Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. John Holland's pioneering Adaptation in Natural and Artificial Systems (1975) described how an analog of the evolutionary process can be applied to solving mathematical problems and engineering optimization problems using what is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut

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

It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various human-written algorithms have appeared in the past two decades for the vexatious majority classification task for one-dimensional two-state cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy of 82.326%. This level of accuracy exceeds that of the original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other known rules produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate repertoire of domains and particles to represent and communicate information across the cellular space. 1.

This paper describes an automated process for designing electrical circuits in which "What You Want Is What You Get " ("WYWIWYG " – pronounced "wow-eee-wig"). The design process uses genetic programming to produce both the topology of the desired circuit and the sizing (numerical values) for all the components of a circuit. Genetic programming successfully evolves both the topology and the sizing for an asymmetric bandpass filter that was described as being difficult-to-design in a leading electrical engineering journal. This evolved circuit is another instance in which a genetically evolved solution to a non-trivial problem is competitive with human performance. 1.

This paper demonstrates the usefulness of automatically defined functions and architecture-altering operations in designing analog electrical circuits using genetic programming. A design for a lowpass filter is genetically evolved in which an automatically defined function is profitably reused in the 100 % compliant circuit. The symmetric reuse of an evolved substructure directly enhances the performance of the circuit. Genetic programming rediscovered the classical ladder topology used in Butterworth and Chebychev filters as well as the more complex topology used in Cauer (elliptic) filters. A design for a double-passband filter is genetically evolved in which the architecture-altering operations discover a suitable program architecture dynamically during the run. Two automatically defined functions are profitably reused in the genetically evolved 100 % complaint circuit. 1.

Abstract – It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domain-independent automatic programming technique in which "What You Want Is What You Get " ("WYWIWYG " – pronounced "woweee-wig"). Genetic programming is such a technique. This paper surveys three recent examples of problems (from the fields of cellular automata and molecular biology) in which genetic programming evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses the problem of electronic circuit synthesis in greater detail. It shows how genetic programming can evolve both the topology of a desired electrical circuit and the sizing (numerical values) for each component in a crossover (woofer and tweeter) filter. Genetic programming has also evolved the design for a lowpass filter, the design of an amplifier, and the design for an asymmetric bandpass filter that was described as being difficult-to-design in an article in a leading electrical engineering journal.

Susumu Ohno's provocative book Evolution by Gene Duplication proposed that the creation of new proteins in nature (and hence new structures and new behaviors in living things) begins with a gene duplication and that gene duplication is "the major force of evolution." This paper describes six new architecture-altering operations for genetic programming that are patterned after the naturally-occurring chromosomal operations of gene duplication and gene deletion. When these new operations are included in a run of genetic programming, genetic programming can dynamically change, during the run, the architecture of a multi-part program consisting of a main program and a set of hierarchically-called subprograms. These on-the-fly architectural changes occur while genetic programming is concurrently evolving the work-performing steps of the main program and the hierarchically-called subprograms. The new operations can be interpreted as an automated way to change the representation of a problem while solving the problem. Equivalently, these operations can be viewed as an automated way to decompose a problem into an non-pre-specified number of subproblems of non-pre-specified dimensionality; solve the subproblems; and assemble the solutions of the subproblems into a solution of the overall problem. These

This paper describes six new architecture-altering operations that provide a way to dynamically determine the architecture of a multipart program during a run of genetic programming. The new operations are patterned after the naturally occurring operations of gene duplication and gene deletion and are motivated by Ohno's provocative book Evolution by Means of Gene Duplication. The new operations are branch duplication, argument duplication, branch creation, argument creation, branch deletion, and argument deletion. These operations dynamically change the architecture of various programs during a run of genetic programming. The new operations can also be interpreted as providing an automated way to specialize and generalize programs. The paper demonstrates that problems can be solved while the architecture is being evolved.

Automated methods of machine learning may prove to be useful in discovering biologically meaningful information hidden in the rapidly growing databases of DNA sequences and protein sequences. Genetic programming is an extension of the genetic algorithm in which a population of computer programs is bred, over a series of generations, in order to solve a problem. Genetic programming is capable of evolving complicated problem-solving expressions of unspecified size and shape. Moreover, when automatically defined functions are added to genetic programming, genetic programming becomes capable of efficiently capturing and exploiting recurring sub-patterns. This chapter describes how genetic programming with automatically defined functions successfully evolved motifs for detecting the D-E-A-D box family of proteins and for 1 detecting the manganese superoxide dismutase family. Both motifs were evolved without prespecifying their length. Both evolved motifs employed automatically defined functions to capture the repeated use of common subexpressions. When tested against the SWISS-PROT database of proteins, the two genetically evolved consensus motifs detect the two families either as well, or slightly better than, the comparable human-written motifs found in the PROSITE database. 1.

A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326%. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it utilizes a finegrained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space. 1.