METHOD OF ARTIFICIAL FITNESS LEVELS FOR DYNAMICS ANALYSIS OF EVOLUTIONARY ALGORITHMS

Subject of Research. Currently, in the theory of evolutionary computation, it becomes relevant to analyze not just the runtime of evolutionary algorithms, but also their dynamics. The two most common methods for dynamics analysis are: fixed-budget analysis, which studies an algorithm reachable fitne...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Maxim V. Buzdalov [verfasserIn] Dmitry V. Vinokurov [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Russisch

Erschienen:	2020

Schlagwörter:	evolutionary algorithms runtime analysis fixed-target analysis fixed-budget analysis

Übergeordnetes Werk:	In: Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki - Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University), 2016, 20(2020), 5, Seite 701-707
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:5 ; pages:701-707

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.17586/2226-1494-2020-20-5-701-707

Katalog-ID:	DOAJ019835191

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callnumber-first	Q - Science
author	Maxim V. Buzdalov
spellingShingle	Maxim V. Buzdalov misc QC350-467 misc QA75.5-76.95 misc evolutionary algorithms misc runtime analysis misc fixed-target analysis misc fixed-budget analysis misc Optics. Light misc Electronic computers. Computer science METHOD OF ARTIFICIAL FITNESS LEVELS FOR DYNAMICS ANALYSIS OF EVOLUTIONARY ALGORITHMS
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topic_title	QC350-467 QA75.5-76.95 METHOD OF ARTIFICIAL FITNESS LEVELS FOR DYNAMICS ANALYSIS OF EVOLUTIONARY ALGORITHMS evolutionary algorithms runtime analysis fixed-target analysis fixed-budget analysis
topic	misc QC350-467 misc QA75.5-76.95 misc evolutionary algorithms misc runtime analysis misc fixed-target analysis misc fixed-budget analysis misc Optics. Light misc Electronic computers. Computer science
topic_unstemmed	misc QC350-467 misc QA75.5-76.95 misc evolutionary algorithms misc runtime analysis misc fixed-target analysis misc fixed-budget analysis misc Optics. Light misc Electronic computers. Computer science
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title	METHOD OF ARTIFICIAL FITNESS LEVELS FOR DYNAMICS ANALYSIS OF EVOLUTIONARY ALGORITHMS
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author_sort	Maxim V. Buzdalov
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title_sort	method of artificial fitness levels for dynamics analysis of evolutionary algorithms
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title_auth	METHOD OF ARTIFICIAL FITNESS LEVELS FOR DYNAMICS ANALYSIS OF EVOLUTIONARY ALGORITHMS
abstract	Subject of Research. Currently, in the theory of evolutionary computation, it becomes relevant to analyze not just the runtime of evolutionary algorithms, but also their dynamics. The two most common methods for dynamics analysis are: fixed-budget analysis, which studies an algorithm reachable fitness in condition of operation time limit, and fixedtarget analysis, which studies the time that an algorithm needs to reach some fixed fitness value. Until now, theoretical studies were systematically carried out only for the first type of analysis. The present work is focused on removal of this disadvantage. Method. We proved the following theorem: if the bounds on optimization time for some evolutionary algorithm on some problem are already proven using artificial fitness levels, than the bounds on this algorithm dynamics on the considered problem derive automatically from the same preconditions. Main Results. Using this theorem, we obtain the upper bounds on fixed-target runtime for the following pairs of algorithms and problems: the family of (1 + 1) evolutionary algorithms on LeadingOnes and OneMax functions, and (μ + 1) evolutionary algorithm on OneMax. These bounds either repeat or refine the existing results, but in a much simpler way. Practical Relevance. The main practical achievement of this paper is that it simplifies the proving of bounds on the dynamics of evolutionary algorithms. In turn, these bounds could be more meaningful for choosing between different evolutionary algorithms for some problem than the time for reaching the optimum, as the latter is mostly infeasible in practice.
abstractGer	Subject of Research. Currently, in the theory of evolutionary computation, it becomes relevant to analyze not just the runtime of evolutionary algorithms, but also their dynamics. The two most common methods for dynamics analysis are: fixed-budget analysis, which studies an algorithm reachable fitness in condition of operation time limit, and fixedtarget analysis, which studies the time that an algorithm needs to reach some fixed fitness value. Until now, theoretical studies were systematically carried out only for the first type of analysis. The present work is focused on removal of this disadvantage. Method. We proved the following theorem: if the bounds on optimization time for some evolutionary algorithm on some problem are already proven using artificial fitness levels, than the bounds on this algorithm dynamics on the considered problem derive automatically from the same preconditions. Main Results. Using this theorem, we obtain the upper bounds on fixed-target runtime for the following pairs of algorithms and problems: the family of (1 + 1) evolutionary algorithms on LeadingOnes and OneMax functions, and (μ + 1) evolutionary algorithm on OneMax. These bounds either repeat or refine the existing results, but in a much simpler way. Practical Relevance. The main practical achievement of this paper is that it simplifies the proving of bounds on the dynamics of evolutionary algorithms. In turn, these bounds could be more meaningful for choosing between different evolutionary algorithms for some problem than the time for reaching the optimum, as the latter is mostly infeasible in practice.
abstract_unstemmed	Subject of Research. Currently, in the theory of evolutionary computation, it becomes relevant to analyze not just the runtime of evolutionary algorithms, but also their dynamics. The two most common methods for dynamics analysis are: fixed-budget analysis, which studies an algorithm reachable fitness in condition of operation time limit, and fixedtarget analysis, which studies the time that an algorithm needs to reach some fixed fitness value. Until now, theoretical studies were systematically carried out only for the first type of analysis. The present work is focused on removal of this disadvantage. Method. We proved the following theorem: if the bounds on optimization time for some evolutionary algorithm on some problem are already proven using artificial fitness levels, than the bounds on this algorithm dynamics on the considered problem derive automatically from the same preconditions. Main Results. Using this theorem, we obtain the upper bounds on fixed-target runtime for the following pairs of algorithms and problems: the family of (1 + 1) evolutionary algorithms on LeadingOnes and OneMax functions, and (μ + 1) evolutionary algorithm on OneMax. These bounds either repeat or refine the existing results, but in a much simpler way. Practical Relevance. The main practical achievement of this paper is that it simplifies the proving of bounds on the dynamics of evolutionary algorithms. In turn, these bounds could be more meaningful for choosing between different evolutionary algorithms for some problem than the time for reaching the optimum, as the latter is mostly infeasible in practice.
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title_short	METHOD OF ARTIFICIAL FITNESS LEVELS FOR DYNAMICS ANALYSIS OF EVOLUTIONARY ALGORITHMS
url	https://doi.org/10.17586/2226-1494-2020-20-5-701-707 https://doaj.org/article/b74ecf057a504bf893032c0eae9a4628 https://ntv.ifmo.ru/file/article/19939.pdf https://doaj.org/toc/2226-1494 https://doaj.org/toc/2500-0373
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