A Framework of Genetic Algorithm Improvement for Optimal Block Division in Lining Layout Planning

Badarudin, Ismadi; Sultan, Abu Bakar; Sulaiman, Md. Nasir; Mamat, Ali; Mohamed, Mahmud Tengku Muda

doi:10.3923/jai.2012.64.75

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Srivastava and Kim (2009) have used a genetic algorithm based solution to optimize software testing efficiency by identifying the most critical path clusters in a program, they did it by developing variable length Genetic algorithms that optimize and select the software path clusters which are weighted in accordance with the criticality of the path. Badarudin et al (2010) applied genetic algorithm in the agricultural domain to find the optimal shapes for space allocation for optimal Convergence is not so fast.…”

Section: Applicationsmentioning

confidence: 99%

Parallel Single and Multiple Objectives Genetic Algorithms

Mishra

Dehuri

Mall

et al. 2011

International Journal of Applied Evolutionary Computation

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This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution. However, some parallel single and multi-objective genetic algorithms converged to better solutions as compared to comparable sequential single and multiple objective genetic algorithms. The authors review several representative models for parallelizing single and multi-objective genetic algorithms. Further, some of the issues that have not yet been studied systematically are identified in the context of parallel single and parallel multi-objective genetic algorithms. Finally, some of the potential applications of parallel multi-objective GAs are discussed.

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Section: Applicationsmentioning

confidence: 99%

Parallel Single and Multiple Objectives Genetic Algorithms

Mishra

Dehuri

Mall

et al. 2011

International Journal of Applied Evolutionary Computation

View full text Add to dashboard Cite

show abstract

An algorithm for the selection of planting lining technique towards optimizing land Area: An algorithm for planting lining technique selection

Badarudin

Sultan

Sulaiman

et al. 2012

2012 4th Conference on Data Mining and Optimization (DMO)

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This paper presents the design of algorithm solution for selecting a planting lining technique. The three techniques with different planting lining direction lead to different number of trees, therefore the technique promotes the highest number of tree is optimal technique. Optimization refers to the maximum number for better area utilization. The huge possible solution and uncertain result make the problem complex and it requires an intelligent expect for the solution. The algorithm is designed based on two basic works in which to calculate number of trees and divide an area into blocks. This algorithm solution generated the dataset based coordinates areas to analyze the techniques. The result shows that for small area the technique to be chosen is inconsistent but in large area the technique-3 is preferred. The series of generate results by the algorithm is also reported in this paper.

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A Framework of Genetic Algorithm Improvement for Optimal Block Division in Lining Layout Planning

Cited by 2 publications

References 4 publications

Parallel Single and Multiple Objectives Genetic Algorithms

Parallel Single and Multiple Objectives Genetic Algorithms

An algorithm for the selection of planting lining technique towards optimizing land Area: An algorithm for planting lining technique selection

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