Relevant bibliographies by topics / Evolutionary Algorithms (EAs)

Academic literature on the topic 'Evolutionary Algorithms (EAs)'

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Published: 10 December 2022
Last updated: 16 July 2024

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Journal articles on the topic "Evolutionary Algorithms (EAs)"

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Mashwani, Wali Khan, Zia Ur Rehman, Maharani A. Bakar, Ismail Koçak, and Muhammad Fayaz. "A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems." Complexity 2021 (March 10, 2021): 1–24. http://dx.doi.org/10.1155/2021/5515701.

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Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.
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Dao, Tran Trong. "Investigation on Evolutionary Computation Techniques of a Nonlinear System." Modelling and Simulation in Engineering 2011 (2011): 1–21. http://dx.doi.org/10.1155/2011/496732.

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The main aim of this work is to show that such a powerful optimizing tool like evolutionary algorithms (EAs) can be in reality used for the simulation and optimization of a nonlinear system. A nonlinear mathematical model is required to describe the dynamic behaviour of batch process; this justifies the use of evolutionary method of the EAs to deal with this process. Four algorithms from the field of artificial intelligent—differential evolution (DE), self-organizing migrating algorithm (SOMA), genetic algorithm (GA), and simulated annealing (SA)—are used in this investigation. The results show that EAs are used successfully in the process optimization.
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Sivadasan, J., M. Willjuice Iruthayarajan, Albert Alexander Stonier, and A. Raymon. "Design of Cross-Coupled Nonlinear PID Controller Using Single-Objective Evolutionary Algorithms." Mathematical Problems in Engineering 2023 (April 15, 2023): 1–13. http://dx.doi.org/10.1155/2023/7820047.

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The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real-Coded genetic algorithm with simulated binary crossover (RGA-SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross-coupled nonlinear PID controllers is compared in this paper. A cross-coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross-coupled nonlinear PID controller using single-objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross-coupled system clearly show that a single-objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off-line. However, only the single-objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.
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Bäck, Thomas, and Hans-Paul Schwefel. "An Overview of Evolutionary Algorithms for Parameter Optimization." Evolutionary Computation 1, no. 1 (March 1993): 1–23. http://dx.doi.org/10.1162/evco.1993.1.1.1.

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Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.
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Heredia, Jorge Pérez. "Modelling Evolutionary Algorithms with Stochastic Differential Equations." Evolutionary Computation 26, no. 4 (December 2018): 657–86. http://dx.doi.org/10.1162/evco_a_00216.

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There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.
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Chen, Yaxin, Xin Shen, Guo Zhang, and Zezhong Lu. "Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning." Remote Sensing 15, no. 16 (August 8, 2023): 3932. http://dx.doi.org/10.3390/rs15163932.

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Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.
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Mashwani, Wali Khan, Ruqayya Haider, and Samir Brahim Belhaouari. "A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems." Complexity 2021 (February 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/5521951.

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Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.
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Dang, Duc-Cuong, Anton Eremeev, and Per Kristian Lehre. "Escaping Local Optima with Non-Elitist Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12275–83. http://dx.doi.org/10.1609/aaai.v35i14.17457.

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Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption that the fittest individuals never die. While elitism favours exploitation and ensures that the best seen solutions are not lost, it has been widely conjectured that non-elitism is necessary to explore promising fitness valleys without getting stuck in local optima. Determining when non-elitist EAs outperform elitist EAs has been one of the most fundamental open problems in evolutionary computation. A recent analysis of a non-elitist EA shows that this algorithm does not outperform its elitist counterparts on the benchmark problem JUMP. We solve this open problem through rigorous runtime analysis of elitist and non-elitist population-based EAs on a class of multi-modal problems. We show that with 3-tournament selection and appropriate mutation rates, the non-elitist EA optimises the multi-modal problem in expected polynomial time, while an elitist EA requires exponential time with overwhelmingly high probability. A key insight in our analysis is the non-linear selection profile of the tournament selection mechanism which, with appropriate mutation rates, allows a small sub-population to reside on the local optimum while the rest of the population explores the fitness valley. In contrast, we show that the comma-selection mechanism which does not have this non-linear profile, fails to optimise this problem in polynomial time. The theoretical analysis is complemented with an empirical investigation on instances of the set cover problem, showing that non-elitist EAs can perform better than the elitist ones. We also provide examples where usage of mutation rates close to the error thresholds is beneficial when employing non-elitist population-based EAs.
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ABBASS, H. A., and R. SARKER. "THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM." International Journal on Artificial Intelligence Tools 11, no. 04 (December 2002): 531–52. http://dx.doi.org/10.1142/s0218213002001039.

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The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is competitive to nine known evolutionary multiobjective algorithms for solving VOPs.
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Fonseca, Carlos M., and Peter J. Fleming. "An Overview of Evolutionary Algorithms in Multiobjective Optimization." Evolutionary Computation 3, no. 1 (March 1995): 1–16. http://dx.doi.org/10.1162/evco.1995.3.1.1.

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The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.
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Dissertations / Theses on the topic "Evolutionary Algorithms (EAs)"

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Muniglia, Mathieu. "Optimisation du pilotage d'un Réacteur à Eau Pressurisée dans le cadre de la transition énergétique à l'aide d'algorithmes évolutionnaires." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS261/document.

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L'augmentation de la contribution des énergies renouvelables (solaire ou éolien) et une évolution majeure du parc électrique français et s'inscrit dans le cadre de la transition énergétique. Il est prévu que la part de ces énergies dans le mix passe de 6% actuellement à 30% d'ici à 2030. Cette augmentation en revanche laisse entrevoir d'importants déséquilibres entre l'offre et la demande, et les autres moyens de production, l'énergie nucléaire en tête, devront donc s'adapter. Ce travail vise à augmenter la disponibilité de suivi de charge des centrales, en améliorant leur pilotage durant tout le cycle d'exploitation. Parmi l'ensemble des réacteurs du parc nucléaire français, les réacteurs à eau pressurisées d'une puissance électrique de $1300$ MW sont choisis en raison de leur capacité de suivi de charge déjà accrue. Dans un premier temps, un modèle multi-physique et de type simulateur de la centrale est développé, permettant de prendre en arguments les paramètres principaux des barres de commande, et permettant de déterminer en quelques dizaines de minutes de calcul, les critères d'intérêt dont le premier est en lien avec le diagramme de pilotage et le second avec le volume d'effluents. Le problème d'optimisation est alors résolu grâce à des algorithmes évolutionnaires parallèles asynchronesde type maître-esclave, et les mode de pilotage obtenus sont commentés
The increase of the renewable energies contribution (as wind farms, solar energy) is a major issue in the actual context of energetic transition. The part of intermittent renewable energies is indeed forecast to be around 30% of the total production in 2030, against 6% today. On the other hand, their intermittent production may lead to an important imbalance between production and consumption. Consequently, the other ways of power production must adapt to those variations, especially nuclear energy which is the most important in France. This work aims at increasing the availability of thepower plants to load-follow, by optimizing their manageability all along their operation cycle. Among the French nuclear fleet, the pressurized water reactors(PWR) producing $1300$ electrical MW and operated in the "G" mode are considered as they show the higher capability to load-follow. In a first step, a multi-physics PWR model is designed taking as inputs the main parameters of the control rods, and computing in few minutes the criteria of interest whichare linked to the control diagram and to the effluents volume. The optimization problem which consists in minimizing those two values of interest is then solved thanks to a parallel asynchronous master-worker evolutionary algorithm. Finally, the efficient operating modes are discussed
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Drouet, Valentin. "Optimisation multi-objectifs du pilotage des réacteurs nucléaires à eau sous pression en suivi de charge dans le contexte de la transition énergétique à l'aide d'algorithmes évolutionnaires." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASP024.

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RTE décrit depuis 2017 le réseau électrique français comme ayant peu de marges, et dont le pilotage dépend fortement des performances du parc nucléaire. En particulier, les options de flexibilité du parc, et en particulier le suivi de charge, vont être sollicitées plus régulièrement dans les années à venir. Ces travaux consistent en une optimisation multi-objectifs des performances du suivi de charge pour les réacteurs à eau pressurisés de 1300MW français, en modifiant les paramètres de gestion du déplacement des barres de contrôle du réacteur. Après avoir développé un modèle de réacteur de type simulateur, nous proposons dans un premier temps une méthode fondée sur l’analyse des paysages de fitness afin de déterminer les objectifs les plus pertinents, d’adapter l’espace de recherche et de calibrer l’algorithme d’optimisation. Nous menons ensuite l’optimisation du suivi de charge au début du cycle selon deux objectifs : le volume d’effluents et l’instabilité axiale de la puissance à l’aide d’un algorithme parallèle asynchrone basé sur l’algorithme MOEA/D, pour un transitoire de puissance de type jour/nuit. Nous étendons ensuite la démarche à l’étude de l’ensemble du cycle d’exploitation du réacteur afin de trouver des gestions des barres de contrôle permettant d’améliorer les performances du suivi de charge pour tout le cycle, et développons pour cela une version de l’algorithme assistée par un métamodèle. L’étude de ces solutions montre que l’augmentation de la bande de manoeuvre du groupe de régulation de température conjointement à une adaptation des recouvrements des barres du groupe de compensation de puissance, permet d’améliorer les performances du réacteur tout en maintenant la sûreté
In the context of the introduction of renewable energies in France, Nuclear Power Plant Operations are a key component for the compensation of the intermittent production of solar and wind power. In this work, we focus on the optimization of the operation cost and stability of power of a real-life power transient, while maintaining safety standards on a 1300 MW Pressurized Water Reactor, by changing the control rods management parameters. We first develop a model of the reactor targeted for load follow operations. We then propose a method based on fitness landscape analysis in order to determine the objectives that are best suited for the problem, adapt the search space and tune the optimization algorithm. We first use that method to minimize the volume of effluents and xenon oscillations at the beginning of the reactor exploitation cycle, with an asynchronous parallel algorithm based on MOEA/D for a classic day-night power transient. We then expand that study to the complete exploitation cycle, in order to find rod parameters that are efficient for the complete reactor cycle. We develop for that study an asynchronous parallel algorithm based on MOEA/D assisted by a surrogate model. The analysis of the solutions shows the need to increase the maneuvering band of the temperature regulation rods, while adjusting the overlaps of the power shimming rods
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Woźniak, Ernest. "Model-based Synthesis of Distributed Real-time Automotive Architectures." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112145/document.

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Les solutions basées sur le logiciel/matériel jouent un rôle important dans le domaine de l'automobile. Il est de plus en plus fréquent que l’implémentation de certaines fonctions jusqu’ici réalisées par des composants mécaniques, se fasse dans les véhicules d’aujourd’hui par des composants électroniques embarquant du logiciel. Cette tendance conduit à un grand nombre de fonctions implémentées comme un ensemble de composants logiciels déployés sur unités de commande électronique (ECU). Par conséquent, la quantité de code embarqué dans les automobiles est estimée à des dizaines de giga-octets et le nombre d’ECU de l’ordre de la centaine. Les pratiques actuelles de développement deviennent donc inefficaces et sont en cours d’évolution. L'objectif de cette thèse est de contribuer aux efforts actuels qui consistent à introduire l’utilisation de l'Ingénierie Dirigée par les Modèles dans la conception d’architectures automobiles basées sur le logiciel/matériel. Une première série de contributions de cette thèse porte sur la proposition de techniques pour soutenir les activités décrites dans la méthodologie automobile établie par le langage EAST-ADL2 et le standard AUTOSAR dont l’objectif principal est l'intégration de l'architecture logicielle avec la plate-forme matérielle. Bien que de nombreux travaux sur la synthèse d’architectures existent, cette thèse met en exergue les principaux défauts les empêchant de pleinement supporter la méthodologie EAST-ADL2/AUTOSAR et propose de nouvelles techniques aidant à surmonter les déficiences actuelles. Une deuxième série de contributions concerne les approches de modélisation. L'utilisation de langages de modélisation généralistes (dans le sens non spécifique à un domaine industriel donné) comme SysML et MARTE bien que bénéfique, n'a pas encore trouvé une manière d'être pleinement exploité par les constructeurs automobiles. Cela concerne en particulier la modélisation d’une spécification analysable et l'optimisation des préoccupations qui permettrait d’effectuer des analyses et optimisations à base de modèles. Ce travail définit une méthodologie et les concepts nécessaires à la construction de modèles d'analyse et d'optimisation de ces systèmes
Hardware/software based solutions play significant role in the automotive domain. It is common that the implementation of certain functions that was done in a mechanical manner, in nowadays cars is done through the software and hardware. This tendency lead to the substantial number of functions operating as a set of software components deployed into hardware entities, i.e. Electronic Control Units (ECU). As a consequence the capacity of the overall code is estimated as tens of gigabytes and the number of ECUs reaches more than 100. Consequently the industrial state of the practice development approaches become inefficient. The objective of this thesis is to add to the current efforts trying to employ the Model Driven Engineering (MDE) in the context of the automotive SW/HW architectures design. First set of contributions relates to the guided strategies supporting the key engineering activities of the automotive methodology established by the EAST-\ADL2 language and the AUTOSAR standard. The main is the integration of the software architecture with the hardware platform. Although the amount of work on the synthesis is substantial, this thesis presents shortcomings of the existing approaches that disable them to fully support the EAST-ADL2/AUTOSAR methodology and delivers new techniques overcoming the current deficiencies. Second contribution concerns approaches for the modeling. Surprisingly the usage of general purpose modeling languages such as the SysML and MARTE although beneficial, haven’t found its way yet to be fully exploited by the automotive OEMs (Original Equipment Manufacturer). This especially relates to the modeling of the analyzable input and the optimization concerns which would enable triggering of the analysis and optimization directly from the models level. This work shows a way and defines additional concepts, necessary to construct analysis and optimization models
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Books on the topic "Evolutionary Algorithms (EAs)"

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Olivér, Gábor. CRITIQUE OF THE ASILOMAR AI PRINCIPLES = AZ ASILOMARI ELVEK KRITIKÁJA. GeniaNet Bt., 2022. http://dx.doi.org/10.15170/cotaap-2022.

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The intelligent man with consciousness is the pinnacle of the evolution of matter that we know. So far. We know, however, that evolution will not stop. Although with sections and dead ends of varying lengths, it is moving towards the increasing complexity of organizations, so it is probably not the human in today's sense is its end point. The development of artificial intelligences that we are planning, for example, may meet both our intentions and the criteria of evolution, but as a novelty it also holds the possibility of a future without us. The latter, in turn, creates tension between the species-maintainer desires of homo sapiens and the unknown future course of evolution. The Asilomar principles seek to alleviate this tension by limiting the development of artificial intelligences. However, as technological advances lead to an increase in autonomy, this is at most a plan for time-gaining. In addition to the Asilomar program, then, there is a need for a “Second Foundation” that can reconcile the future of man not only with artificial intelligences but also with evolution. If we want to survive, the evolutionary adaptation of homo sapiens could really ease the pressure of technological determinism on us. At the 2017 International Conference on Artificial Intelligence Safety Technology in Asilomar, participants signed an agreement.[1] They were of the opinion that the development of artificial intelligences should be controlled. More specifically, to limit the future development of algorithms in a way that suits for homo sapiens. In doing so, they sought to meet the future challenges posed by autonomous technologies.[2] The question is whether the Asilomar goal is a real possibility or just a formulation of desires? In the following, after a brief introduction to the ability or get know and formability of the future, I examine the truthfulness of the Asilomar program.
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Book chapters on the topic "Evolutionary Algorithms (EAs)"

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Drechsler, Rolf. "Applications of EAs." In Evolutionary Algorithms for VLSI CAD, 57–145. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2866-8_6.

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Periaux, Jacques, Felipe Gonzalez, and Dong Seop Chris Lee. "Advanced Techniques for Evolutionary Algorithms (EAs)." In Intelligent Systems, Control and Automation: Science and Engineering, 39–52. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9520-3_4.

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Zhou, Zhi-Hua, Yang Yu, and Chao Qian. "Boundary Problems of EAs." In Evolutionary Learning: Advances in Theories and Algorithms, 83–92. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5956-9_7.

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De Jong, Kenneth. "Parameter Setting in EAs: a 30 Year Perspective." In Parameter Setting in Evolutionary Algorithms, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-69432-8_1.

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Kao, Ming-Yang, Tak-Wah Lam, Wing-Kin Sung, and Hing-Fung Ting. "A Decomposition Theorem for MaximumWeight Bipartite Matchings with Applications to Evolutionary Trees." In Algorithms - ESA’ 99, 438–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48481-7_38.

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Jansen, Thomas, and Ingo Wegener. "On the Analysis of Evolutionary Algorithms — A Proof That Crossover Really Can Help." In Algorithms - ESA’ 99, 184–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48481-7_17.

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Chen, Ting, and Ming-Yang Kao. "On the Informational Asymmetry between Upper and Lower Bounds for Ultrametric Evolutionary Trees." In Algorithms - ESA’ 99, 248–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48481-7_22.

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Taubert, Oskar, Marie Weiel, Daniel Coquelin, Anis Farshian, Charlotte Debus, Alexander Schug, Achim Streit, and Markus Götz. "Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations." In Lecture Notes in Computer Science, 106–24. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32041-5_6.

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AbstractWe present , an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare to the established optimization tool . We find that is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulate/.
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"Microwave structures design using EAs." In Emerging Evolutionary Algorithms for Antennas and Wireless Communications, 161–227. Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/sbew534e_ch5.

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"Antenna array design using EAs." In Emerging Evolutionary Algorithms for Antennas and Wireless Communications, 83–128. Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/sbew534e_ch3.

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Conference papers on the topic "Evolutionary Algorithms (EAs)"

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Xue, Ke, Chao Qian, Ling Xu, and Xudong Fei. "Evolutionary Gradient Descent for Non-convex Optimization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/443.

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Non-convex optimization is often involved in artificial intelligence tasks, which may have many saddle points, and is NP-hard to solve. Evolutionary algorithms (EAs) are general-purpose derivative-free optimization algorithms with a good ability to find the global optimum, which can be naturally applied to non-convex optimization. Their performance is, however, limited due to low efficiency. Gradient descent (GD) runs efficiently, but only converges to a first-order stationary point, which may be a saddle point and thus arbitrarily bad. Some recent efforts have been put into combining EAs and GD. However, previous works either utilized only a specific component of EAs, or just combined them heuristically without theoretical guarantee. In this paper, we propose an evolutionary GD (EGD) algorithm by combining typical components, i.e., population and mutation, of EAs with GD. We prove that EGD can converge to a second-order stationary point by escaping the saddle points, and is more efficient than previous algorithms. Empirical results on non-convex synthetic functions as well as reinforcement learning (RL) tasks also show its superiority.
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Qian, Chao. "Towards Theoretically Grounded Evolutionary Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/819.

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Machine learning tasks are often formulated as complex optimization problems, where the objective function can be non-differentiable, non-continuous, non-unique, inaccurate, dynamic, and have many local optima, making conventional optimization algorithms fail. Evolutionary Algorithms (EAs), inspired by Darwin's theory of evolution, are general-purpose randomized heuristic optimization algorithms, mimicking variational reproduction and natural selection. EAs have yielded encouraging outcomes for solving complex optimization problems (e.g., neural architecture search) in machine learning. However, due to the heuristic nature of EAs, most outcomes to date have been empirical and lack theoretical support, encumbering their acceptance to the general machine learning community. In this paper, I will review the progress towards theoretically grounded evolutionary learning, from the aspects of analysis methodology, theoretical perspectives and learning algorithms. Due to space limit, I will include a few representative examples and highlight our contributions. I will also discuss some future challenges.
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Bian, Chao, Chao Qian, and Ke Tang. "A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/195.

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Evolutionary algorithms (EAs) have been widely applied to solve multi-objective optimization problems. In contrast to great practical successes, their theoretical foundations are much less developed, even for the essential theoretical aspect, i.e., running time analysis. In this paper, we propose a general approach to estimating upper bounds on the expected running time of multi-objective EAs (MOEAs), and then apply it to diverse situations, including bi-objective and many-objective optimization as well as exact and approximate analysis. For some known asymptotic bounds, our analysis not only provides their leading constants, but also improves them asymptotically. Moreover, our results provide some theoretical justification for the good empirical performance of MOEAs in solving multi-objective combinatorial problems.
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Bonissone, Stefano R. "Evolutionary algorithms for multi-objective optimization: fuzzy preference aggregation and multisexual EAs." In International Symposium on Optical Science and Technology, edited by Bruno Bosacchi, David B. Fogel, and James C. Bezdek. SPIE, 2001. http://dx.doi.org/10.1117/12.448334.

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Lehre, Per Kristian, and Pietro Simone Oliveto. "Runtime Analysis of Population-based Evolutionary Algorithms - Part I: Steady State EAs." In GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583133.3595056.

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Bian, Chao, Yawen Zhou, Miqing Li, and Chao Qian. "Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/612.

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Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update is a key component in multi-objective EAs (MOEAs), and it is performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied bi-objective problem, OneJumpZeroJump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. This work is an attempt to challenge a common practice for the population update in MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.
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Hao, Wang, Li Zhou, Xiaobo Zhang, and Zhanxue Wang. "Acceleration Method for Evolutionary Optimization of Variable Cycle Engine." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14369.

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Abstract Variable cycle engine (VCE) is considered as one of the best options for advanced military or commercial supersonic propulsion system. Variable geometries enable the engine to adjust performance over the entire the flight envelope but add complexity to the engine. Evolutionary algorithms (EAs) have been widely used in the design of VCE. The initial guesses of the engine model are generally set using design point information during evolutionary optimization. However, the design point information is not suitable for all situations. Without suitable initial guesses, the Newton-Raphson solver will not be able to reach the solution quickly, or even get a convergent solution. In this paper, a new method is proposed to obtain suitable initial guesses of VCE model during evolutionary optimization. Differential evolution (DE) algorithm is used to verify our method through a series of optimization cases of a double bypass VCE. The result indicates that the method can significantly reduce the VCE model call number during evolutionary optimization, which means a dramatic reduction in terms of evolution time. And the robustness of the optimization is not affected by the method. The method can also be used in the evolutionary optimization of other engines.
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Shang, Haopu, Jia-Liang Wu, Wenjing Hong, and Chao Qian. "Neural Network Pruning by Cooperative Coevolution." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/667.

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Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to measure their importance, which, however, requires expertise and trial-and-error. Due to the advantage of automation, pruning by evolutionary algorithms (EAs) has attracted much attention, but the performance is limited for deep neural networks as the search space can be quite large. In this paper, we propose a new filter pruning algorithm CCEP by cooperative coevolution, which prunes the filters in each layer by EAs separately. That is, CCEP reduces the pruning space by a divide-and-conquer strategy. The experiments show that CCEP can achieve a competitive performance with the state-of-the-art pruning methods, e.g., prune ResNet56 for 63.42% FLOPs on CIFAR10 with -0.24% accuracy drop, and ResNet50 for 44.56% FLOPs on ImageNet with 0.07% accuracy drop.
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Abbas, Iraq, and Qusay Al-Salami. "Inverted Generational Distance Bat Algorithm for Many-Objective Optimization Problems." In ‎4th International Conference on ‎Administrative ‎& Financial Sciences. Cihan University-Erbil, 2023. http://dx.doi.org/10.24086/icafs2023/paper.905.

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Evolutionary Algorithms (EAs) can be used to solve extremely large-scale Many-Objective Optimization Problems (MOPs/I). Multi-Objective BAT Algorithm based on Inverted Generational Distance MOBAT / IGD, a dominance-decomposition bat algorithm, solves this problem. Due to the Tchebycheff Strategy leader selection process, addressing the issues concurrently inside the BAT foundation may result in rapid convergence. In this paper decomposing the MOP as a Tchebycheff Approach set simplifies it. Dominance allows leaders to scan less densely populated areas, avoiding local optima and producing a more diverse estimated Pareto front as well creating the executives archive. MOBAT/IGD was evaluated to various decomposition-based development methods utilizing 35 standard MOPs. MATLAB produced all results (R2017b).
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M, Ganga, and Gini R. "Optimized Multi Support Vector Machine Based Approach for Fake News Detection." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/jaad9174/ngcesi23p46.

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Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country’s status or overstates the expense of special functions for a government, destroying democracy in certain countries. The project proposes an Multi Support Vector Machine (MSVM) -based approach for detecting fake news. The proposed model will be used to classify or detect the news as fake or real. Principal Component Analysis (PCA) is used for Feature Extraction. Principal Component Analysis (PCA) reduces the dimension of the data set comprising many related variables and recalls the maximum change in actual data. The proposed work will select the essential features with a Firefly-Optimized Algorithm (FA). The Firefly Optimized Algorithm (FA) is one of the various Evolutionary Algorithms (EAs) with various purposes. For the classification of fake news, an Multi Support Vector Machine (MSVM) classifier algorithm is implemented.
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