Authors:
Wojciech Bożejko
1
and
Mieczyslaw Wodecki
2
Affiliations:
1
Wrocław University of Technology, Poland
;
2
University of Wrocław, Poland
Keyword(s):
Metaheuristics, Parallel computing, Evolutionary algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
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 function calculation) among a number of processors (slaves). This master-slave parallelization is regarded as easy in programming, which makes it popular with practitioners. Additionally, if the master processor keeps the population (and slave processors are used only as computational units for individuals fitness function evaluation), it explores the solution space in exactly the same manner as the sequential genetic algorithm. In this case we can say that we analyze the single-walk parallel genetic algorithm.