Abstract
Genetic algorithms (GA) mimic natural reproduction to search for complex problem solutions. Their principles are shortly explained. A point of interest is the regular and repetititve structure of computation involving communication, data exchanges, and control phases. Interaction with presentation and analysis tools is also a requirement. This make sense for the definition of a general framework allowing fast building of parallel applications in an object-oriented system. A GA workbench is developed using the Smalltalk-80 system with parallel machine code generation in mind.
- 1 D.E.Goldberg, Genetic Algorithms, in Search, Optimization & Machine Learning, Addison Wesley, 1989. Google ScholarDigital Library
- 2 E.G. Talbi, P.Bessiere, "A Parallel genetic algorithm for the graph partitioning problem" available as IMAG Report. (Contact bessiere@ima g.fr)Google Scholar
- 3 J.M.Filloque, E.Gauuin, B.Pottier, Efficient Global Computation on a Processor Network with Programmable Logic, ARLE91, LNCS 505, Springer-Verlag, June 91. Google ScholarDigital Library
Index Terms
An object-oriented environment for specification and concurrent execution of genetic algorithms
Recommendations
An object-oriented environment for specification and concurrent execution of genetic algorithms
OOPSLA '92: Addendum to the proceedings on Object-oriented programming systems, languages, and applications (Addendum)Genetic algorithms (GA) mimic natural reproduction to search for complex problem solutions. Their principles are shortly explained. A point of interest is the regular and repetititve structure of computation involving communication, data exchanges, and ...
A parallel execution environment for a sequential object oriented language
ICS '92: Proceedings of the 6th international conference on SupercomputingTo efficiently program massively parallel systems we propose to use a form of parallelism known as data parallelism along with a SPMD programming model. We describe how a sequential Object Oriented Language (OOL) can embed data parallelism in a clean ...
Genetic algorithms for task scheduling problem
The scheduling and mapping of the precedence-constrained task graph to processors is considered to be the most crucial NP-complete problem in parallel and distributed computing systems. Several genetic algorithms have been developed to solve this ...
Comments