Multi-Pareto-Ranking Evolutionary Algorithm

Bloch, Christelle; Charlet, Damien; Spies, François

doi:10.1007/978-3-642-29124-1_17

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…MOEA/D-M2M [31,25] MOEA/D-PSF [25] MOEA/D-MSF [25] LiuLi [52,1] DMOEA-DD [52,1] COEA HEIA BCE MTS [52,1] problems were not available during the development of many of these algorithms, and only the top performing algorithms eventually being tested over a large range of test problems. There is a prevalence towards testing on unconstrained problems, with all but the GAME and aGAME tested on these problems, with more limited testing on the constrained and imbalanced problems.…”

Section: The Requirement For General Algorithmsmentioning

confidence: 99%

“…1, where darker circles indicate fitter individuals and darker yellow rectangles indicate higher collective fitness. The new algorithm, competitor algorithms and benchmarking are coded in C++ 1 .…”

Section: Novel Co-evolutionary Approachmentioning

confidence: 99%

“…In cMLSGA there is an additional step where half of the collectives utilise the selected "Evolutionary Strategy 1" (ES1) subroutine, and the other half the 1 The code is available online with detailed instructions at:…”

Section: Novel Co-evolutionary Approachmentioning

confidence: 99%

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cMLSGA: A Co-Evolutionary Multi-Level Selection Genetic Algorithm for Multi-Objective Optimization

Grudniewski,

Sobey

2021

Preprint

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In practical optimisation the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialised approach to each application. The hybrid form of Multi-Level Selection Genetic Algorithm (MLSGA) already shows good performance on range of problems due to its diversity-first approach, which is rare among Evolutionary Algorithms. To increase the generality of its performance this paper proposes a distinct set of co-evolutionary mechanisms, which defines co-evolution as competition between collectives rather than individuals. This distinctive approach to co-evolutionary provides less regular communication between sub-populations and different fitness definitions between individuals and collectives. This encourages the collectives to act more independently creating a unique sub-regional search, leading to the development of co-evolutionary MLSGA (cMLSGA). To test this methodology nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The new mechanisms are tested on over 100 different functions and benchmarked against the 9 state-of-the-art competitors in order to find the best general solver. The results show that the diversity of co-evolutionary approaches is more important

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Section: The Requirement For General Algorithmsmentioning

confidence: 99%

Section: Novel Co-evolutionary Approachmentioning

confidence: 99%

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cMLSGA: A Co-Evolutionary Multi-Level Selection Genetic Algorithm for Multi-Objective Optimization

Grudniewski,

Sobey

2021

Preprint

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“…In line with these observations, an automatic mapping distillation technique is presented in Reference [67] which operates as follows: The original set of Pareto-optimal mappings is first projected into the space of resource-related objectives where Pareto ranking [72] is used to sort the mappings. Then, the mappings are projected into the space of quality objectives where a grid-based selection scheme is employed to retain mappings from different regions of the quality space (ensuring diverse quality trade-offs) based on the previously computed Pareto ranks (ensuring resource efficiency and diversity).…”

Section: Mapping Distillationmentioning

confidence: 99%

Hybrid Application Mapping for Composable Many-Core Systems: Overview and Future Perspective

Pourmohseni

Glaß

Henkel

et al. 2020

JLPEA

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Many-core platforms are rapidly expanding in various embedded areas as they provide the scalable computational power required to meet the ever-growing performance demands of embedded applications and systems. However, the huge design space of possible task mappings, the unpredictable workload dynamism, and the numerous non-functional requirements of applications in terms of timing, reliability, safety, and so forth. impose significant challenges when designing many-core systems. Hybrid Application Mapping (HAM) is an emerging class of design methodologies for many-core systems which address these challenges via an incremental (per-application) mapping scheme: The mapping process is divided into (i) a design-time Design Space Exploration (DSE) step per application to obtain a set of high-quality mapping options and (ii) a run-time system management step in which applications are launched dynamically (on demand) using the precomputed mappings. This paper provides an overview of HAM and the design methodologies developed in line with it. We introduce the basics of HAM and elaborate on the way it addresses the major challenges of application mapping in many-core systems. We provide an overview of the main challenges encountered when employing HAM and survey a collection of state-of-the-art techniques and methodologies proposed to address these challenges. We finally present an overview of open topics and challenges in HAM, provide a summary of emerging trends for addressing them particularly using machine learning, and outline possible future directions. While there exists a large body of HAM methodologies, the techniques studied in this paper are developed, to a large extent, within the scope of invasive computing. Invasive computing introduces resource awareness into applications and employs explicit resource reservation to enable incremental application mapping and dynamic system management.

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“…In the following discussion, we use the terms solution and individual to mean the same thing, since individuals in the population represent solutions to the problem that is being optimized. In non-dominated sorting genetic algorithm (NSGA-II) no niching parameter is required [3,14]. And to find out density of the solution crowding distance is used.…”

Section: Non-dominated Sorting Genetic Algorithm (Nsga-ii)mentioning

confidence: 99%

Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms

Chand¹,

Mohanty²

2013

IJCA

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Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multi objective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm for big problem gives less efficient results, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in giving good results but, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which gives better efficient results. The improved algorithm is applied to the transportation problem: vehicle routing problem (VRP). Results of comparative tests are presented showing that the improved algorithm performs well on large populations.

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Multi-Pareto-Ranking Evolutionary Algorithm

Cited by 9 publications

References 15 publications

cMLSGA: A Co-Evolutionary Multi-Level Selection Genetic Algorithm for Multi-Objective Optimization

cMLSGA: A Co-Evolutionary Multi-Level Selection Genetic Algorithm for Multi-Objective Optimization

Hybrid Application Mapping for Composable Many-Core Systems: Overview and Future Perspective

Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms

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