Inductive linkage identification on building blocks of different sizes and types

Chen, Ying-ping; Chuang, Chung-Yao; Yuan-wei, Huang

doi:10.1080/00207721.2011.566639

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…We further extend the classical building block hypothesis within the ETO learning settings to confirm the existence of positional building blocks (BB) [7][8][9][24][25][26][27] in whole chromosomes, whether head, middle or tail (the first surgical knife of a building block).…”

mentioning

confidence: 82%

Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization

Wang

Guo

et al. 2023

Processes

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Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer optimization (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” is continuous and is firstly performed by Professors L. Feng and H.D. Wang, and “4” are firstly proposed by our group for discrete cases. As a new computational paradigm, theoretical insights into SMOs are relatively rare now. Therefore, we present a proposal on the fine brushworks of SMOs for theoretical advances here, which is based on a case study of a permutation flow shop scheduling problem (PFSP) in manufacturing systems via lenses of building blocks, transferring gaps, auxiliary task and asynchronous rhythms. The empirical studies on well-studied benchmarks enrich the rough strokes of SMOs and guide future designs and practices in ETO based manufacturing scheduling, and even ETO based evolutionary processes for engineering optimization in other cases.

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mentioning

confidence: 82%

Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization

Wang

Guo

et al. 2023

Processes

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“…The behavior of these algorithms for other decomposable problems has been also studied from the perspective of ADFs [46,148]. Linkage identification methods have been analyzed using ADFs [19,24,128,165,190].…”

Section: Class Of Problemsmentioning

confidence: 99%

Gray-box optimization and factorized distribution algorithms: where two worlds collide

Santana

2017

Preprint

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The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves about the idea of exploiting the problem structure to implement more efficient evolutionary algorithms (EAs). Work on factorized distribution algorithms (FDAs), whose factorizations are directly derived from the problem structure, has also contributed to show how exploiting the problem structure produces important gains in the efficiency of EAs. In this paper we analyze the general question of using problem structure in EAs focusing on confronting work done in gray-box optimization with related research accomplished in FDAs. This contrasted analysis helps us to identify, in current studies on the use problem structure in EAs, two distinct analytical characterizations of how these algorithms work. Moreover, we claim that these two characterizations collide and compete at the time of providing a coherent framework to investigate this type of algorithms. To illustrate this claim, we present a contrasted analysis of formalisms, questions, and results produced in FDAs and gray-box optimization. Common underlying principles in the two approaches, which are usually overlooked, are identified and discussed. Besides, an extensive review of previous research related to different uses of the problem structure in EAs is presented. The paper also elaborates on some of the questions that arise when extending the use of problem structure in EAs, such as the question of evolvability, high cardinality of the variables and large definition sets, constrained and multi-objective problems, etc. Finally, emergent approaches that exploit neural models to capture the problem structure are covered.

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Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW

Wang

Guo

et al. 2022

Mathematics

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Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW.

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Inductive linkage identification on building blocks of different sizes and types

Cited by 3 publications

References 29 publications

Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization

Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization

Gray-box optimization and factorized distribution algorithms: where two worlds collide

Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW

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