evolutionaryalgorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the proble...
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evolutionaryalgorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the problem classes of submodular optimization, which have a wide range of applications, such as maximum coverage, sparse regression, influence maximization, document summarization and sensor placement, just to name a few. Though they have provided some theoretical explanation for the general-purpose nature of EAs, the considered submodular objective functions are defined only over sets or multisets. To complement this line of research, this paper studies the problem class of maximizing monotone submodular functions over sequences, where the objective function depends on the order of items. We prove that for each kind of previously studied monotone submodular objective functions over sequences, i.e., prefix monotone submodular functions, weakly monotone and strongly submodular functions, and DAG monotone submodular functions, a simple multi-objective EA, i.e., GSEMO, can always reach or improve the best known approximation guarantee after running polynomial time in expectation. Note that these best-known approximation guarantees can be obtained only by different greedy-style algorithms before. Empirical studies on various applications, e.g., accomplishing tasks, maximizing information gain, search-and-tracking and recommender systems, show the excellent performance of the GSEMO.(c) 2022 Elsevier B.V. All rights reserved.
Recommender systems are beneficial in suggesting items to users by knowing their preferences and, therefore, effectively managing the vast amount of available information. Regarding the classical systems that focus on...
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ISBN:
(纸本)9783031686498;9783031686504
Recommender systems are beneficial in suggesting items to users by knowing their preferences and, therefore, effectively managing the vast amount of available information. Regarding the classical systems that focus on accuracy, the needs of their users have changed so much that many sometimes-conflicting performance measures now have to be taken into account. Recent research has enhanced the applicability of multi-objective evolutionary algorithms in recommender systems, balancing indicators such as accuracy with other essential ones. This survey provides a listing of recent works that applied MOEAs to the problem of recommender systems and pays special attention to critical areas, such as methodological approaches, goals, datasets, and evaluation strategies. This analysis, beyond the state-of-the-art synthesis, helps in the determination of the problems that are linked to the use of MOEAs and the prospects of the development of future research. The exploration targets aiding progress and innovation in this dynamic field.
Optimal portfolio selection-composing a set of stocks/assets that provide high yields/returns with a reasonable risk-has attracted investors and researchers for a long time. As a consequence, a variety of methods and ...
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Optimal portfolio selection-composing a set of stocks/assets that provide high yields/returns with a reasonable risk-has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users' preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor's preferences has proved to be very effective in the portfolio's composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation.
When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed ...
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When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling evolutionaryalgorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance-consisting of achieving the optimum period of time to carry out a preventive maintenance activity-are taken into account before being considered by the authors of the present paper. This work couples multi-objective evolutionary algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the multi-objectiveevolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed multi-objective evolutionary algorithms is handled;moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of multi-objective evolutionary algorithms and Discrete Event Simulation. Finally, the methodology is
In the domain of reliability engineering and risk assessment, the development of fault tree (FT) models is pivotal for decision-making in complex systems. Traditional FT model development, relying on manual efforts an...
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ISBN:
(纸本)9783031681493;9783031681509
In the domain of reliability engineering and risk assessment, the development of fault tree (FT) models is pivotal for decision-making in complex systems. Traditional FT model development, relying on manual efforts and expert collaboration, is both time-consuming and error-prone. The era of Industry 4.0 introduces capabilities for automatically deriving FTs from inspection and monitoring data. This paper presents FT-MOEA-CM, an extension of the FT-MOEA algorithm for inferring FT models from failure data using multi-objective optimization. FT-MOEA-CM enhances its predecessor by integrating confusion matrix-derived metrics and incorporating parallelization and caching mechanisms. Our evaluation on six FTs from diverse application areas showcases that FT-MOEA-CM exhibits (1) enhanced robustness, (2) faster convergence and (3) better scalability than FT-MOEA, suggesting its potential in efficiently inferring larger FT models.
This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancin...
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ISBN:
(纸本)9798350350685;9798350350678
This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multi-objectiveevolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool.
Some location problems with unreliable facilities present two different objectives, one consisting of minimizing the opening and transportation costs if none of the facilities fail and another consisting of minimizing...
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Some location problems with unreliable facilities present two different objectives, one consisting of minimizing the opening and transportation costs if none of the facilities fail and another consisting of minimizing the expected transportation costs. Usually, these different targets are combined in a single objective function and the decision maker can obtain some different solutions weighting both objectives. However, if the decision maker prefers to obtain a diverse set of non-dominated optimal solutions, then such procedure would not be effective. We have designed and implemented two multi-objective evolutionary algorithms for the realibility fixed-charge location problem by exploiting the peculiarities of this problem in order to obtain sets of solutions that are properly distributed along the Pareto-optimal frontier. The computational results demonstrate the outstanding efficiency of the proposed algorithms, although they present clear differences. (C) 2019 Elsevier B.V. All rights reserved.
作者：
Felipe Coello Castillo, CarlosA. Coello, CarlosUniversidad Autó Noma Metropolitana
Unidad Cuajimalpa Posgrado en Ciencias Naturales e Ingeniería Av. Vasco de Quiroga 4871 Col. Santa Fe Cuajimalpa Delegació CuajimalpaCiudad de Maxico Mexico05348 Mexico
Departamento de Computación Av. IPN No. 2508 Col. San Pedro Zacatenco Mexico Mexico
This paper presents a survey of applications of multi-objective evolutionary algorithms in several biotechnology areas. The application areas covered in the survey include: molecular docking, metabolic engineering, sy...
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The result diversification problem is to select an optimal subset with high “quality” and “diversity” from a given ground set of items, which is popular in various applications such as web-based search, multi-docu...
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Recent advances in applying deep generative learning to molecular design have led to a large number of novel approaches to the targeted generation of molecules towards specific features and applications. In this work,...
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ISBN:
(纸本)9783030726980;9783030726997
Recent advances in applying deep generative learning to molecular design have led to a large number of novel approaches to the targeted generation of molecules towards specific features and applications. In this work, we expand on the latent space navigation approach, where molecules are optimized by operating in their latent representation inside a deep auto-encoder, by introducing multi-objective evolutionary algorithms (MOEAs), and benchmarking the proposed framework on several objectives from recent literature. Using several case studies from literature, we show that our proposed method is capable of controlling abstract chemical properties, is competitive with other state-of-the-art methods and can perform relevant tasks such as optimizing a predefined molecule while maintaining a similarity threshold. Also, MOEAs allow to generate molecules with a good level of diversity, which is a desired feature.
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