The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or serial hardware while hiding parallelism from the user. This paper surveys structured population models, explains and motivates the benefits of generic systems such as RPL2 and describes the suite of applications that have used it to date.

The reproductive plan language RPL2 is a computer language designed to facilitate the writing, execution and modification of evolutionary algorithms. It provides a number of data parallel constructs appropriate to evolutionary computing, facilitating the building of efficient parallel interpreters and compilers. This facility is exploited by the current interpreted implementation. RPL2 supports all current structured population models and their hybrids at language level. Users can extend the system by linking against the supplied framework C-callable functions, which may then be invoked directly from an RPL2 program. There are no restrictions on the form of genomes, making the language particularly well suited to real-world optimisation problems and the production of hybrid algorithms. This paper describes the theoretical and practical considerations that shaped the design of RPL2, the language, interpreter and run-time system built, and a suite of industrial applications...

. Abstract. The availability of faster and cheaper parallel computers makes it possible to apply genetic algorithms to large populations and very complex applications. This report presents a survey of current implementation techniques for genetic algorithms on parallel hardware. 1. Introduction 1. Introduction Genetic Algorithms (GA) are stochastic search and optimization techniques which were inspired by the analogy of evolution and population genetics. They have been demonstrated to be effective and robust in searching very large, varied, spaces in a wide range of applications. During recent years the area of Genetic Algorithms in general and the field of Parallel Genetic Algorithms (PGA) in particular has matured up to a point, where the application to a complex real-world problem in some applied science is now potentially feasible and to the benefit of both fields. The effectiveness of GAs, is limited by their ability to balance the need for a diverse set of sampling points...