Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima

Plessis, Mathys C. du; Engelbrecht, Andries P.

doi:10.1007/978-3-642-30665-5_7

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Self-adaptation has been popular for some time in the field of evolutionary computation [7,22,33], having notable success in the area of evolution strategies [25]. In a review of evolutionary self-adaptation, Kramer [25] noted that currently self-adaptation mechanisms most often involve modification of mutation step sizes or varying the type of mutation employed [29].…”

Section: Previous Workmentioning

confidence: 99%

Balancing exploration and exploitation in genetic programming using inversion with individualized self-adaptation

Fitzgerald

Ryan

2014

HIS

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In this article we explore and develop a holistic scheme of self adaptive, individualized genetic operators combined with an adaptive tournament size together with a novel implementation of an inversion genetic operator which is suitable for tree based Genetic Programming. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive Genetic Programming implementation. Our results also demonstrate that an inversion operator may have a useful role to play in exploitation, particularly when used towards the end of evolution to facilitate gradual convergence of the learning system towards a good solution.

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Section: Previous Workmentioning

confidence: 99%

Balancing exploration and exploitation in genetic programming using inversion with individualized self-adaptation

Fitzgerald

Ryan

2014

HIS

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“…The tracking performance are measured by moving peaks benchmark (MPB) [11,24], which is implemented in [30]. MPB is a maximizing problem with a multimodal function, which has a global optimum and some local optima.…”

Section: Conditionsmentioning

confidence: 99%

“…3.2. We apply the proposed memory update procedure to Nelder-Mead method, PSO, bare born PSO (BBPSO) [23], APSO and OPRC, then, compare the tracking performance for a well-known dynamic benchmark problem: moving peaks benchmark (MPB) [11,24]. As the results, OPRC tracked the optima with the best accuracy among them.…”

Section: Introductionmentioning

confidence: 99%

Tracking optima in dynamic problems by an optimizer based on piecewise-rotational chaos system

Tsubone

2018

NOLTA

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Tracking optima in dynamic problems is achieved by a multi-population optimizer based on piecewise-rotational chaotic system (OPRC) using memory update within a tolerance. Tracking optima is a difficult task for multi-population-based optimizers because of two issues, called outdated memory and divergent loss. To solve the outdated memory issue, a simple procedure named memory update within a tolerance is proposed. The proposed procedure is applied to our previous proposed optimizer OPRC, and its outstanding tracking performance is observed. This result shows OPRC can solve the divergent loss issue without any modification of its searching dynamics. The tracking mechanism of OPRC is also considered, and it is uncovered that the searching behavior given by the folding dynamics of the chaotic system contributes to catch the shifting optima.

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WD2O: a novel wind driven dynamic optimization approach with effective change detection

Boulesnane

Meshoul

2017

Appl Intell

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Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima

Cited by 6 publications

References 26 publications

Balancing exploration and exploitation in genetic programming using inversion with individualized self-adaptation

Balancing exploration and exploitation in genetic programming using inversion with individualized self-adaptation

Tracking optima in dynamic problems by an optimizer based on piecewise-rotational chaos system

WD2O: a novel wind driven dynamic optimization approach with effective change detection

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