On optimal population size of genetic algorithms

Alander, Jarmo T.

doi:10.1109/cmpeur.1992.218485

Cited by 127 publications

(80 citation statements)

References 2 publications

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“…Table 2 lists parameters chosen for the SPEA-II. The choice of population size is according to the suggestion by [11] on the population size of 1.5 log 2 N , where N is the number of possible permutations of the chromosome. All experiments were run remotely on a single rack mounted Dell Poweredge 2950 workstation, having 2 dualcore, hyperthreading 3.73 GHz Xeon processors and 24 GB of shared memory.…”

Section: Resultsmentioning

confidence: 99%

Heterogeneous multiprocessor mapping for real-time streaming systems

Lin

Srivatsa

Gerstlauer

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Real-time streaming signal processing systems typically desire high throughput and low latency. Many such systems can be modeled as synchronous data flow graphs. In this paper, we address the problem of multi-objective mapping of SDF graphs onto heterogeneous multi-processor platforms. The primary contributions include (1) an integer linear programming (ILP) model that globally optimizes throughput, latency and cost; (2) a low-complexity two-stage heuristic based on a combination of an evolutionary algorithm with an ILP to generate either a single sub-optimal mapping solution or a Pareto front for design space optimization. In our simulations, the proposed heuristic shows a 10 −6 gap from the ILP optimal solution, with up to 12x better run-time efficiency.

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Section: Resultsmentioning

confidence: 99%

Heterogeneous multiprocessor mapping for real-time streaming systems

Lin

Srivatsa

Gerstlauer

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…For example, enumeration of all designs for an -dimensional problem with settings for each variable would result in design options. Empirical research in genetic algorithms suggests that for optimal performance, generation sizes be set between and 2 for binary variables [12]. The proposed approach allows the user to vary the generation size from ten designs up to min (150,8 ), where is the length of the binary design vector equivalent to the -dimensional real-valued version.…”

Section: Generation Sizementioning

confidence: 99%

Combining structural performance and designer preferences in evolutionary design space exploration

Mueller

Ochsendorf

2015

Automation in Construction

100

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This paper addresses the need to consider both quantitative performance goals and qualitative requirements in conceptual design. A new computational approach for design space exploration is proposed that extends existing interactive evolutionary algorithms for increased inclusion of designer preferences, overcoming the weaknesses of traditional optimization that have limited its use in practice. This approach allows designers to set the evolutionary parameters of mutation rate and generation size, in addition to parent selection, in order to steer design space exploration. This paper demonstrates the potential of this approach through a numerical parametric study, a software implementation, and series of case studies.

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“…Population size : The population size is generally taken 5 to 100 [13][14][15][16]. Numerical experiments show that too large and too Small number of chromosomes in the population can lead to poor solutions [17].…”

Section: Genetic Algorithmmentioning

confidence: 99%

The Applicability of Genetic Algorithm in Cryptanalysis: A Survey

Khan¹,

Lone²,

Badroo³

2015

IJCA

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The Cryptanalysis has been the most fascinating area for science fraternity. The application of Genetic Algorithm (GAs) to the field of cryptanalysis is rather unique as no robust model for cryptanalysis using Genetic Algorithm exists. Genetic Algorithm (GAs) are the class of heuristic algorithm which are known for optimization and search problem. The paper presents the systematic review of Genetic Algorithm applied to the Cryptanalysis.

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On optimal population size of genetic algorithms

Cited by 127 publications

References 2 publications

Heterogeneous multiprocessor mapping for real-time streaming systems

Heterogeneous multiprocessor mapping for real-time streaming systems

Combining structural performance and designer preferences in evolutionary design space exploration

The Applicability of Genetic Algorithm in Cryptanalysis: A Survey

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