetic programming (GP) can be viewed as the use of genetic algorithms (GAs) to evolve computational processes in the form of computer programs. In GAs more generally, the individuals in a population may be represented and interpreted in a variety of ways, while in GP the individuals are usually treated as explicit computer programs written in a subset or variant of a conventional programming language. The overall algorithm of the GA is maintained in GP: search proceeds by iteratively evaluating the fitness of the individuals in the population and by applying genetic operators such as crossover and mutation to the higher-fitness individuals in order to explore other promising areas of the search space. In GAs more generally the fitness evaluation step can take many forms, while in GP an individual is evaluated for fitness at least in part by executing the program and by assessing the quality of its outputs. GP techniques have proven valuable for the evolution of structures other than computer programs (e.g. neural networks [1] and analog electrical circuits [2]), but the emphasis on individuals as literal computer programs is the most central defining feature of GP.

Journal of Engineering Science and Technology Review

In this work, a Cartesian Genetic Programming (CGP) method is presented for the evolutionary synthesis of optimal digital circuits. In this evolutionary method, possible circuits are represented as directed graphs in the form of an MxN Cartesian grid. Evolved circuits are qualified using a custom fast digital circuit emulator that was built to serve as the algorithm's fitness function. The performance of the CGP algorithm is tested on six different digital circuits, used as benchmarks. The simulation results show the ability of the CGP scheme to find optimal solutions with significant probabilities. Moreover, the CGP algorithm is able to produce unconventional solutions for known circuits.

Proceedings of the AISB …, 1999

This paper examines the idea of a genetic or evolutionary algorithm being an inspirational or discovery engine. This is illustrated in the particular context of designing electronic circuits. We argue that by connecting pieces of logic together and testing them to see if they carry out the desired function it may be possible to discover new principles of design, and new algebraic techniques. This is illustrated in the design of binary circuits, particularly arithmetic functions, where we demonstrate that by evolving a hierarchical series of examples, it becomes possible to re-discover the well known ripple-carry principle for building adder circuits of any size. We also examine the much harder case of multiplication. We show also that extending the work into the field of multiple-valued logic, the genetic algorithm is able to produce fully working circuits that lie outside conventional algebra. In addition we look at the issue of principle extraction from evolved data.

Advances in Fuzzy Systems — Applications and Theory, 1998

The goal of automating the design process bears many similarities to the goal of automatically creating computer programs. The design process entails creation of a complex structure to satisfy user-defined requirements. The design process is usually viewed as requiring human intelligence. Indeed, design is a major activity of practicing engineers. For these reasons, the design process offers a practical yardstick for evaluating automated programming (program synthesis) techniques. In particular, the design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of a circuit's components. There has previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This paper shows how genetic programming can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller circuit. All five of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.

IC-AI, 2004

Lecture Notes in Computer Science, 1999

Genetic programming is known to be capable of creating designs that satisfy prespecified high-level design requirements for analog electrical circuits and other complex structures. However, in the real world, it is often important that a design satisfy various non-technical requirements. One such requirement is that a design not possess the key characteristics of any previously known design. This paper shows that genetic programming can be used to generate novel solutions to a design problem so that genetic programming may be potentially used as an invention machine. This paper turns the clock back to the period just before the time (1917) when George Campbell of American Telephone and Telegraph invented and patented the design for an electrical circuit that is now known as the ladder filter. Genetic programming is used to reinvent the Campbell filter. The paper then turns the clock back to the period just before the time (1928) when Wilhelm Cauer invented and patented the elliptic filter. Genetic programming is then used to reinvent a technically equivalent filter that avoids the key characteristics of then-preexisting Campbell filter. Genetic programming can be used as an invention machine by employing a twopart fitness measure that incorporates both the degree to which an individual in the population satisfies the given technical requirements and the degree to which the individual does not possess the key characteristics of preexisting technology.

Engineered Biomimicry, 2013

We discuss Evolutionary Computation, in particular Genetic Programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context, discuss the history of Evolutionary Computation and its variants before looking more closely at Genetic Programming. After a discussion of methods and the state-of-theart, we review application areas of Genetic Programming and its strength in providing human-competitive solutions.

2007

Genetic programming (GP) can be viewed as the use of genetic algorithms (GAs) to evolve computational processes in the form of computer programs. In GAs more generally, the individuals in a population may be represented and interpreted in a variety of ways, while in GP the individuals are usually treated as explicit computer programs written in a subset or variant of a conventional programming language. The overall algorithm of the GA is maintained in GP: search proceeds by iteratively evaluating the fitness of the individuals in the population and by applying genetic operators such as crossover and mutation to the higher-fitness individuals in order to explore other promising areas of the search space. In GAs more generally the fitness evaluation step can take many forms, while in GP an individual is evaluated for fitness at least in part by executing the program and by assessing the quality of its outputs. GP techniques have proven valuable for the evolution of structures other th...

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively rewriting them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.