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Problem of Domain/Building Block Preservation in the Evolution of Biological Macromolecules and evolutionary computation

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IEEE-ACM TRANSACTIONS ON computationAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 1345-1362页

作者： Spirov, Alexander V. Myasnikova, Ekaterina M. Russian Acad Sci IM Sechenov Inst Evolutionary Physiol & Biochem St Petersburg 194223 Russia Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

Structurally and functionally isolated domains in biological macromolecular evolution, both natural and artificial, are largely similar to "schemata", building blocks (BBs), in evolutionary computation (EC). The problem of preserving in subsequent evolutionary searches the already found domains / BBs is well known and quite relevant in biology as well as in EC. Both biology and EC are seeing parallel and independent development of several approaches to identifying and preserving previously identified domains / BBs. First, we notice the similarity of DNA shuffling methods in synthetic biology and multi-parent recombination algorithms in EC. Furthermore, approaches to computer identification of domains in proteins that are being developed in biology can be aligned with BB identification methods in EC. Finally, approaches to chimeric protein libraries optimization in biology can be compared to evolutionary search methods based on probabilistic models in EC. We propose to validate the prospects of mutual exchange of ideas and transfer of algorithms and approaches between evolutionary systems biology and EC in these three principal directions. A crucial aim of this transfer is the design of new advanced experimental techniques capable of solving more complex problems of in vitro evolution.

关键词： Proteins Biology Evolution (biology) Statistics Sociology Genetic algorithms Synthetic biology evolutionary computation genetic algorithms synthetic biology evolution of biological macromolecules In Vitro evolution cross disciplinary knowledge transfer transfer of algorithms modularity building blocks conservative protein domains DNA shuffling multi-parent recombination building block identification chimeric protein libraries optimization estimation of distribution algorithms

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Exploring seasonal patterns using process modelling and evolutionary computation

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ECOLOGICAL MODELLING 2006年第1-2期195卷 146-152页

作者： Whigham, PA Dick, G Recknagel, R Univ Otago Dept Informat Sci Dunedin New Zealand Univ Adelaide Sch Earth & Environm Sci Adelaide SA 5005 Australia

Process models of chlorophyll-a concentration for freshwater systems, and in particular lake environments, have been developed over many years. Previous work has demonstrated that the optimisation of constants within these models has been able to significantly improve the quality of the resulting model on unseen data. This paper explores two properties of one particular process model: can the model predictions be improved by optimising the constants over different temporal scales;and can seasonal patterns be identified, based on monthly training scales, that allow further understanding of the response of the freshwater system to changing environmental conditions. The results show that there is some improvement on the prediction of unseen data when using constants of the process model optimised for individual months, versus constants trained over a yearly cycle. Additionally, by studying the patterns of the constants over various time scales some underlying seasonal patterns can be observed. These patterns can be further studied by exploring how the various elements of the process model vary with monthly versus yearly training constants. This work demonstrates some possible directions for understanding how the behaviour of freshwater systems at different time scales can be used to understand the properties of these complexes, non-linear systems. The results also suggest that local models of ecological time series data can be used to extract information that may not be obtainable from a single, global model. (c) 2005 Elsevier B.V. All rights reserved.

关键词： process model chlorophyll-a evolutionary computation optimisation local model

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Interactive hybrid evolutionary computation for MEMS design synthesis

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MATHEMATICS AND COMPUTERS IN SIMULATION 2012年 86卷 32-38页

作者： Zhang, Ying Agogino, Alice M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

An interactive hybrid evolutionary computation (IHC) process for MEMS design synthesis is described, which uses both human expertise and local performance improvement to augment the performance of an evolutionary process. The human expertise identifies good design patterns, and local optimization fine-tunes these designs so that they reach their potential at early stages of the evolutionary process. At the same time, the feedback on local optimal designs confirms and refines the human assessment. The advantages of the IHC process are demonstrated with micromachined resonator test cases. Guidelines on how to set parameters for the IHC algorithm are also made based on experimental observations and results. (C) 2011 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Interactive hybrid computation MEMS synthesis evolutionary computation Optimal design

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Simultaneous inductive and deductive modeling of ecological systems via evolutionary computation and information theory

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2006年第7期82卷 439-450页

作者： Hoffmann, James P. Univ Vermont Dept Plant Biol Burlington VT 05405 USA

The addition of two emerging technologies (evolutionary computation and ecoinformatics) to computational ecology can advance our ability to build better ecological models and thus deepen our understanding of the mechanistic complexity of ecological systems. This article describes one feasible approach toward this goal-the combining of inductive and deductive modeling techniques with the optimizing power of simple algorithms of Darwinian evolution that include information-theoretic model selection methods. Specifically, the author shows a way to extend classic genetic algorithms beyond typical parameter fitting of a single, previously chosen model to a more flexible technique that can work with a suite of possible models. Inclusion of the Akaike information-theoretic model selection method within an evolutionary algorithm makes it possible to accomplish simultaneous parameter fitting and parsimonious model selection. Experiments with synthetic data show the feasibility of this approach, and experiments with time-series field data of the zebra mussel invasion of Lake Champlain (United States) result in a model of the invasion dynamics that is consistent with the known hydrodynamic features of the lake and the motile life history stage of this invasive species. The author also describes a way to extend this approach with a modified genetic programming algorithm.

关键词： computational ecology ecological models evolutionary computation information theory model selection genetic algorithms genetic programming zebra mussels

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An evolutionary computation-Based Federated Learning for Host Intrusion Detection in Real-Time Traffic Analysis

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WIRELESS PERSONAL COMMUNICATIONS 2024年 1-27页

作者： Suresh, A. Dwarakanath, B. Nanda, Ashok Kumar Santhosh Kumar, P. Sankar, S. Cheerla, Sreevardhan Vellore Inst Technol Sch Comp Sci & Engn Vellore India SRM Inst Sci & Technol Fac Engn & Technol Dept Informat & Technol Chennai India BV Raju Inst Technol Dept Comp Sci & Engn Hyderabad Telangana India SIMATS Dept Comp Sci & Engn Saveetha Sch Engn Chennai India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram Andhra Pradesh India

Important information needs to be sent over the Internet safely. Real-time data is mixed with harmful information, lowering the quality of communication and the system's overall performance. A network intruder detection system is software that examines all incoming and outgoing network packets to detect malicious events. Federated learning (FL) is a way to put artificial intelligence at the cutting edge. It is a way to solve problems that is not centralized and lets people learn from significant amounts of data. Deep learning (DL) methods have often been used to find harmful data in host intrusion detection systems (HIDS) that look for unusual behavior. The FL architecture allows multiple users to train a global model while respecting the privacy of each user's data, making DL-based methods more useful. But there has yet to be a complete analysis of how well FL-based HIDSs protect against known privacy threats with the already in-place defenses. To solve this problem, we offer two privacy assessment measures for FL-based HIDSs, including a privacy score that rates how close the original and restored traffic attributes are. The CICIDS2017 dataset, which includes several attacks from the present day, was used to make the real-time model. In addition, an adaptive threshold-correlation algorithm (ATCA) is presented to enhance detection accuracy by dynamically adjusting threshold values according to traffic patterns and intrusion behaviors. The FL-HIDS framework was created and tested using a realistic network dataset. Experiment results show that the suggested technique outperforms existing intrusion detection systems regarding detection precision and scalability. The federated learning strategy effectively leverages the collective intelligence of network devices, enabling continuous learning and adaptation to emergent attack strategies. Furthermore, the adaptive threshold technique considerably reduces the rate of false positive and false negative detection, boosting t

关键词： evolutionary computation Deep learning (DL) Federated learning (FL) Host intrusion detection Real-time traffic analysis

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A hybrid training algorithm based on gradient descent and evolutionary computation

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APPLIED INTELLIGENCE 2023年第18期53卷 21465-21482页

作者： Xue, Yu Tong, Yiling Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Surrey Dept Comp Sci NICE Res Grp Guildford England

Back propagation (BP) is widely used for parameter search of fully-connected layers in many neural networks. Although BP has the potential of quickly converging to a solution, due to its gradient-based nature, it tends to fall into a local optimum. Metaheuristics such as evolutionary computation (EC) techniques, as gradient-free methods, may have excellent global search capability due to their stochastic nature. However, these techniques tend to perform worse than BP in terms of convergence speed. In this paper, a hybrid gradient descent search algorithm (HGDSA) is proposed for training the parameters in fully-connected neural networks. HGDSA initially searches the space extensively by means of an ensemble of gradient descent strategies in the early stage and then uses BP as an exploitative local search operator. Moreover, a self-adaptive method which selects strategies and updates the learning rates of strategies has been designed and embedded in the global search operators to prevent stagnation in local optima. To verify the effectiveness of HGDSA, experiments were performed on eleven classification datasets. Experimental results demonstrate that the proposed HGDSA possesses both powerful global and local search abilities. Furthermore, the proposed approach appears to be promising also on high-dimensional datasets.

关键词： evolutionary computation Backpropagation Fully-connected neural networks Adaptive mechanism Parameter optimization

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Medical Image Registration Using evolutionary computation: An Experimental Survey

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IEEE computationAL INTELLIGENCE MAGAZINE 2011年第4期6卷 26-42页

作者： Damas, S. Cordon, O. Santamaria, J. European Ctr Soft Comp Mieres Spain Univ Granada CITIC UGR Res Ctr Informat & Commun Technol E-18014 Granada Spain Univ Granada DECSAI Dept Comp Sci & Artificial Intelligence E-18014 Granada Spain

In the last few decades, image registration (IR) has been established as a very active research area in computer vision. Over the years, IR's applications cover a broad range of real-world problems including remote sensing, medical imaging, artificial vision, and computer-aided design. In particular, medical IR is a mature research field with theoretical support and two decades of practical experience. Traditionally, medical IR has been tackled by iterative approaches considering numerical optimization methods which are likely to get stuck in local optima. Recently, a large number of medical IR methods based on the use of metaheuristics such as evolutionary algorithms have been proposed providing outstanding results. The success of the latter modern search methods is related to their ability to perform an effective and efficient global search in complex solution spaces like those tackled in the IR discipline. In this contribution, we aim to develop an experimental survey of the most recognized feature-based medical IR methods considering evolutionary algorithms and other metaheuristics. To do so, the generic IR framework is first presented by providing a deep description of the involved components. Then, a large number of the latter proposals are reviewed. Finally, the most representative methods are benchmarked on two real-world medical scenarios considering two data sets of three-dimensional images with different modalities.

关键词： Biomedical imaging Feature extraction Medical image processing Image registration evolutionary computation Three dimensional displays computational modeling

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Application of Swarm Intelligence and evolutionary computation Algorithms for Optimal Reservoir Operation

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WATER RESOURCES MANAGEMENT 2022年第7期36卷 2275-2292页

作者： Yaghoubzadeh-Bavandpour, Arya Bozorg-Haddad, Omid Rajabi, Mohammadreza Zolghadr-Asli, Babak Chu, Xuefeng Bu Ali Sina Univ Fac Agr Dept Water Sci & Engn Hamadan Hamadan Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Karaj *** Iran North Dakota State Univ Dept Civil & Environm Engn Dept 2470 Fargo ND 58108 USA

Real-world problems often contain complex structures and various variables, and classical optimization techniques may face difficulties finding optimal solutions. Hence, it is essential to develop efficient and robust techniques to solve these problems. computational intelligence (CI) optimization methods, such as swarm intelligence (SI) and evolutionary computation (EC), are promising alternatives to conventional gradient-based optimizations. SI algorithms are multi-agent systems inspired by the collective behavior of individuals, while EC algorithms implement adaptive search inspired by the evolution process. This study aims to compare SI and EC algorithms and to compare nature-based and human-based algorithms in the context of water resources planning and management to optimize reservoir operation. In this study four optimization algorithms, including particle swarm optimization (PSO), teaching-learning based optimization algorithm (TLBO), genetic algorithm (GA), and cultural algorithm (CA), were applied to determine the optimal operation of the Aydoghmoush reservoir in Iran. This study used four criteria, known as objective function value, run time, robustness, and convergence rate, to compare the overall performances of the selected optimization algorithms. In term of the objective function, PSO, TLBO, GA, and CA achieved 2.81 x 10(-31), 1.66 x 10(-24), 4.29 x 10(-4), and 1.44 x 10(-2), respectively. The results suggested that although both SI and EC algorithms performed acceptably and provided optimal solutions for reservoir operation, SI algorithms outperformed the EC algorithms in terms of accuracy of solutions, convergence rate, and run time to reach global optima.

关键词： Swarm intelligence evolutionary computation Optimization Reservoir operation Aydoghmoush reservoir

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Experimental interferogram analysis using an automatic polynomial fitting method based on evolutionary computation -: art. no. 045604

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OPTICAL ENGINEERING 2005年第4期44卷 1-8页

作者： Sánchez-Escobar, JJ Vázquez-Montiel, S Ctr Ensenanza Tecn Ind Guadalajara 44620 Jalisco Mexico Inst Nacl Astrofis Opt & Elect Puebla 72000 Mexico

We report on the experimental verification of an alternative method to the conventional least-squares fit method to obtain the phase of real interferograms. The proposed method performs an automatic polynomial fitting by solving an optimization problem, where an objective function is minimized and the solution to the irradiance equation is considered as an inverse problem. In this work, we consider fundamental concepts by comparing the performance of the least-squares fit method and the evolutionary algorithm. It is important to point out that the experimental verification of interferograms with the proposed method is applied to confirm the quality of some manufactured optical surfaces at the Instituto Nacional de Astrofisica, Optica y Electronica. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

关键词： interferogram analysis image processing evolutionary computation

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On evolutionary computation techniques for multi-view triangulation

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MACHINE VISION AND APPLICATIONS 2020年第4期31卷 29-29页

作者： Nair, Nirmal S. Nair, Madhu S. Univ Kerala Res Ctr Dept Comp Sci Kariyavattom 695581 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 Kerala India

Multi-view triangulation is an essential step in recovering three-dimensional structure from a set of images. It is a well-studied problem in computer vision with many suboptimal and optimal methods based on different optimality criteria. In this paper, we assess the ability of evolutionary computation (EC) methods in finding highly accurate solutions to this problem. We use an overlaying Luus-Jaakola optimizer to find good parameter configurations and determine appropriate computational budget for the EC methods. Empirical results on synthetic and real data demonstrate the superior performance of EC methods over existing triangulation methods.

关键词： Triangulation Parameter tuning evolutionary computation 3D reconstruction

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