Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Pawel B. Myszkowski, Maciej Laszczyk
Summary: The paper introduces a novel many-objective evolutionary method that aims to increase diversity and spread in the Pareto Front approximation. Experimental results show that guiding the evolution process towards less explored parts of a space can lead to increased diversity but may also increase convergence. The introduction of a novel selection operator is shown to circumvent the issue of existing diversity mechanisms in combinatorial spaces.
INFORMATION SCIENCES
(2021)
Article
Multidisciplinary Sciences
Xiyang Dong, Yongyi Peng, Muhua Wang, Laura Woods, Wenxue Wu, Yong Wang, Xi Xiao, Jiwei Li, Kuntong Jia, Chris Greening, Zongze Shao, Casey R. J. Hubert
Summary: Little is known about genetic heterogeneity within deep sea cold seep microbial populations. This study examines the intraspecies diversity patterns of 39 abundant species identified in sediment layers below the sea floor across six cold seep sites. The results reveal different evolutionary trajectories at the genomic level among these diverse populations and highlight the interplay between ecological processes and the evolution of key bacteria and archaea in deep sea cold seep extreme environments.
NATURE COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Xuemin Ma, Jingming Yang, Hao Sun, Ziyu Hu, Lixin Wei
Summary: This paper introduces a multiregional co-evolutionary dynamic multiobjective optimization algorithm, which effectively addresses the dynamic multiobjective optimization problems through a combination of multiregional prediction strategy and multiregional diversity maintenance mechanism, achieving good performance in experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Software Engineering
Per Kristian Lehre, Xiaoyu Qin
Summary: This article investigates the performance and parameter settings of non-elitist evolutionary algorithms on uncertain objective problems. By analyzing two classical benchmark problems, the study shows that non-elitist evolutionary algorithms outperform other methods in certain scenarios and provides more precise guidance on parameter selection.
Article
Multidisciplinary Sciences
Layla Hockerstedt, Elina Numminen, Ben Ashby, Mike Boots, Anna Norberg, Anna-Liisa Laine
Summary: This study found that isolated host populations are more affected by pathogen infection, while connected host populations have higher levels of resistance diversity. Spatial structure and host gene flow play important roles in the impacts of pathogens on hosts.
NATURE COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Tina Malalanirainy, Alberto Moraglio
Summary: This article introduces an evolutionary algorithm and improves it to adapt to different representations. The authors propose a more faithful generalization by introducing a new crossover operator. The article also discusses the application of this algorithm in specific metric spaces and provides a polynomial expected runtime upper bound for certain problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zhengxin Huang, Yuren Zhou
Summary: The study compared the expected runtime of a simple multi-objective evolutionary algorithm using different mutation operators, showing that immune-inspired hypermutation operators can always find the Pareto fronts in polynomial expected runtime and sometimes exponentially faster on certain problems. This analysis enhances understanding of immune-inspired hypermutation operators in solving multi-objective optimization problems and may be useful for designing efficient multi-objective evolutionary algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Agronomy
Joerg Peter Baresel, Lorenz Buelow, Maria Renate Finckh, Lothar Frese, Samuel Knapp, Urs Schmidhalter, Odette Weedon
Summary: The aim of this study was to investigate the potential of heterogeneous composite cross populations (CCPs) to increase sustainability and resilience of wheat cropping systems. The results showed that under organic conditions, CCPs had similar yields to commercial cultivars and higher yields than inbred lines, while under conventional conditions, conventionally-bred cultivars had higher yields. The CCPs exhibited higher yield stability and foliar disease resistance compared to commercial cultivars and inbred lines due to their high genetic diversity. The CCPs also showed differences in morphological and phenological traits, indicating adaptation to environmental conditions through natural selection.
Article
Chemistry, Multidisciplinary
Liang Jin, Xiao Zhang, Yilin Fang, Duc Truong Pham
Summary: This study proposes a task-based dynamic disassembly process model and a feature-based transfer learning-assisted evolutionary dynamic optimisation algorithm for the dynamic human-robot collaborative disassembly line balancing problem, effectively solving the optimization problem in dynamic environments.
APPLIED SCIENCES-BASEL
(2022)
Article
Food Science & Technology
Mohammad Majid al-Rifaie, Marc Cavazza
Summary: Modern computational techniques are used to personalize beer properties in craft beers, with an evolutionary computation technique mapping desired organoleptic properties to ingredients. The study introduces a solution discovery method suitable for complex industrial setups, and explores an automated quantitative ingredient-selection approach for the first time.
Article
Computer Science, Software Engineering
Dirk Sudholt
Summary: In this study, we analysed the performance of well-known evolutionary algorithms, the (1 + 1) EA and the (1 + similar to) EA, in the prior noise model. We discovered that the (1 + 1) EA on LeadingOnes is surprisingly sensitive to noise and showed that offspring populations of size similar to = 3.42 log n can effectively deal with much higher noise than previously known.
Article
Computer Science, Artificial Intelligence
Jialin Liu, Qingquan Zhang, Jiyuan Pei, Hao Tong, Xudong Feng, Feng Wu
Summary: This paper explores the engine calibration process by modeling a real aero-engine calibration problem as a many-objective optimization problem and proposing a fast many-objective evolutionary optimization algorithm. Comparisons with other optimization algorithms show that the fSDE algorithm exhibits better performance in terms of efficiency and quality.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Cell Biology
Worapong Singchat, Syed Farhan Ahmad, Kitipong Jaisamut, Thitipong Panthum, Nattakan Ariyaraphong, Ekaphan Kraichak, Narongrit Muangmai, Prateep Duengkae, Sunchai Payungporn, Suchinda Malaivijitnond, Kornsorn Srikulnath
Summary: This study investigated the genetic diversity in the centromeric region of long-tailed and rhesus macaques, revealing diverse subfamilies and interspecific variability. The study also found admixture patterns and high level polymorphisms within populations.
Article
Computer Science, Interdisciplinary Applications
Fei Wu, Jiacheng Chen, Wanliang Wang
Summary: In this paper, a prediction approach based on diversity screening and special point prediction (DSSP) is proposed to tackle the dynamic optimization issue. The approach includes a decision variable clustering and screening strategy as well as a method for predicting special points. Experimental results demonstrate the effectiveness of the proposed algorithm, DSSP.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, Christine Zarges
Article
Computer Science, Theory & Methods
Pietro S. Oliveto, Dirk Sudholt, Christine Zarges
THEORETICAL COMPUTER SCIENCE
(2019)
Article
Computer Science, Artificial Intelligence
Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Mustapha Lebbah
KNOWLEDGE AND INFORMATION SYSTEMS
(2020)
Article
Computer Science, Software Engineering
Zaineb Chelly Dagdia, Christine Zarges
Summary: In this paper, a new distributed RST version based on Locality Sensitive Hashing (LSH) is proposed, named LSH-dRST, for big data feature selection. LSH-dRST utilizes LSH to match similar features into the same bucket, enabling more efficient splitting of the universe.
FUNDAMENTA INFORMATICAE
(2021)
Proceedings Paper
Mathematics, Interdisciplinary Applications
Amanda Clare, Jacqueline W. Daykin, Thomas Mills, Christine Zarges
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION)
(2019)
Proceedings Paper
Mathematics, Interdisciplinary Applications
Jun He, Thomas Jansen, Christine Zarges
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Hanene Azzag, Mustapha Lebbah
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Thomas Jansen, Christine Zarges
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XV, PT II
(2018)
Article
Computer Science, Software Engineering
Pietro S. Oliveto, Tiago Paixao, Jorge Perez Heredia, Dirk Sudholt, Barbora Trubenova
Proceedings Paper
Computer Science, Artificial Intelligence
Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Mustapha Lebbah
2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Thomas Jansen, Christine Zarges
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIV
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Carola Doerr, Nicolas Bredeche, Enrique Alba, Thomas Bartz-Beielstein, Dimo Brockhoff, Benjamin Doerr, Gusz Eiben, Michael G. Epitropakis, Carlos M. Fonseca, Andreia Guerreiro, Evert Haasdijk, Jacqueline Heinerman, Julien Hubert, Per Kristian Lehre, Luigi Malago, J. J. Merelo, Julian Miller, Boris Naujoks, Pietro Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Patricia Ryser-Welch, Giovanni Squillero, Joerg Stork, Dirk Sudholt, Alberto Tonda, Darrell Whitley, Martin Zaefferer
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIV
(2016)
Proceedings Paper
Computer Science, Theory & Methods
Duc-Cuong Dang, Tobias Friedrich, Timo Koetzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton
GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
(2016)
Proceedings Paper
Computer Science, Theory & Methods
Per Kristian Lehre, Pietro S. Oliveto
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION)
(2016)
Article
Computer Science, Theory & Methods
Hoa T. Vu
Summary: This paper addresses the maximum satisfiability problem in the data stream model. It presents algorithms that achieve approximations to the optimum value and corresponding Boolean assignments in sublinear space complexity. The paper also discusses related problems and provides approximation algorithms and space complexity for them.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sameep Dahal, Jukka Suomela
Summary: This research demonstrates that finding a maximal fractional matching in distributed graph algorithms requires the use of fine-grained fractional values, and gives a specific characterization of the small value requirements, with a denominator of at least 2d. It also provides a new algorithm to meet these requirements.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Peng Yang, Yuan Huang, Zhiguo Fu
Summary: This paper investigates the computational complexity classification of Holant problems on 3-regular graphs and provides corresponding conclusions.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Yi-Jun Chang
Summary: This paper investigates the energy complexity of distributed graph problems in multi-hop radio networks. Recent studies have shown that some problems can be solved with energy-efficient algorithms, while others require significant energy consumption. To improve energy efficiency, this paper focuses on bounded-genus graphs and proposes corresponding algorithms.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Dekel Tsur
Summary: This paper proposes algorithms for 2-CLUB CLUSTER VERTEX DELETION and 2-CLUB CLUSTER EDGE DELETION problems. The algorithms have running times of O*(3.104k) and O*(2.562k) respectively, and were obtained using automated generation of branching rules. These results improve upon previous algorithms for the same problems.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xianrun Chen, Sai Ji, Chenchen Wu, Yicheng Xu, Yang Yang
Summary: This paper introduces the diversity-aware fair k-supplier problem and presents an efficient 5-approximation algorithm to address the fairness and over-representation issues in facility selection.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Masaaki Kanzaki, Yota Otachi, Giovanni Viglietta, Ryuhei Uehara
Summary: Sliding block puzzles play a crucial role in computational complexity, with their complexity varying depending on the rules and set of pieces. In this study, we explore the computational complexities of jumping block puzzles, a newer concept in the puzzle community. We analyze different variants of these puzzles based on real puzzles and a natural model, and determine their complexities. Our findings show that these puzzles are generally PSPACE-complete, with additional cases being NP-complete or solvable in polynomial-time.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Meghana Nasre, Prajakta Nimbhorkar, Keshav Ranjan, Ankita Sarkar
Summary: This paper studies the many-to-many bipartite matching problem with two-sided preferences and two-sided lower quotas. By defining the concept of critical matching, the goal is to find a popular matching in the set of critical matchings, and an efficient algorithm is proposed to compute a popular matching of the largest size.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rahul Jain, Raghunath Tewari
Summary: To solve the reachability problem is to determine if there is a path between two vertices in a graph. This paper studies space-efficient algorithms that run in polynomial time to decide reachability. An algorithm is presented that solves reachability in directed graphs using O(n log n) space.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Daniel Berend, Liat Cohen, Omrit Filtser
Summary: The Tower of Hanoi puzzle has been a fascination for mathematicians and theoretical computer scientists for over a century. By using graph theory, we can study connectivity, shortest paths, and other properties of the Hanoi puzzle. Additionally, the Hanoi graphs are related to interesting structures such as the Sierpinski gasket and Gray codes.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Ling Gai, Weiwei Zhang, Zhao Zhang
Summary: This paper studies the problem of selfish bin packing with punishment. Different types of punishments are considered, and it is shown that punishment based on the result can achieve better performance with an upper bound of approximately 1.48 compared to the optimal solution.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Di Wang, Jinhui Xu
Summary: This paper studies the problem of Differentially Private Empirical Risk Minimization (DP-ERM) with both convex and non-convex loss functions. Several new methods are proposed for DP-ERM with smooth convex loss functions, achieving near-optimal expected excess risks while reducing gradient complexity. For DP-ERM with non-convex loss functions, both low and high dimensional spaces are explored, and utility measurements are introduced using different norms and error bounds. The paper reveals that certain non-convex loss functions can be reduced to a level similar to convex loss functions.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Florian Bruse, Maurice Herwig, Martin Lange
Summary: This paper presents a weight measure for formal languages based on the summands of a geometric series discounted by the fraction of words of certain length in the language. It shows that this weight measure is computable for regular languages. As an application, a distance metric between languages is derived as the weight of their symmetric difference, which can be used in automatic grading of standard exercises in formal language theory classes.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Oliver Bachtler, Tim Bergner, Sven O. Krumke
Summary: By studying the almost disjoint paths problem and the separating by forbidden pairs problem, this paper reveals that they have an unbounded duality gap and analyzes their complexity. In addition, for a fixed value of k, an efficient algorithm is proposed to solve the ADP problem.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Elisabeth Remy, Paul Ruet
Summary: This paper studies the extension of the nested canalization property to multivalued functions and investigates the effect of the Van Ham mapping on this property. The study finds that the Van Ham mapping transforms softly nested canalizing multivalued functions into nested canalization Boolean functions, a property that is also relevant in the modeling of gene regulatory networks.
THEORETICAL COMPUTER SCIENCE
(2024)