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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Maliszewski, Michal Boryczka, Urszula Diebold Nixdorf Software Secur Grp Katowice Poland Univ Silesia Inst Comp Sci Sosnowiec Poland

ISBN: (纸本)9781728183923

Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.

关键词： ATM Clustering Cyber Security Whitelisting Intrusion Protection Sandboxing Software Security evolutionary computing Big Data

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Automatic classification of nuclear physics data via a Constrained evolutionary Clustering approach

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COMPUTER PHYSICS COMMUNICATIONS 2021年 259卷 107667-107667页

作者： Dell'Aquila, D. Russo, M. Rudjer Boskovic Inst Div Expt Phys Bijenicka 54 HR-10000 Zagreb Croatia Univ Catania Dipartimento Fis & Astron I-95235 Catania Italy INFN Sez Catania I-95235 Catania Italy

This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus-nucleus collisions at low and intermediate energies. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Nuclear physics data classification evolutionary computing Clustering algorithms Charged particle identification in nuclear collisions

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A Comparative study of Hyper-Parameter Optimization Tools

A Comparative study of Hyper-Parameter Optimization Tools

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8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (IEEE CSDE)

作者： Shekhar, Shashank Bansode, Adesh Salim, Asif Subex Ltd AI Labs Bangalore Karnataka India

ISBN: (纸本)9781665495523

Most of the machine learning models have associated hyper-parameters along with their parameters. While the algorithm gives the solution for parameters, its utility for model performance is highly dependent on the choice of hyperparameters. For a robust performance of a model, it is necessary to find out the right hyper-parameter combination. Hyper-parameter optimization (HPO) is a systematic process that helps in finding the right values for them. The conventional methods for this purpose are grid search and random search and both methods create issues in industrial-scale applications. Hence a set of strategies have been recently proposed based on Bayesian optimization and evolutionary algorithm principles that help in runtime issues in a production environment and robust performance. In this paper, we compare the performance of four python libraries, namely Optuna, Hyper-opt, Optunity, and sequential model-based algorithm configuration (SMAC) that has been proposed for hyper-parameter optimization. The performance of these tools is tested using two benchmarks. The first one is to solve a combined algorithm selection and hyper-parameter optimization (CASH) problem The second one is the NeurIPS black-box optimization challenge in which a multilayer perceptron (MLP) architecture has to be chosen from a set of related architecture constraints and hyper-parameters. The benchmarking is done with six real-world datasets. From the experiments, we found that Optuna has better performance for CASH problem and HyperOpt for MLP problem.

关键词： hyper-parameter optimization bayesian optimization evolutionary computing

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A novel technique to self-adapt parameters in parallel/distributed genetic programming

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SOFT computing 2020年第22期24卷 16885-16894页

作者： Russo, Marco Univ Catania Catania Italy

This paper introduces the Supervisor evolutionary Algorithm, a novel technique that allows for self-adapt almost all the internal parameters in parallel distributed client-server genetic programming. This novel adapting mechanism, is itself of an evolutionary nature, so we have a double evolutionary tool. The upper level, as is usual in evolutionary computing, has its own customized selection, crossover, and mutation mechanisms. The lower stage used here is the Brain Project a parallel-distributed software tool for formal modelling of numerical data using a hybrid neural-genetic programming technique. As demonstrated by the experiment reported in this paper, our approach works well adapting continuously its internal parameters.

关键词： Genetic programming Neural networks evolutionary computing Parallel computing Distributed computing

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An evolutionary General Type-2 Fuzzy Neural Network applied to Trajectory Planning in Remotely Operated Underwater Vehicles

An Evolutionary General Type-2 Fuzzy Neural Network applied ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Rubio-Solis, Adrian Salgado-Jimenez, Tomas Govinda Garcia-Valdovinos, Luis Nava-Balanzar, Luciano Hernandez-Hernandez, Rolando A. Martinez-Hernandez, Uriel Ctr Engn & Ind Dev Dept Energy Qto 76125 Mexico Univ Bath Dept Elect & Elect Engn Bath BA2 7AY Avon England

ISBN: (纸本)9781728169323

In this paper, an evolutionary General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) for trajectory planning in Remotely Operated underwater Vehicles (ROVs) is suggested. The GT2-RBFNN is used as a data-driven learning system to orient the current position of an ROV in underwater environments. To determine the parameters of GT2-RBFNN, Galactic Swarm Optimisation (GSO) was implemented. A BlueROV2 and a squared water container of 2.5m x 2.5m x 3.5m were employed to run all experiments. To control the ROV position, a sensory system that consists of a compass, a micro data sonar, a ping sonar and a pressure sensor was integrated. First, a Proportional Derivative fuzzy controller was implemented to control the depth and yaw positions of the ROV. Secondly, the GT2-RBFNN was applied to discriminate between two different types of contours, i.e. corners and walls in order to follow an obstacle-free trajectory. To compare the efficiency of the GT2-RBFNN, a number of learning techniques that are based on Extreme Learning Machine (ELM) and evolutionary optimisation were implemented. Based on our results, a high trade-off between model simplicity and low computational burden are provided by the GT2-RBFNN.

关键词： Neural Networks GT2 Fuzzy Logic evolutionary computing Remotely Operated Underwater Vehicles

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A Conceptual Direction on Automatically Evolving Computer Malware using Genetic and evolutionary Algorithms 5

A Conceptual Direction on Automatically Evolving Computer Ma...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Murali, Ritwik Velayutham, C. Shunmuga Coimbatore Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781728146850

The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.

关键词： Malware Malware Creation Bio-inspired computing evolutionary Algorithms evolutionary computing Malware Analysis Proactive Defence

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An Evaluation on Competitive and Cooperative evolutionary Algorithms for Data Clustering

An Evaluation on Competitive and Cooperative Evolutionary Al...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pacifico, Luciano D. S. Ludermir, Teresa B. Univ Fed Rural Pernambuco UFRPE Dept Comp DC Recife PE Brazil Univ Fed Pernambuco UFPE Ctr Informat CIn Recife PE Brazil

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

Data clustering methods are important tools for exploratory data analysis in many real world applications, such as data mining, image understanding, text analysis, engineering, medicine, and so on. Partitional clustering models are the most popular clustering methods, but these approaches suffer from some limitations, like the sensibility to algorithm initialization and the lack of mechanisms to help them escaping from local minima points. evolutionary Algorithms (EAs) are global optimization meta-heuristics known for their capabilities to find optimal solutions even when dealing with hard and complex problems. Although many EAs are based on competitive behavior among individuals, its is known that cooperation may lead to better solutions then sheer competition. In this work, we perform a comparative analysis among four state-of-the-art EAs (Genetic Algorithm, Differential Evolution, Particle Swarm Optimization and Group Search Optimization), implemented in both competitive and cooperative frameworks, in the context of data clustering problem. Experiments are executed using eleven real world benchmark datasets as the testing bed, so we could access whether competitive or cooperative behaviors would prevail. The experimental results showed that cooperative algorithms are able to find better solutions, in average, when dealing with clustering problems, than their corresponding competitive approaches, and such models also require less storage memory to keep their population in comparison to competitive methods.

关键词： evolutionary computing Data Clustering Cooperative Algorithms Cooperative Coevolutionary Algorithms

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Generating Agent-Based Models From Scratch With Genetic Programming

Generating Agent-Based Models From Scratch With Genetic Prog...

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2021 Conference on Artificial Life, ALIFE 2021

作者： Greig, Rory Arranz, Jordi Improbable LondonE1 6EG United Kingdom

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Multilingual audio information management system based on semantic knowledge in complex environments

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NEURAL computing & APPLICATIONS 2020年第24期32卷 17869-17886页

作者： Lopez-de-Ipina, Karmele Barroso, Nora Calvo, Pilar M. Hernandez, Carmen Ezeiza, Aitzol Susperregi, Unai Fernandez, Elsa Univ Basque Country Dept Syst Engn & Automat Donostia San Sebastian Spain Univ Cambridge Dept Psychiat Cambridge England Univ Basque Country EleKin Engn & Soc Res Grp Europa Plaza 1 Donostia San Sebastia pr 20018 Spain Univ Basque Country Dept Comp Architecture & Technol Donostia San Sebastia pr Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Donostia San Sebastia pr Spain Irunweb SL Co Zentolen Gunea 121 Left Renteriaz 20100 Gipuzkoa Spain

This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources);the poor quality of the audio signal taken from an internet radio channel;the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas);and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.

关键词： evolutionary computing Artificial neural networks Internet information management Management of complex systems

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Heuristic solutions to robust variants of the minimum-cost integer flow problem

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JOURNAL OF HEURISTICS 2020年第4期26卷 531-559页

作者： Spoljarec, Marko Manger, Robert Privredna Banka Zagreb Radnicka Cesta 44 Zagreb 10000 Croatia Univ Zagreb Dept Math Fac Sci Bijenicka Cesta 30 Zagreb 10000 Croatia

This paper deals with robust optimization applied to network flows. We consider two robust variants of the minimum-cost integer flow problem. Thereby, uncertainty in problem formulation is limited to arc costs and expressed by a finite set of explicitly given scenarios. It turns out that both problem variants are NP-hard. To solve the considered variants, we propose several heuristics based on local search or evolutionary computing. We also evaluate our heuristics experimentally on appropriate problem instances.

关键词： Robust optimization Network flow Minimum-cost flow Heuristic Local search evolutionary computing

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