Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application

Seada, Haitham; Deb, Kalyanmoy

doi:10.1007/978-981-13-1471-1_1

Cited by 17 publications

(6 citation statements)

References 50 publications

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“…Examples of pseudocode are provided in (Akdemir and Sanchez 2016) and the Genomic Mating R package manual (2018). It is important to note that the parameters values in the GA algorithm can be optimized and the set of solutions in the pareto-front can be explored for better solutions by other methods such as NSGA-II, NSGA-III, SPEA-1, SPEA-2 and other recent improved versions of GA for better convergence rate and quality of solutions, determined by the proximity to global optimum (Deb 2011; Seada and Deb 2018) (Supplementary Figure1).…”

Section: Methodsmentioning

confidence: 99%

Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

Ramasubramanian

Beavis

2021

Preprint

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Plant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize loss of useful genetic variance. For commercial plant breeders competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS retains useful genetic diversity during the early cycles of selection. With limited resources must a soybean breeder choose between the two extreme responses provided by GS or PS? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs and whether the breeding population should be organized as family islands. For breeding populations organized into islands decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

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Section: Methodsmentioning

confidence: 99%

Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

Ramasubramanian

Beavis

2021

Preprint

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“…SPEA-2 and other recent improved versions of GAs for better convergence rate and quality of solutions, determined by the proximity to global optimum (Deb, 2011;Seada and Deb, 2018; Supplementary Figure 1).…”

Section: Genomic Mating In Populations Organized As Family Islandsmentioning

confidence: 99%

Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

Ramasubramanian

Beavis²

2021

Front. Genet.

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Plant breeding is a decision-making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize the rate of genetic improvement and minimize the loss of useful genetic variance. For commercial plant breeders, competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast, public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing the loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short-term genetic gains from genomic selection are much greater than phenotypic selection, while phenotypic selection provides better long-term genetic gains because it retains useful genetic diversity during the early cycles of selection. With limited resources, must a soybean breeder choose between the two extreme responses provided by genomic selection or phenotypic selection? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs, and whether the breeding population should be organized as family islands. For breeding populations organized into islands, decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to 10 cycles using genomic selection and a hub network mating design, where the hub parents with the largest selection metric make large parental contributions. It also requires that the breeding populations be organized as fully connected family islands, where every island is connected to every other island, and migration rules allow the exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except that the mating design could be hub network, chain rule, or a multi-objective optimization method-based mating design. Weighted genomic selection applied to centralized populations also resulted in the realization of the greatest proportion of the genetic potential of the founders but required more cycles than the best compromise strategy.

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“…31 Evolutionary algorithms are effective solvers for multi/many-objective optimisation problems. [31][32][33] Non-dominated sorting based algorithm (NSGA-III) proposed by Deb and Jain 16 is the state-of-the-art method for MaOPs. They use a set of reference points uniformly distributed across a normalised hyper-plane to handle the exponentially increasing number of non-dominated sets in the selection phase.…”

Section: Optimisationmentioning

confidence: 99%

Recommending healthy meal plans by optimising nature-inspired many-objective diet problem

Türkmenoğlu

Uyar²,

Kiraz

2021

Health Informatics J

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Healthy eating is an important issue affecting a large part of the world population, so human diets are becoming increasingly popular, especially with the devastating consequences of Coronavirus Disease (Covid-19). A realistic and sustainable diet plan can help us to have a healthy eating habit since it considers most of the expectations from a diet without any restriction. In this study, the classical diet problem has been extended in terms of modelling, data sets and solution approach. Inspired by animals’ hunting strategies, it was re-modelled as a many-objective optimisation problem. In order to have realistic and applicable diet plans, cooked dishes are used. A well-known many-objective evolutionary algorithm is used to solve the diet problem. Results show that our approach can optimise specialised daily menus for different user types, depending on their preferences, age, gender and body index. Our approach can be easily adapted for users with health issues by adding new constraints and objectives. Our approach can be used individually or by dietitians as a decision support mechanism.

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Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application

Cited by 17 publications

References 50 publications

Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

Recommending healthy meal plans by optimising nature-inspired many-objective diet problem

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