A differential evolution algorithm in the optimization task with a Lipschitz continuous cost function

Knobloch, Roman

doi:10.1063/1.5082064

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Differential Evolutionary algorithms iteratively attempt to improve a candidate hyperparameter combinations by performing “crossover” with other candidate hyperparameters, resulting in novel combinations. Unlike other approaches, they use stochastic processes instead of gradient ones, which allow them to be effective in exploring high-dimensional search spaces (Knobloch, 2018; Storn & Price, 1997).…”

Section: Methodsmentioning

confidence: 99%

One-year predictions of delayed reward discounting in the adolescent brain cognitive development study.

Hahn¹,

Allgaier²,

MacKillop³

et al. 2022

Experimental and Clinical Psychopharmacology

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Steep discounting of delayed rewards is a factor in many behavioral problems and psychiatric disorders. The present study demonstrates that steepness of discounting can be reliably predicted 1 year in advance in 10/11-year-old children using a machine learning approach with an array of questionnaire and task data. Specific variables predictive of discounting include sociodemographic factors, cognitive ability, developmental history, screen media activity, impulsive personality traits, and social activities.

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Section: Methodsmentioning

confidence: 99%

One-year predictions of delayed reward discounting in the adolescent brain cognitive development study.

Hahn¹,

Allgaier²,

MacKillop³

et al. 2022

Experimental and Clinical Psychopharmacology

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show abstract

“…Because these algorithms are population-based stochastic algorithms, their solution quality is far superior to that of traditional optimization techniques because of fewer parameters, high convergence speed, simplicity implementation [7]. It can be divided into three categories: i) Evolutionary Algorithms (EA), like Genetic Algorithm (GA) [8], A Differential Evolution Algorithm (DE) [9]. ii) physics-based algorithms, like gravitational search algorithm (GSA) [10],Harmony Search Algorithm [11] and Simulated Annealing [12].iii) Swarm intelligence (SI)-based algorithm, like Particle Swarm (PSO) [13],…”

Section: Introductionmentioning

confidence: 99%

Solving Suggested Problems using Grey Wolf Optimization

Issa¹,

Alaa²,

Abed³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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In this research, the Grey Wolf Optimizer (GWO) algorithm was applied, which is a type of metaheuristic optimization algorithms that is important for identifying problems and finding ways to improve them. In 2014, Seyedali Mirjalili came up with this algorithm. Its social hierarchy is modeled after that of grey wolves in nature. These wolves live in groups consisting of( 5 -12) individuals. Wolves are divided into four levels, alpha represents the first level it is accountable for the manufacture of important resolutions for the peak like hunting, bedtime, wake up, etc. As for the second level in the hierarchy, it represents the beta wolf and is the advisor of the alpha. Beta can be the leader after the death of one of the alpha wolves. The third level represents delta, which follows the commands of alpha and beta. it is the dominant omega. omega represents the last level, which obeys all other wolves. Furthermore, the chief algorithm stages such as chasing, searching the prey, encircle and attack the prey were applied. GWO algorithm was tested on three benchmarks test functions using MATLAB R2014a. the results were confirmed by comparing GWO through another intelligent swarm algorithm like Particle Swarm Optimization (PSO) algorithm. results showed the superiority GWO in achieving better results and high convergence speed.

show abstract

A differential evolution algorithm in the optimization task with a Lipschitz continuous cost function

Cited by 2 publications

References 5 publications

One-year predictions of delayed reward discounting in the adolescent brain cognitive development study.

One-year predictions of delayed reward discounting in the adolescent brain cognitive development study.

Solving Suggested Problems using Grey Wolf Optimization

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