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...

The application of a genetic algorithm to an industrial pipe-sizing problem is presented. The optimum sizes for the pipes in a given network with specified supply and demand requirements must be determined, subject to two additional constraints. The problem was used as a test case for the evolutionary computing language RPL2. The genetic approach is shown to produce better results than the existing industrial heuristic at the expense of longer run times. 1 Problem Specification It is a frequent criticism of genetic algorithms and evolutionary computation more generally that published problems are usually contrived and unconstrained. (There are of course notable exceptions, for instance the work of Goldberg (1989) on gas network compressor optimisation.) This paper demonstrates how a relatively straightforward implementation of a genetic algorithm can find significantly better solutions than the standard heuristics actually used for a real, constrained pipe-sizing problem to offe...

The application of a genetic algorithm to an industrial pipe-sizing problem is presented. The optimum sizes for the pipes in a given network with specified supply and demand requirements must be determined, subject to two additional constraints. The problem was used as a test case for the evolutionary computing language RPL2. The genetic approach is shown to produce better results than the existing industrial heuristic at the expense of longer run times.