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Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems

APPLIED SOFT COMPUTING 2014年第0期24卷 432-446页

作者： Guzek, Mateusz Pecero, Johnatan E. Dorronsoro, Bernabe Bouvry, Pascal Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg Univ Luxembourg Fac Sci Technol & Commun Luxembourg Luxembourg Univ Lille 1 Lab Informat Fondamentale Lille F-59655 Villeneuve Dascq France

The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy. This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms. The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance. (C) 2014 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms Multi-objective optimization Scheduling Energy efficiency Dynamic voltage scaling

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Large language models as surrogate models in evolutionary algorithms: A preliminary study

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SWARM AND evolutionary COMPUTATION 2024年 91卷

作者： Hao, Hao Zhang, Xiaoqun Zhou, Aimin Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China East China Normal Univ Shanghai Inst AI Educ Shanghai 200062 Peoples R China

Large Language Models (LLMs) have demonstrated remarkable advancements across diverse domains, manifesting considerable capabilities in evolutionary computation, notably in generating new solutions and automating algorithm design. Surrogate-assisted selection plays a pivotal role in evolutionary algorithms (EAs), especially in addressing expensive optimization problems by reducing the number of real function evaluations. However, whether LLMs can serve as surrogate models remains an unknown. In this study, we propose a novel surrogate model based purely on LLM inference capabilities, eliminating the need for training. Specifically, we formulate model-assisted selection as a classification problem or a regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data. This involves predicting whether a solution is good or bad, or approximating its value. This approach is then integrated into EAs, termed LLM-assisted EA (LAEA). Detailed experiments compared the visualization results of 2D data from 9 mainstream LLMs, as well as their performance on 5-10 dimensional problems. The experimental results demonstrate that LLMs have significant potential as surrogate models in evolutionary computation, achieving performance comparable to traditional surrogate models only using inference. This work offers new insights into the application of LLMs in evolutionary computation. Code is available at: https://***/hhyqhh/***.

关键词： Large language models Black-box optimization Surrogate models evolutionary algorithms

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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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Analysing the Robustness of evolutionary algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial

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ALGORITHMICA 2021年第4期83卷 976-1011页

作者： Sudholt, Dirk Univ Sheffield Sheffield S1 4DP S Yorkshire England

We analyse the performance of well-known evolutionary algorithms, the (1 + 1) EA and the (1 + similar to) EA, in the prior noise model, where in each fitness evaluation the search point is altered before the evaluation with probability p. We present refined results for the expected optimisation time of these algorithms on the function -LeadingOnes, where bits have to be optimised in sequence. Previous work showed that the (1 + 1) EA on LeadingOnes runs in polynomial expected time if p = O((log n)/n2) and needs superpolynomial expected time if p = similar to((log n)/n), leaving a huge gap for which no results were known. We close this gap by showing that the expected optimisation time is similar to(n2) . exp(similar to(min{pn2, n})) for all p = 1/2, allowing for the first time to locate the threshold between polynomial and superpolynomial expected times at p = similar to((log n)/n2). Hence the (1 + 1) EA on -LeadingOnes is surprisingly sensitive to noise. We also show that offspring populations of size similar to = 3.42 log n can effectively deal with much higher noise than known before. Finally, we present an example of a rugged landscape where prior noise can help to escape from local optima by blurring the landscape and allowing a hill climber to see the underlying gradient. We prove that in this particular setting noise can have a highly beneficial effect on performance.

关键词： evolutionary algorithms Noisy optimisation Robustness Runtime analysis Theory Uncertainty

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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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Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms

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ATMOSPHERIC ENVIRONMENT 2010年第31期44卷 3787-3796页

作者： Cervone, Guido Franzese, Pasquale Grajdeanu, Adrian George Mason Univ Dept Geog & Geoinformat Sci Fairfax VA 22030 USA George Mason Univ Ctr Earth Observing & Space Res Fairfax VA 22030 USA George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source. The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Source characterization evolutionary algorithms Monte Carlo Dispersion modeling

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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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A population initialization method for evolutionary algorithms based on clustering and Cauchy deviates

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 60卷 294-310页

作者： Bajer, Drazen Martinovic, Goran Brest, Janez JJ Strossmayer Univ Osijek Fac Elect Engn Kneza Trpimira 2b Osijek 31000 Croatia Univ Maribor Fac Elect Engn & Comp Sci Smetanova Ul 17 SLO-2000 Maribor Slovenia

The initial population of an evolutionary algorithm is an important factor which affects the convergence rate and ultimately its ability to find high quality solutions or satisfactory solutions for that matter. If composed of good individuals it may bias the search towards promising regions of the search space right from the beginning. Although, if no knowledge about the problem at hand is available, the initial population is most often generated completely random, thus no such behavior can be expected. This paper proposes a method for initializing the population that attempts to identify i.e., to get close to promising parts of the search space and to generate (relatively) good solutions in their proximity. The method is based on clustering and a simple Cauchy mutation. The results obtained on a broad set of standard benchmark functions suggest that the proposed method succeeds in the aforementioned which is most noticeable as an increase in convergence rate compared to the usual initialization approach and a method from the literature. Also, insight into the usefulness of advanced initialization methods in higher-dimensional search spaces is provided, at least to some degree, by the results obtained on higher-dimensional problem instances-the proposed method is beneficial in such spaces as well. Moreover, results on several very high-dimensional problem instances suggest that the proposed method is able to provide a good starting position for the search. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cauchy deviates Clustering Differential evolution evolutionary algorithms Initial population Mutation

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A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第5期37卷 3853-3862页

作者： Chan, K. Y. Kwong, C. K. Jiang, H. Aydin, M. E. Fogarty, T. C. Curtin Univ Technol Digital Ecosyst & Business Intelligence Inst Perth WA Australia Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Bedfordshire Dept Comp & Informat Syst Luton Beds England London S Bank Univ Fac Business Comp & Informat Management London England

Recent research shows that orthogonal array based crossovers outperform standard and existing crossovers in evolutionary algorithms in solving parametrical problems with high dimensions and multi-optima. However, those crossovers employed so far, ignore the consideration of interactions between genes. In this paper, we propose a method to improve the existing orthogonal array based crossovers by integrating information of interactions between genes. It is empirically shown that the proposed orthogonal array based crossover outperforms significantly both the existing orthogonal array based crossovers and standard crossovers on solving parametrical benchmark functions that interactions exist between variables. To further compare the proposed orthogonal array based crossover with the existing crossovers in evolutionary algorithms, a validation test based on car door design is used in which the effectiveness of the proposed orthogonal array based crossover is studied. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Crossover evolutionary algorithms Interactions between genes Orthogonal array Car door design

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EFFECTS OF ADAPTIVE SOCIAL NETWORKS ON THE ROBUSTNESS OF evolutionary algorithms

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2011年第5期20卷 783-817页

作者： Whitacre, James M. Sarker, Ruhul A. Pham, Q. Tuan Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ New S Wales Australian Def Force Acad Sch Informat Technol & Elect Engn Canberra ACT 2600 Australia Univ New S Wales Sch Chem Sci & Engn Sydney NSW 2052 Australia

Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the influence of component fitness and community cohesion (modularity) have attracted interest from the scientific community. In this study, we apply these concepts to an evolutionary algorithm and allow its population to self-organize using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based topological operators for guiding network structural dynamics, which in turn are guided by population changes taking place over evolutionary time. To investigate the effect on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and panmictic evolutionary algorithm designs. Our results suggest that a self-organizing topology evolutionary algorithm can exhibit robust search behavior with strong performance observed over short and long time scales. More generally, the coevolution between a population and its topology may constitute a promising new paradigm for designing adaptive search heuristics.

关键词： evolutionary algorithms network evolution optimization adaptive population topology self-organization

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