On Proportions of Fit Individuals in Population of Mutation-Based Evolutionary Algorithm with Tournament Selection

Eremeev, Anton V.

doi:10.1162/evco_a_00210

Cited by 17 publications

(4 citation statements)

References 34 publications

(61 reference statements)

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“…These feature subsets were then optimized for high AUC scores as determined by the leave-out site CV using the five sites that include both cases and controls. Multi-objective optimization was conducted with the aid of a number of successful GA strategies, and these include: random tournament selection (20), feature set mutations, repeated runs with isolated populations, a sparsity objective similar in function to “Age-fitness Pareto optimization” (21), among others. An introduction to GA and a complete description of our design decisions regarding the algorithm are provided in the supplemental material.…”

Section: Methodsmentioning

confidence: 99%

Predicting Alcohol Dependence from Multi-Site Brain Structural Measures

Hahn

Mackey

Cousijn

et al. 2020

Preprint

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Background The search for neuroimaging biomarkers of alcohol use disorder (AUD) has primarily been restricted to significance testing in small datasets of low diversity. To identify neurobiological markers beyond individual differences, it may be useful to develop classification models for AUD. The ever-increasing quantity of neuroimaging data demands methods that are robust to the complexities of multi-site designs and are generalizable to data from new scanners. Methods This study represents a mega-analysis of previously published datasets from 2,034 AUD and comparison participants spanning 27 sites, coordinated by the ENIGMA Addiction Working Group. Data were grouped into a training set including 1,652 participants (692 AUD, 24 sites), and test set with 382 participants (146 AUD, 3 sites). A battery of machine learning classifiers was evaluated using repeated random cross-validation (CV) and leave-site-out CV. Area under the receiver operating characteristic curve (AUC) was our base metric of performance. Results Multi-objective evolutionary search was conducted to identify sparse, generalizable, and high performing subsets of brain measurements. Cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume, appeared most frequently across searches. Restricting a regularized logistic regression model to these four features yielded a test-set AUC of .768. Conclusions Developing classification models on multi-site data with varied underlying class distributions poses unique challenges. Supplementing datasets with controls from new sites and performing feature selection increases generalizability. Four features identified by evolutionary search may serve as specific biomarkers for individuals with current AUD.

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Section: Methodsmentioning

confidence: 99%

Predicting Alcohol Dependence from Multi-Site Brain Structural Measures

Hahn

Mackey

Cousijn

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…С обширным перечнем известных результатов и их обсуждением можно ознакомиться в обзорах [54,55]. Среди российских ученых стоит отметить В. Редько [56,57], Е. Семенкина [58,59], С. Родзина [60,61], А. Еремеева [62][63][64][65].…”

Section: Introductionunclassified

The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm

Bassin

Buzdalov

Shalyto

2021

Aut. Control Comp. Sci.

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Self-adjustment of parameters can signi cantly improve the performance of evolutionary algorithms. A notable example is the (1 + ( , )) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the "one-h rule" on problems with weak tness-distance correlation. We propose a modi cation of the "one-h rule" in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modi cation, while still yielding a provable linear runtime on OneMax, shows be er results on linear function with random weights, as well as on random satis able MAX-3SAT problems.

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Section: Introductionunclassified

The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Басин

Буздалов

Шалыто

2020

Model. anal. inf. sist.

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Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ,λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation.We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear function with random weights, as well as on random satisfiable MAX-3SAT problems.

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On Proportions of Fit Individuals in Population of Mutation-Based Evolutionary Algorithm with Tournament Selection

Cited by 17 publications

References 34 publications

Predicting Alcohol Dependence from Multi-Site Brain Structural Measures

Predicting Alcohol Dependence from Multi-Site Brain Structural Measures

The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm

The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

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