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On the Structural Shape Optimization through Variational Methods and evolutionary algorithms

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2011年第4期18卷 225-243页

作者： Fraternali, Fernando Marino, Andrea El Sayed, Tamer Della Cioppa, Antonio Univ Salerno Dept Civil Engn I-84100 Salerno Italy CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA Univ Salerno Dept Informat & Elect Engn Salerno Italy

We employ the variational theory of optimal control problems and evolutionary algorithms to investigate the form finding of minimum compliance elastic structures. Mathematical properties of ground structure approaches are discussed with reference to arbitrary collections of structural elements. A numerical procedure based on a Breeder Genetic Algorithm is proposed for the shape optimization of discrete structural models. Several numerical applications are presented, showing the ability of the adopted search strategy in avoiding local optimal solutions. The proposed approach is validated against a parade of results available in the literature.

关键词： shape optimization optimal control ground structures compliance optimization evolutionary algorithms

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Parameter estimation based on stacked regression and evolutionary algorithms

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 1999年第5期146卷 406-414页

作者： Hong, X Billings, SA Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (OLS) algorithm. The PRESS prediction errors ale used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

关键词： stacked regression MDE evolutionary algorithms statistical analysis Simulation, modelling and identification Optimisation techniques parameter estimation sunspot time series linear-in-the-parameter models Canadian lynx time series OLS algorithm evolutionary computation minimisation mean dispersion error Interpolation and function approximation (numerical analysis) cross-validation parsimony concise model structure PRESS prediction errors Other topics in statistics cross-validated prediction error minimisation least squares approximations forward orthogonal least-squares algorithm

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DOPGA: a new fitness assignment scheme for multi-objective evolutionary algorithms

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第3期45卷 407-426页

作者： Ergul, Engin Ufuk Eminoglu, Ilyas Amasya Univ Dept Elect & Elect Engn Fac Technol TR-05100 Amasya Turkey Ondokuz Mayis Univ Dept Elect & Elect Engn Fac Engn TR-55189 Samsun Turkey

In this article, a new fitness assignment scheme to evaluate the Pareto-optimal solutions for multi-objective evolutionary algorithms is proposed. The proposed DOmination Power of an individual Genetic Algorithm (DOPGA) method can order the individuals in a form in which each individual (the so-called solution) could have a unique rank. With this new method, a multi-objective problem can be treated as if it were a single-objective problem without drastically deviating from the Pareto definition. In DOPGA, relative position of a solution is embedded into the fitness assignment procedures. We compare the performance of the algorithm with two benchmark evolutionary algorithms (Strength Pareto evolutionary Algorithm (SPEA) and Strength Pareto evolutionary Algorithm 2 (SPEA2)) on 12 unconstrained bi-objective and one tri-objective test problems. DOPGA significantly outperforms SPEA on all test problems. DOPGA performs better than SPEA2 in terms of convergence metric on all test problems. Also, Pareto-optimal solutions found by DOPGA spread better than SPEA2 on eight of 13 test problems.

关键词： fitness assignment domination power DOPGA evolutionary algorithms SPEA and SPEA2 test functions

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Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report

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COMPUTERS & OPERATIONS RESEARCH 2010年第3期37卷 481-497页

作者： Lozano, M. Garcia-Martinez, C. Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain

Nowadays, a promising way to obtain hybrid metaheuristics concerns the combination of several search algorithms with strong specialization in intensification and/or diversification. The flexible architecture of evolutionary algorithms allows specialized models to be obtained with the aim of providing intensification and/or diversification. The outstanding role that is played by evolutionary algorithms at present justifies the choice of their specialist approaches as suitable ingredients to build hybrid metaheuristics. This paper focuses on hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification. We first give an overview of the existing research on this topic, describing several instances grouped into three categories that were identified after reviewing specialized literature. Then, with the aim of complementing the overview and providing additional results and insights on this line of research, we present an instance that consists of an iterated local search algorithm with an evolutionary perturbation technique. The benefits of the proposal in comparison to other iterated local search algorithms proposed in the literature to deal with binary optimization problems are experimentally shown. The good performance of the reviewed approaches and the suitable results shown by our instance allow an important conclusion to be achieved: the use of evolutionary algorithms specializing in intensification and diversification for building hybrid metaheuristics becomes a prospective line of research for obtaining effective search algorithms. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Hybrid metaheuristics evolutionary algorithms Intensification and diversification Iterated local search

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

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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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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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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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