EasyGenetic: A Template Metaprogramming Framework for Genetic Master-Slave Algorithms
نویسندگان
چکیده
In this work we present EasyGenetic, a genetic solver based on template metaprogramming, that enables the user to configure the solver via templates. The framework allows to combine flexibility with efficiency. The framework is designed to be applied to problems for which a master-slave solution strategy can be defined. In the realm of combinatorial optimization, such problems can be those for which a parametrized constructive procedure is available and the solver search the parameter space. We present two successful applications of EasyGenetic to hard optimization problems, namely the Haplotype Inference Problem and the Capacitated Vehicle Routing Problem.
منابع مشابه
Design of PID Controller for Teleopration System with Genetic Algorithm
This paper presents a novel teleoperation controller for a nonlinear master–slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the pas...
متن کاملThe Design and Implementation of MPI Master-Slave Parallel Genetic Algorithm
In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic genetic algorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of maximum value problems (Rosen brocks function) .And we acquire the factors influencing the master-slave parallel genetic algorithm by deriving from the analysis of test data. The expe...
متن کاملSolving Signal Coordination Problems Using Master- Slave Genetic Algorithms
This paper presents the design of master-slave genetic algorithms (GA) in solving signal coordination problems. When a serial GA is applied, its performance in terms of computation time diminishes as more accurate results (smaller time slices to evaluate flows and queues) of network performances are needed, or the size of signal networks increases. Because GA works with a population of independ...
متن کاملImplementing Fast and Flexible Parallel Genetic Algorithms
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use better algorithms and more eecient implementations to reach good solutions fast. This chapter describes the implementation of master-slave and multiple-population parallel GAs. The goal of the chapter is to help others to implement their own parallel codes. To this eeect, the text discusses some of th...
متن کاملSingle-walk Parallelization of the Genetic Algorithm
Abstract: This paper aims at presenting theoretical properties which can be used to approximate the theoretical speedup of parallel genetic algorithms. The most frequently parallelization method employed to genetic algorithm implements a master-slave model by distributing the most computationally exhausting elements of the algorithm (usually evaluation of the fitness function, i.e. cost functio...
متن کامل