Human-competitive results produced by genetic programming

Abstract

Genetic programming has now been used to produce at least 76 instances of results that are competitive with human-produced results. These human-competitive results come from a wide variety of fields, including quantum computing circuits, analog electrical circuits, antennas, mechanical systems, controllers, game playing, finite algebras, photonic systems, image recognition, optical lens systems, mathematical algorithms, cellular automata rules, bioinformatics, sorting networks, robotics, assembly code generation, software repair, scheduling, communication protocols, symbolic regression, reverse engineering, and empirical model discovery. This paper observes that, despite considerable variation in the techniques employed by the various researchers and research groups that produced these human-competitive results, many of the results share several common features. Many of the results were achieved by using a developmental process and by using native representations regularly used by engineers in the fields involved. The best individual in the initial generation of the run of genetic programming often contains only a small number of operative parts. Most of the results that duplicated the functionality of previously issued patents were novel solutions, not infringing solutions. In addition, the production of human-competitive results, as well as the increased intricacy of the results, are broadly correlated to increased availability of computing power tracked by Moore's law. The paper ends by predicting that the increased availability of computing power (through both parallel computing and Moore's law) should result in the production, in the future, of an increasing flow of human-competitive results, as well as more intricate and impressive results.

Searching for Novel Classifiers

“…Research in Evolutionary Computation (EC) has produced search and optimization algorithms that frequently achieve promising new results in diverse domains [4]. Therefore, the practical value of evolutionary algorithms (EAs) is by now widely accepted.…”

Speeding up global optimization with the help of intelligent supervisors

“…Many human-competitive results produced using runs of genetic programming with a developmental process are described in [25]. An idea of intelligent supervisors capable of solving a specific case of the RCPSP which consists in efficient rescheduling of the project tasks by using limited renewable resources was introduced in [37].…”

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

“…Whereas this paper proposes a new approach of process and controller data based automatic rule generation via GP. It is worth mentioning that early researches shows that GP is capable of producing human competitive PID like controller topology along with its parameters and successfully applied in process control applications like [40]- [50]. Genetic programming [40] is a class of computational intelligence techniques which extends the notion of the conventional Genetic Algorithm, to evolve computer programs which can perform user defined tasks.…”