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Combining evolutionary algorithms and Machine Learning Models in Landslide Susceptibility Assessments

REMOTE SENSING 2020年第23期12卷 3854-3854页

作者： Chen, Wei Chen, Yunzhi Tsangaratos, Paraskevas Ilia, Ioanna Wang, Xiaojing Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Minist Nat Resources Key Lab Coal Resources Explorat & Comprehens Util Xian 710021 Peoples R China Natl Tech Univ Athens Sch Min & Met Engn Dept Geol Sci Lab Engn Geol & Hydrogeol Zografos 15780 Greece

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.

关键词： landslide susceptibility feature selection optimizing structural parameters evolutionary algorithms genetic algorithms particle swarm optimization support vector machines artificial neural network

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evolutionary algorithms for solving unconstrained, constrained and multi-objective noisy combinatorial optimisation problems

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arXiv 2021年

作者： Aishwaryaprajna Rowe, Jonathan E. School of Computer Science University of Birmingham Birmingham United Kingdom The Alan Turing Institute London United Kingdom

We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as ONEMAX and other linear problems. We find that UMDA and the Paired-Crossover evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SUBSETSUM, KNAPSACK and SETCOVER. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multi-objective problems (COUNTING-ONESCOUNTINGZEROS and a multi-objective formulation of SETCOVER). We compare several adaptations of UMDA for multi-objective problems with the Simple evolutionary Multi-objective Optimiser (SEMO) and NSGAII. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms. © 2021, CC BY.

关键词： evolutionary algorithms

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When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms

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THEORETICAL COMPUTER SCIENCE 2020年 832卷 166-185页

作者： Corus, Dogan Oliveto, Pietro S. Yazdani, Donya Univ Sheffield Dept Comp Sci Rigorous Res Sheffield S1 4DP S Yorkshire England

We present a time complexity analysis of the Opt-IA artificial immune system (AIS). We first highlight the power and limitations of its distinguishing operators (i.e., hypermutations with mutation potential and ageing) by analysing them in isolation. Recent work has shown that ageing combined with local mutations can help escape local optima on a dynamic optimisation benchmark function. We generalise this result by rigorously proving that, compared to evolutionary algorithms (EAs), ageing leads to impressive speed-ups on the standard CLIFFd benchmark function both when using local and global mutations. Unless the stop at first constructive mutation (FCM) mechanism is applied, we show that hypermutations require exponential expected runtime to optimise any function with a polynomial number of optima. If instead FCM is used, the expected runtime is at most a linear factor larger than the upper bound achieved for any random local search algorithm using the artificial fitness levels method. Nevertheless, we prove that algorithms using hypermutations can be considerably faster than EAs at escaping local optima. An analysis of the complete Opt-IA reveals that it is efficient on the previously considered functions and highlights problems where the use of the full algorithm is crucial. We complete the picture by presenting a class of functions for which Opt-IA fails with overwhelming probability while standard EAs are efficient. (C) 2019 Published by Elsevier B.V.

关键词： Artificial immune systems Opt-IA Runtime analysis evolutionary algorithms Hypermutation Ageing

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Memetic algorithms outperform evolutionary algorithms in multimodal optimisation

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ARTIFICIAL INTELLIGENCE 2020年 287卷 103345-103345页

作者： Phan Trung Hai Nguyen Sudholt, Dirk Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

Memetic algorithms integrate local search into an evolutionary algorithm to combine the advantages of rapid exploitation and global optimisation. We provide a rigorous runtime analysis of memetic algorithms on the Hurdle problem, a landscape class of tunable difficulty with a "big valley structure", a characteristic feature of many hard combinatorial optimisation problems. A parameter called hurdle width describes the length of fitness valleys that need to be overcome. We show that the expected runtime of plain evolutionary algorithms like the (1+1) EA increases steeply with the hurdle width, yielding superpolynomial times to find the optimum, whereas a simple memetic algorithm, (1+1) MA, only needs polynomial expected time. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, it becomes easier for memetic algorithms. We further give the first rigorous proof that crossover can decrease the expected runtime in memetic algorithms. A (2+1) MA using mutation, crossover and local search outperforms any other combination of these operators. Our results demonstrate the power of memetic algorithms for problems with big valley structures and the benefits of hybridising multiple search operators. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Search heuristics Hybridisation evolutionary algorithms Black-box optimisation Memetic algorithms Time complexity

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Revising seismic behaviour factors for reinforced concrete bridge design in the longitudinal direction using multi-objective evolutionary algorithms

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BULLETIN OF EARTHQUAKE ENGINEERING 2020年第3期18卷 925-951页

作者： Camacho, Vitor T. Lopes, Mario Oliveira, Carlos S. IST Dept Civil Engn CERIS Av Rovisco Pais 1 P-1049001 Lisbon Portugal

Seismic behaviour factors represent the ratio between the strength of a structure, assuming it always maintains an elastic behaviour, and the strength demand with plastic behaviour and consequent loss of stiffness, at the seismic target displacement. This value is closely related to ductility and to energy dissipation due to hysteretic behaviour. The use of behaviour factors allows to design structures with elastic models, without having to explicitly account for material non-linearity while taking advantage of ductility. However, the definition of these values is not easy, and is dependent on several factors. In bridges, these factors can be, among others, regularity of the bridge in terms of pier height, concrete and steel quality, size of elements and amount of steel reinforcement, pier confinement, etc. These factors influence ductility demand and available ductility in different ways and through multi-objective optimization (MOO), the infrastructure solutions that maximize the use of the available ductility under a given earthquake action and for a given bridge superstructure, pier height scheme and ductility class according to Eurocode 8-part 2, can be obtained. Those optimized solutions, which are obtained through the minimization of steel and concrete in the piers as concurrent objectives, are associated with the maximum behaviour factors that can be used in the design of a given bridge and can be compared with the values recommended by EC8-part 2. Without loss of generality, the methodology is applied to a set of case-studies composed of RC bridges with four 30-m spans and circular piers, analysed in the longitudinal direction and without accounting for abutment effects. With the results from the MOO, the behaviour factors associated to solutions with different ductility levels and pier irregularity schemes are calculated and equations are derived, relating the obtained behaviour factors with a pier irregularity measure and ductility level. The results also

关键词： Seismic behaviour factors Multi-objective optimization evolutionary algorithms Bridge seismic design Non-linear static analysis

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An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第27-28期79卷 18941-18979页

作者： Belhaj Soulami, Khaoula Kaabouch, Naima Saidi, Mohamed Nabil Tamtaoui, Ahmed Natl Inst Posts & Telecommun INPT STRS Lab Rabat Morocco Univ North Dakota Elect Engn Dept Grand Forks ND 58202 USA Natl Inst Stat & Appl Econ INSEA Lab Informat Syst SI2M Rabat Morocco

Breast cancer is the most common cancer in women worldwide and the second main cause of cancer mortality after lung cancer. Up to now, there still no prevention nor early symptoms of breast cancer. Early detection can decrease significantly the mortality rate as the disease can be treated at an early stage. X-Ray is the current screening method that helps in detecting the most two common abnormalities of the breast, masses and micro-calcifications. However, interpreting mammograms is challenging in dense breasts as the abnormal masses and the normal glandular tissue of the breast have similar characteristics. Recently, the evolutionary algorithms have been widely used in image segmentation. In this paper, we evaluate and compare the performance of six most used evolutionary algorithms, invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), electromagnetism-like optimization (EMO), ant colony optimization (ACO), and artificial bee colony (ABC) in terms of clustering abnormal masses in the breast, particularly dense and extremely dense breasts. This evaluation is conducted based on quantitative metrics including Cohen's Kappa, correlation, and false positive and false negative rates. The evolutionary algorithms are then ranked based on two multi-criteria decision analysis methods, the Preference Ranking Organization Method for the Enrichment of Evaluations (PROMETHEE) and the Graphical Analysis for Interactive Aid (GAIA).

关键词： Mammograms Breast cancer Mass Segmentation Metaheuristics evolutionary algorithms

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Optimizing laser-plasma interactions for ion acceleration using particle-in-cell simulations and evolutionary algorithms

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NEW JOURNAL OF PHYSICS 2020年第10期22卷 1-11页

作者： Smith, Joseph R. Orban, Chris Morrison, John T. George, Kevin M. Ngirmang, Gregory K. Chowdhury, Enam A. Roquemore, W. Mel Ohio State Univ Dept Phys 174 W 18th Ave Columbus OH 43210 USA Innovat Sci Solut Inc Dayton OH USA Natl Acad Sci Engn & Med Washington DC USA US Air Force Res Lab Dayton OH USA Intense Energy Solut Inc Plain City OH USA Ohio State Univ Dept Mat Sci & Engn 116 W 19th Ave Columbus OH 43210 USA

The development of ultra-intense laser-based sources of high energy ions is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversion efficiency from laser energy to ion energy. We apply a new approach to this problem, in which we use an evolutionary algorithm to optimize conversion efficiency by exploring variations of the target density profile with thousands of one-dimensional particle-in-cell (PIC) simulations. We then compare this 'optimal' target identified by the one-dimensional PIC simulations to more conventional choices, such as with an exponential scale length pre-plasma, with fully three-dimensional PIC simulations. The optimal target outperforms the conventional targets in terms of maximum ion energy by 20% and show a noticeable enhancement of conversion efficiency to >2 MeV ions. This target geometry enhances laser coupling to the electrons, while still allowing the laser to strongly reflect from an effectively thin target. These results underscore the potential for this statistics-driven approach to guide research into optimizing laser-plasma simulations and experiments.

关键词： laser– plasma interaction particle-in-cell method ion acceleration evolutionary algorithms optimization

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Automatically Designing State-of-the-Art Multi- and Many-Objective evolutionary algorithms

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evolutionary COMPUTATION 2020年第2期28卷 195-226页

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Fed Rio Grande do Norte Inst Metropole Digital IMD Natal RN Brazil Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium

A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization. Our work is based on two main considerations. The first is that high-performing algorithms can be obtained from a configurable algorithmic framework in an automated way. The second is that multiple performance metrics may be required to guide this automatic design process. In the first part of this work, we extend our previously proposed algorithmic framework, increasing the number of MOEAs, underlying evolutionary algorithms, and search paradigms that it comprises. These components can be combined following a general MOEA template, and an automatic configuration method is used to instantiate high-performing MOEA designs that optimize a given performance metric and present state-of-the-art performance. In the second part, we propose a multiobjective formulation for the automatic MOEA design, which proves critical for the context of many-objective optimization due to the disagreement of established performance metrics. Our proposed formulation leads to an automatically designed MOEA that presents state-of-the-art performance according to a set of metrics, rather than a single one.

关键词： Multiobjective optimization evolutionary algorithms automatic algorithm design

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Optimizing neotissue growth inside perfusion bioreactors with respect to culture and labor cost: a multi-objective optimization study using evolutionary algorithms

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2020年第7期23卷 285-294页

作者： Mehrian, Mohammad Geris, Liesbet Univ Liege GIGA In Silico Med Biomech Res Unit Liege Belgium Katholieke Univ Leuven Div Skeletal Tissue Engn Prometheus Leuven Belgium Katholieke Univ Leuven Biomech Sect Leuven Belgium

Tissue engineering is a fast progressing domain where solutions are provided for organ failure or tissue damage. In this domain, computer models can facilitate the design of optimal production process conditions leading to robust and economically viable products. In this study, we use a previously published computationally efficient model, describing the neotissue growth (cells + their extracellular matrix) inside 3D scaffolds in a perfusion bioreactor. In order to find the most cost-effective medium refreshment strategy for the bioreactor culture, a multi-objective optimization strategy was developed aimed at maximizing the neotissue growth while minimizing the total cost of the experiment. Four evolutionary optimization algorithms (NSGAII, MOPSO, MOEA/D and GDEIII) were applied to the problem and the Pareto frontier was computed in all methods. All algorithms led to a similar outcome, albeit with different convergence speeds. The simulation results indicated that, given the actual cost of the labor compared to the medium cost, the most cost-efficient way of refreshing the medium was obtained by minimizing the refreshment frequency and maximizing the refreshment amount.

关键词： Multi-objective optimization evolutionary algorithms bone tissue engineering 3D scaffold labor cost

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Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface

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SWARM AND evolutionary COMPUTATION 2020年 52卷

作者： Tan, Ping Wang, Xin Wang, Yong Hunan Univ Technol & Business Sch Comp Sci & Informat Technol Changsha 410205 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

For the classification of motor imagery brain-computer interface (BCI) based on electroencephalography (EEG), appropriate features are crucial to obtain a high classification accuracy. Considering the characteristics of the EEG signals, the time-frequency-space three-dimensional features are extracted. Due to a considerable number of the extracted features, the performance of a classifier will degrade. Therefore, it is necessary to implement feature selection. However, existing feature selection methods are easy to fall into a local optimum of a high-dimensional feature selection problem. In this paper, a dimensionality reduction mechanism (called DimReM) is proposed, which gradually reduces the dimension of the search space by removing some unimportant features. In principle, DimReM transforms a high-dimensional feature selection problem into a low-dimensional one. DimReM does not introduce any additional parameters and its implementation is simple. To verify its effectiveness, DimReM is combined with different evolutionary algorithms and different classifiers to select features on various kinds of datasets. Compared with evolutionary algorithms without dimensionality reduction, their augmented versions equipped with DimReM can find feature subsets with higher classification accuracies while smaller numbers of selected features.

关键词： Brain-computer interface evolutionary algorithms Motor imagery Dimensionality reduction Feature selection

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