Using Quasi Random Sequences in Genetic Algorithms

Maaranen, Heikki; Miettinen, Kaisa; Mäkelä, Marko M.

doi:10.1007/978-94-017-2494-4_4

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…The PRIS offers a better performance at the very beginning of the optimization process (see [45]), but it is outperformed after 12,000 fitness evaluations by the QRIS. According to our interpretation, the QRIS generates a set of solutions that are more genotypically spread out than the ones generated by the PRIS.…”

Section: Initial Samplingmentioning

confidence: 98%

“…In order to find a compromise between the necessity of having spread out solutions and the impossibility of handling an initial population with an enormous size due to computational limitations, a Quasi-Random Initial Sampling (QRIS) [44], [45], [46] consisting of the following is proposed here: The range of variability of the genes having positions 1, 2, 3, 132, 133, and 134 is divided into three intervals, ½0; T =3, ½T =3 þ 1; 2=3 Á T , and ½2=3 Á T þ 1; T . This division individuates 3 6 ¼ 729 possible combinations of the intervals for these six genes.…”

Section: Initial Samplingmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies

Neri

Toivanen

Cascella

et al. 2007

IEEE/ACM Trans. Comput. Biol. and Bioinf.

104

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This paper proposes a period representation for modeling the multidrug HIV therapies and an Adaptive Multimeme Algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimization process. Furthermore, the AMmA makes use of an adaptation which dynamically sets the algorithmic parameters in order to prevent stagnation and premature convergence. The numerical results demonstrate that the application of the proposed algorithm leads to very efficient medication schedules which quickly stimulate a strong immune response to HIV. The earlier termination of the medication schedule leads to lesser unpleasant side effects for the patient due to strong antiretroviral therapy. A numerical comparison shows that the AMmA is more efficient than three popular metaheuristics. Finally, a statistical test based on the calculation of the tolerance interval confirms the superiority of the AMmA compared to the other methods for the problem under study.

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Section: Initial Samplingmentioning

confidence: 98%

Section: Initial Samplingmentioning

confidence: 99%

An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies

Neri

Toivanen

Cascella

et al. 2007

IEEE/ACM Trans. Comput. Biol. and Bioinf.

104

View full text Add to dashboard Cite

show abstract

“…Random initialization of sparrow individuals in the search space may lead to an uneven distribution among individuals, afecting the quality of the optimal global solution. Previous studies [40] have applied a quasirandom population initialization strategy to a genetic algorithm, and although the method was able to improve the quality of the fnal result, little change in the speed of convergence was observed. In this study, we use the tent chaos reverse learning initialization strategy to initialize the sparrow population, thereby increasing the speed of the algorithm in the global optimal search process.…”

Section: Tent Chaos Reverse Learning Initialization Strategymentioning

confidence: 99%

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

Song

Liu

Xue

et al. 2023

Mathematical Problems in Engineering

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Air strikes are among the main means of attack in modern warfare. To improve air defense capabilities and aid military decision-making, threat assessment models have been introduced. As the parameters of the kernel extreme learning machine (KELM) model need to be set individually, this study proposes a parameter learning strategy based on a multistrategy improved sparrow search algorithm (MISSA). First, a reasonable threat assessment model was established based on the capability and situation factors of air targets. Second, the sparrow search algorithm was improved in terms of population position initialization and position update strategy, incorporating tent chaos reverse learning, nonlinear inertia weights, a global search strategy, and adaptive t-distribution. The effectiveness of the MISSA strategy was verified using nine common benchmark functions. The results show that the proposed MISSA finds an effective balance between global and local searches. Moreover, when the MISSA is applied to solve the tuning problem of KELM, the values of mean absolute percentage error, mean square error, root mean square error, and mean absolute error for MISSA–KELM in the air target threat assessment problem are 2.013 × 10−2, 1.282 × 10−4, 1.132 × 10−2, and 8.316 × 10−3, respectively, all of which are higher than that of the other metaheuristic algorithms (e.g., ACWOA-KELM and HGWO-KELM). Therefore, the method proposed in this study can be used as a parameter-tuning tool for KELM, enabling KELM to perform better in practical applications.

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Statistical tests suites analysis methods. Cryptographic recommendations

Luengo¹

2023

Cryptologia

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Using Quasi Random Sequences in Genetic Algorithms

Cited by 5 publications

References 21 publications

An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies

An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

Statistical tests suites analysis methods. Cryptographic recommendations

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