Behavior of finite population variable length genetic algorithms under random selection

Stringer, Hal; Wu, Annie S.

doi:10.1145/1068009.1068213

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…In pursuit of a better coverage technique, a majority of scholars have tried to use intelligent algorithms, like Genetic Algorithm (GA) [18] and Particle Swarm Optimization (PSO) [19], to solve the issue. Though the Fruit Fly Optimization Algorithm is more simple and practicable than GA and PSO, but due to unavoidable limitations, the researchers are still exerting their efforts to develop a shrewder algorithm.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Ashraf

Alfandi

Ahmad³

et al. 2020

Wireless Communications and Mobile Computing

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Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

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Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Ashraf

Alfandi

Ahmad³

et al. 2020

Wireless Communications and Mobile Computing

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“…It processes a variable length string which can be underspecified or over specified. Application of Messy GA in feature selection has been widely reported(Whitley et al, 1997;Stringer and Annie, 2004;Hsiao, 2012).GA needs an encoding scheme for representing the individual messy gene which is collection of: Gene numbers and Allele values. Messy Chromosomes is collection of Messy Genes .The proposed Framework uses two representation schemes: Binary and Prufer for coding messy genes for showing the variation in results due to change the representation scheme.…”

mentioning

confidence: 99%

A New Hybrid Method to Solve the Multi-objective Optimization Problem for a Composite Hat-Stiffened Panel

Samrout¹,

Braydi²,

Younès³

et al. 2018

Bioinspired Heuristics for Optimization

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Algorithm portfolios are known to offer robust performances, efficiently overcoming the weakness of every single algorithm on some particular problem instances. Two complementary approaches to get the best out of an algorithm portfolio is to achieve algorithm selection (AS), and to define a scheduler, sequentially launching a few algorithms on a limited computational budget each. The presented Algorithm Selector And Prescheduler system relies on the joint optimization of a pre-scheduler and a per instance AS, selecting an algorithm well-suited to the problem instance at hand. ASAP has been thoroughly evaluated against the state-of-the-art during the ICON challenge for algorithm selection, receiving an honourable mention. Its evaluation on several combinatorial optimization benchmarks exposes surprisingly good results of the simple heuristics used; some extensions thereof are presented and discussed in the paper.This research work has been funded by the French Program "Investissements d'Avenir". 4 One often considers the joint problems of selecting an algorithm and the optimal hyper-parameters thereof, referred to as Algorithm Configuration (AC), as the choice of the hyper-parameter values governs the algorithm performance. AC is outside the scope of the paper and will not be further considered.2 sciencesconf.org:meta2016:108942 and reproducible benchmarking of AS approaches on 13 domains ranging from satisfiability to operations research (section 4).The comparative empirical validation of ASAP demonstrates its good performances comparatively to state-of-art pre-schedulers and AS approaches (section 5), and its complementarity with respect to the prominent Zilla algorithms [22]. The paper concludes with a discussion of the limitations of the ASAP approach, and some perspectives for further research.

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“…Concerning representation, the genome length can be taken as a variable during an evolutionary run [28,43,36,57]. Consider Ramsey et al [43] who investigate a variable length genome under different mutation rates.…”

Section: Representationmentioning

confidence: 99%

“…Stringer and Wu [57] show that a variable-length GA can evolve to shorter average size populations. This is observed when: 1) selection is absent from the GA, or 2) when selection focuses on some other property not influenced by the length of individuals.…”

Section: Representationmentioning

confidence: 99%

New Ways to Calibrate Evolutionary Algorithms

Eiben

Schut

Natural Computing Series

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Summary. The issue of setting the values of various parameters of an evolutionary algorithm (EA) is crucial for good performance. One way to do it is controlling EA parameters on-the-fly, which can be done in various ways and for various parameters. We briefly review these options in general and present the findings of a literature search and some statistics about the most popular options. Thereafter, we provide three case studies indicating a high potential for uncommon variants. In particular, we recommend to focus on parameters regulating selection and population size, rather than those concerning crossover and mutation. On the technical side, the case study on adjusting tournament size shows by example that global parameters can also be self-adapted, and that heuristic adaptation and pure self-adaptation can be successfully combined into a hybrid of the two.

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Behavior of finite population variable length genetic algorithms under random selection

Cited by 5 publications

References 9 publications

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

A New Hybrid Method to Solve the Multi-objective Optimization Problem for a Composite Hat-Stiffened Panel

New Ways to Calibrate Evolutionary Algorithms

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