tailieunhanh - The Reproductive Plan Language RPL2: Motivation, Architecture and Applications

Hamilton and Clemens (1999) have provided estimates of genuine saving in a number of countries. Among the resources that make up natural capital, only forests, oil and minerals, and pollution were included (not included were such vital resources as water). So there is an undercount. Moreover, the accounting prices used to value natural capital were crudely estimated. Nevertheless, one has to start somewhere. The figures imply that sub-SaharanAfrica, theMiddle East, Pakistan, and Bangladesh have been depleting their capital assets over several decades: they are becoming poorer even if their GNP per capita are increasing. The data are far too crude to indicate if this has been the case as. | Appears in Genetic Algorithms in Optimisation Simulation and Modelling Eds J. Stender E. Hillebrand J. Kingdon IOS Press 1994. The Reproductive Plan Language RPL2 Motivation Architecture and Applications Nicholas J. Radcliffe and Patrick D. Surry njr pds @ Edinburgh Parallel Computing Centre King s Buildings University of Edinburgh Scotland EH9 3JZ Abstract. 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 that have used the system. 1 Motivation As evolutionary computing techniques acquire greater popularity and are shown to have ever wider application a number of trends have emerged. The emphasis of early work in genetic algorithms on low cardinality representations is diminishing as problem complexities increase and more natural data structures are found to be more convenient and effective. There is now extensive evidence both empirical and theoretical that the arguments for the superiority of binary representations were at least overstated. As the fields of genetic algorithms evolution strategies genetic programming and evolutionary .