Comparative Study of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial (NP) Problems

Hasan, Basima Hani; Mustafa, Moutaz Saleh

doi:10.1109/isms.2011.11

Cited by 6 publications

(6 citation statements)

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“…Hasil simulasi menujukkan bahwa jumlah populasi berperan penting dalam perolehan fitness terbaik.  Pemaparan perbandingan dari berbagai proses/metode mutasi algoritma genetika dalam penyelesaian 0/1 Knapsack problem oleh Hasan, dkk dibahas di [3]. Dari 50 kali percobaan yang dilakukan proses mutasi dengan inversi menghasilkan hasil dengan rata-rata tertinggi meski perbedaan performa secara umum tidak terlalu berbeda dibandingkan metode mutasi lainnya yang dipaparkan dalam artikel tersebut.…”

Section: Pendahuluanunclassified

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Optimasi Pemilihan Barang Dagangan bagi Pedagang Keliling dengan Algoritma Genetika

Iryanto

Ismantohadi

2017

jtt

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AbstrakAdanya kendala meningkatkan tingkat kesulitan dalam melakukan pemilihan barang, tidak terkecuali bagi seorang pedagang keliling. Tentunya seorang pedagang ingin mengoptimalkan keuntungan yang diperolehnya. Namun keterbatasan tempat dan modal menjadikan pedagang tersebut memerlukan strategi khusus dalam proses pemilihan barang yang dibeli dan dijajakannya. Terlebih lagi suatu barang dagangan memiliki peluang yang berbeda untuk terjual sehari-harinya. Hal tersebut semakin menambah kerumitan pemilihan tersebut. Dalam artikel ini, masalah tersebut diselesaikan dengan menggunakan algoritma genetika. Hasil percobaan dan solusi analitik menunjukkan kesesuaian yang baik. Percobaan dengan N-jenis barang juga dipaparkan dalam artikel ini. AbstractExistence of constraints increases difficulty in choosing the right goods for a seller. Indeed, the seller wants to optimize the profit gained. Due to limitation of capital and maximum capacity, the seller needs a certain strategy to do the selection. Moreover the fact that each goods has its own probability to sell makes the problem becomes more complex. In this paper, the problem is solved using genetic algorithm. The result of simulation is in a good agreement with analytical solution. The simulation of N-goods selection is also given in the paper.

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Section: Pendahuluanunclassified

“…Merujuk hasil penelitian yang disajikan di [3], algoritma genetika yang digunakan adalah algoritma genetika biner dengan proses mutasi inversi. Sedangkan dari referensi [7], nantinya akan digunakan jumlah populasi yang cukup besar dalam proses pencarian solusi dengan algoritma genetika tersebut.…”

Section: Pendahuluanunclassified

Optimasi Pemilihan Barang Dagangan bagi Pedagang Keliling dengan Algoritma Genetika

Iryanto

Ismantohadi

2017

jtt

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“…The performance of different mutation operators highly depends on the parameter choice of genetic algorithms (Brizuela and Aceves 2003 ; Osaba et al. 2014 ; Wang and Zhang 2006 ) and the type of problems (Hasan and Saleh 2011 ; Karthikeyan et al. 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems

Liu

Kroll

2016

SpringerPlus

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Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-016-3027-2) contains supplementary material, which is available to authorized users.

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“…Some studies suggest a combination of the mutation operators [64] or the self-adaptive mutation operators [65][66][67]. e efficiency of various mutation operators depends on the GA parameters [53,68,69] and problem type [70,71]. However, the number of various mutation operators with real encoding for continuous problems is very limited.…”

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K-Means Genetic Algorithms with Greedy Genetic Operators

Kazakovtsev

Rozhnov

Shkaberina

et al. 2020

Mathematical Problems in Engineering

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The k-means problem is one of the most popular models of cluster analysis. The problem is NP-hard, and modern literature offers many competing heuristic approaches. Sometimes practical problems require obtaining such a result (albeit notExact), within the framework of the k-means model, which would be difficult to improve by known methods without a significant increase in the computation time or computational resources. In such cases, genetic algorithms with greedy agglomerative heuristic crossover operator might be a good choice. However, their computational complexity makes it difficult to use them for large-scale problems. The crossover operator which includes the k-means procedure, taking the absolute majority of the computation time, is essential for such algorithms, and other genetic operators such as mutation are usually eliminated or simplified. The importance of maintaining the population diversity, in particular, with the use of a mutation operator, is more significant with an increase in the data volume and available computing resources such as graphical processing units (GPUs). In this article, we propose a new greedy heuristic mutation operator for such algorithms and investigate the influence of new and well-known mutation operators on the objective function value achieved by the genetic algorithms for large-scale k-means problems. Our computational experiments demonstrate the ability of the new mutation operator, as well as the mechanism for organizing subpopulations, to improve the result of the algorithm.

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Comparative Study of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial (NP) Problems

Cited by 6 publications

References 14 publications

Optimasi Pemilihan Barang Dagangan bagi Pedagang Keliling dengan Algoritma Genetika

Optimasi Pemilihan Barang Dagangan bagi Pedagang Keliling dengan Algoritma Genetika

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems

K-Means Genetic Algorithms with Greedy Genetic Operators

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