Aleš Zamuda

Aleš Zamuda
University of Maribor | UM · Faculty of Electrical Engineering and Computer Science (FERI)

PhD, Associate Professor

About

78
Publications
13,436
Reads
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1,604
Citations
Introduction
Assoc. Prof. Dr. Ales Zamuda received B.Sc. (2006), M.Sc. (2008), and Ph.D. (2012) degree in computer science from University of Maribor. He is currently affiliated with the Faculty of Electrical Engineering and Computer Science at the University of Maribor. His areas of interest include evolutionary algorithms, multicriterion optimization, artificial life, and computer animation. He is an IEEE member and also reviewer for several scientific journals and conferences and editor at SWEVO.
Additional affiliations
November 2006 - present
University of Maribor - Univerza v Mariboru
November 2006 - present
University of Maribor
Position
  • Research Assistant
Description
  • The Computer Architecture and Languages Laboratory is also involved in research activities of Programme-Funded Unit Computer Systems, Methodologies, and Intelligent Services, see http://sicris.izum.si/search/prg.aspx?lang=eng&id=6131.
Education
October 2008 - May 2012
University of Maribor
Field of study
  • Computer Science
October 2006 - July 2008
University of Maribor
Field of study
  • Computer Science
October 2001 - June 2006
University of Maribor
Field of study
  • Computer Science

Publications

Publications (78)
Article
This paper presents tackling of a hard optimization problem of computational linguistics, specifically automatic multi-document text summarization, using grid computing. The main challenge of multi-document summarization is to extract the most relevant and unique information effectively and efficiently from a set of topic-related documents, constra...
Book
This volume constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019, held in Maribor, Slovenia, in July 2019. The 18 full papers presented in this volume were carefully...
Article
Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires...
Conference Paper
The concept of smartness is an essential topic that was only recently extended to rural areas. Although smartness is already incorporated strongly into numerous urban environments, differences between cities and villages prevent direct transfer of the methods and tools used for the smart transformation. To increase the awareness of newly developed...
Conference Paper
This paper proposes a new discrete optimization benchmark 100b-Digit, a binary discretized version for the 100-Digit Challenge. The continuous version 100-Digit Challenge utilizing continuous input parameters for a fitness function was suggested for the competitions at 2019 GECCO and 2019 CEC, while this paper proposes an extension, a discrete vers...
Conference Paper
This paper describes a competition entry for the "100-Digit Challenge, and Four Other Numerical Optimization Competitions" at The Genetic and Evolutionary Computation Conference (GECCO) 2019, by assessing the function evaluations up to 1e+12, and large population sizes in Distance-based Success History Differential Evolution for the 100-Digit Chall...
Conference Paper
The real-world implementation of Underwater Glider Path Planning (UGPP) over the dynamic and changing environment in deep ocean waters requires complex mission planning under very high uncertainties. Such a mission is also influenced to a large extent by remote sensing for forecasting weather models outcomes used to predict spatial currents in deep...
Conference Paper
A cooperative model of well-known evolutionary algorithms is proposed and tested on CEC 2019 benchmark suite. The four adaptive algorithms are chosen for this model, namely Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) and three variants of adaptive Differential Evolution. Although the three algorithms use constant population size, th...
Chapter
Control-flow computer architectures are based on the von Neumann paradigm. They are flexible enough to support the execution of instructions in any order. Each instruction is fetched from the memory before it could be executed. Passing the data from the instruction that produces it to the instruction that requires it is done using registers or memo...
Chapter
Full-text available
The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because o...
Chapter
Full-text available
This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this pro...
Article
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC201...
Article
This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compare...
Conference Paper
This paper provides a taxonomical identification survey of classes in discrete optimization challenges that can be found in the literature including a proposed pipeline for benchmarking, inspired by previous computational optimization competitions. Thereby, a Black-Box Discrete Optimization Benchmarking (BB-DOB) perspective is presented for the [em...
Conference Paper
This paper presents a Genetic Algorithm (GA) application to measuring feature importance in machine learning (ML) from a large-scale database. Too many input features may cause over-fitting, therefore a feature selection is desirable. Some ML algorithms have feature selection embedded, e.g., lasso penalized linear regression or random forests. Othe...
Conference Paper
This paper presents insights into the proportions between the k-means clusters of successful Differential Evolution (DE), donor generating vectors. This is demonstrated by the high certainty that these proportions are similar - and thereby, that these cluster size proportions regularly appear. A characteristic of these proportions is that they are...
Article
Full-text available
Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A...
Article
Full-text available
This paper proposes a lossy image representation where a reference image is approximated by an evolved image, constituted of variable number of triangular brushstrokes. The parameters of each triangle brush are evolved using differential evolution, which self-adapts the triangles to the reference image, and also self-adapts some of the control para...
Conference Paper
Full-text available
This paper presents an approach to underwater glider path planning (UGPP), where the population size reduction mechanism is introduced into the differential evolution (DE) meta-heuristic and two types of DE strategies (DE/best and DE/rand) are applied interchangeably. The newly proposed DE instance algorithms using population size reduction on the...
Chapter
This chapter gives an overview of Differential Evolution (DE), then presents adaptive and self-adaptive mechanisms within the DE algorithm. They can be used in order to make a DE solver more robust, efficient, etc., and to overcome parameter tuning which is usually a time-consuming task needed to be done before the actual optimization process start...
Article
This paper presents an approach where differential evolution is applied to underwater glider path planning. The objective of a glider is to reach a target location and gather research data along its path by propelling itself underwater and returning periodically to the surface. The main hypothesis of this work is that gliders operational capabiliti...
Conference Paper
Abstract This paper proposes a lossy image representation where a reference image is approximated by an evolved image, constituted of variable number of triangular brushstrokes. The parameters of each triangle brush are evolved using differential evolution, which self-adapts the triangles to the reference image, and also self-adapts some of the con...
Conference Paper
Full-text available
This paper presents a differential evolution (DE) algorithm for real-parameter optimization. The algorithm includes the self-adaptive jDE algorithm with one of its strongest extensions, population reduction, combined with multiple mutation strategies using a structured population. The two mutation strategies used are run dependent on the population...
Conference Paper
A new differential evolution algorithm for single objective optimization is presented in this paper. The proposed algorithm uses a self-adaptation mechanism for parameter control, divides its population into more subpopulations, applies more DE strategies, promotes population diversity, and eliminates the individuals that are not changed during som...
Article
Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm...
Article
We propose an environmental framework for simulation and visualization of woody plant forests. A complex application software system develops and animates a spontaneous afforestation process within this environment. The system considers several environmen-tal properties and combines computer animation with artificial life. The main goal of the pres...
Conference Paper
Full-text available
This paper presents an innovization design principle for procedural tree model of woody plants (trees) reconstruction by multi-objective optimization. Reconstruction of a parameterized procedural model from imagery is addressed by a multi-objective differential evolution algorithm, which evolves a parametrized procedural model by fitting a set of i...
Conference Paper
In this paper we present self-adaptive differential evolution algorithm with a small and varying population size on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2012 competition and special session on Large Scale Global Optimization are presented. The experiments wer...
Conference Paper
This paper presents a novel differential evolution algorithm for optimization of state-of-the-art real world industry challenges. The algorithm includes the self-adaptive jDE algorithm with one of its strongest extensions, population reduction, and is now combined with multiple mutation strategies. The two mutation strategies used are run dependent...
Article
This paper presents an approach for reconstruction of procedural three-dimensional models of woody plants (trees). The used procedural tree model operates by recursively computing all building parts of a three-dimensional tree structure by applying a fixed procedure on a given large set of numerically coded input parameters. The parameterized proce...
Conference Paper
Differential Evolution (DE) is widely used in solv- ing single objective constrained real-parameter optimization problems. In this paper we present a new version of DE- based algorithm for constrained optimization. A new mutation strategy is also used in the proposed algorithm. The performance of the proposed algorithm is evaluated on the set of 36...
Conference Paper
Full-text available
In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 benchmark funct...
Article
Full-text available
This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process. The general idea is based on individual evaluations according to played games through several g...
Article
Full-text available
This paper presents an approach for recognition of procedural three-dimensional models of woody plants (trees). The used procedural tree model operates by building a three-dimensional structure of a tree by applying a fixed procedure on a given set of numerically-coded input parameters. The parameterized procedural model can later be used for compu...
Conference Paper
Full-text available
This paper presents differential evolution with self-adaptation and local search for constrained multiobjective optimization algorithm (DECMOSA-SQP), which uses the self-adaptation mechanism from DEMOwSA algorithm presented at CEC 2007 and a SQP local search. The constrained handling mechanism is also incorporated in the new algorithm. Assessment o...
Conference Paper
Full-text available
In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation i...
Conference Paper
Full-text available
This paper presents Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization algorithm (DECMOSA-SQP), which uses the self-adaptation mechanism from DEMOwSA algorithm presented at CEC 2007 and a SQP local search. The constrained handling mechanism is also incorporated in the new algorithm. Assessment o...
Chapter
This chapter describes an algorithm for the tuning of a chess program which is based on Differential Evolution using adaptation and opposition based optimization mechanisms. The mutation control parameter F is adapted according to the deviation of search parameters in each generation. Opposition-based optimization is included in the initialization,...
Chapter
The main goal of this chapter is to present an analysis of how self-adaptive control parameters are being changed during the current evolutionary process. We present a comparison of two distinct self-adaptive control parameters’ mechanisms, both using Differential Evolution (DE). The first mechanism has recently been proposed in the jDE algorithm,...
Conference Paper
In this paper we investigate a self-adaptive differential evolution algorithm ( jDEdynNP-F ) where F and CR control parameters are self-adapted and a population size reduction method is used. Additionally the proposed jDEdynNP-F algorithm uses a mechanism for sign changing of F control parameter with some probability based on the fitness values of...
Conference Paper
Full-text available
In this paper, an optimization algorithm is formulated and its performance assessment for large scale global optimization is presented. The proposed algorithm is named DEwSAcc and is based on Differential Evolution (DE) algorithm, which is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces. The original...
Conference Paper
Full-text available
This paper presents performance assessment of differential evolution for multiobjective optimization with self adaptation algorithm, which uses the self adaptation mechanism from evolution strategies to adapt F and CR parameters of the candidate creation in DE. Results for several runs on CEC2007 special session test functions are presented and ass...
Conference Paper
Full-text available
We present a framework for the simulation of forests, where a complex application system simulates a spontaneous afforestation process. Within this virtual environment, trees can be seen to grow over several centuries. The obtained simulation results are used to animate ecosystem development, where trees struggle for survival. The visualization of...
Thesis
Full-text available
Najprej podamo zgodovino in razvoj modeliranja in vizualizacije ekosistemov do danes. Nato navedemo uporabljene metode pri simulaciji in opišemo nekatere poglede na vizualizacijo. Za vizualizacijo ekosistemov je bilo potrebno pripraviti vizualno realistične modele rastlin. Pri tem uporabimo leta 2004 pripravljen interaktivni modelirnik in vizualiza...
Article
Full-text available
In this paper we propose an interactive user friendly tool to design tree models. Our tool enables more flexible and rapid construction of procedural models by use of graphs to design local branch parameters as vector data. Our obtained procedural models can also be shaped by placing them in natural environments. Built procedural models can be used...

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Projects

Projects (5)
Archived project
WEB: http://sicris.izum.si/search/prg.aspx?opt=1&lang=eng&id=6131 Keywords: digital signal and image processing, biomedical engineering, computational geometry, computer graphics, artificial intelligence, computer-aided geometrical design, programming languages, compilers, computer architectures, distributed processing, Internet programming ABSTRACT The Computer Systems, Methodologies, and Intelligent Services group, supported by the governmental programme funding, comprises researchers from five laboratories of the Institute for Computer Science at the Faculty of Electrical Engineering and Computer Science of the University of Maribor. The group pursues two global objectives: firstly, to do scientific research in the fields developed by the individual laboratories, and secondly, to utilize the heterogeneous background and diverse knowledge for synergetic effects. Our scientific research is oriented in the following topics whereat widely competitive results may be expected: - Development of new methodologies for domain-specific languages - Inference about the context-free grammars - Heuristic approaches in graphics and geometric modelling - 3D and 4D compression of graphic and geometric data - Introduction of the Internet personalisation systems and vertical services - Decomposition of complex signals in the cases with less measurements than sources - Analysis of magnetic resonance and ultrasound medical images - Development of virtual environments for training and education The group as a whole tends to take advantage of its complementary knowledge and synergy for the development of intelligent Internet services. We are going to construct a backbone with common vertical services, which will be used by the intelligent solutions with different, aforementioned topics. The system of intelligent services will be publicly accessible and will be developed throughout the existence of the programme group, according to our own expectation and design as well as according to the interaction with the users. SIGNIFICANCE FOR SCIENCE Ubiquitous computing and intelligent services build on ever-more demanding approaches that process sets of signals and images from human living and business environment. Scientific research we conduct contributes new theoretical and practical grounds. Most prominent are the methods for the decomposition of compound signals. Highest-ranked is our convolution kernel compensation method (CKC) which can, the only in the world, decompose surface electromyograms even in dynamic contractions. Advanced decomposition has also been derived for fibre-optic and inertial signals acquired by unobtrusive sensors. Scientific community recognises the efficiency of our detection and recognition approaches in medical imaging. Most success was achieved by 2D and 3D-based reconstruction of ovarian follicles from ultrasound images. Differential evolution was used in the reconstruction of numerically coded procedural models from images of natural trees, which we improved this time with effective encoding of local auxiliary matrix parameters in a procedural model. Thus, we have significantly reduced the dimensionality of the challenge and also added a reconstruction on real trees from photographs of nature, where we automatically detected, extracted, and aligned the reference tree from a photo. We have also shown that it is possible using the reconstructed tree morphology to perform animation of tree sway in the wind and add different patterns of leaves for such tree. The use of Ellenberg ecological indicators in ForestMAS is an important novelty inside the succession models with a lot of potential for further development. Our research in the field of domain-specific languages has been internationally recognized at research level (high number of citations, Editor-In-Chief of Computer Languages, Systems and Structures journal) as well as at business level (we are currently in contacts with USA company about development of proprietary domain-specific language). Furthermore, our research already leads to competitive advantages of Slovenian software companies. We are collaborating with the successful company DEWESoft, where a domain-specific modeling language Sequencer has been developed, which is already successfully used by DEWESoft in measurement systems. The developed semantic and linguistic infrastructure can be used in research areas of text categorization, semantic search, recommendation systems and similar text detection systems. SIGNIFICANCE FOR SLOVENIA Progress of information and communication technologies and biomedical engineering was recognised strategically crucial for the development of Slovenia. We derived services that are based on innovation in the design of new devices, approaches, and algorithms in these fields. Highest relevance appertains to our unique system for decomposition surface electromyograms, which shows first commercial results. Its development depends on part Slovenian funding. A similar observation is valid for the system of unobtrusive sensors that assess human functional-health parameters in the living environment. Also our solutions in the field of ultrasound and X-ray imaging have been accepted by the Slovenian partners. The algorithm meta-GA for parameter control of neural network for fire analysis of steel frame enables the better modeling of steel behavior during fires and consequently improves fire protection building of objects in civil engineering. Interestinglly, steel exhibits very unusual wavy behavior which is a very difficult to model by a close form empirical models when heated to temperatures between 250° C and 600° C. Therefore, the empirical model was replaced with neural network and its parameters were tunned with the meta-GA. Planning the sports training sessions enable amateur and recreative athletes predicting the program of sports training sessions from data obtained with sportswatches during preparation on the more important competitions without expensive and usually busy trainers. Visualization of landscape changes obtained by ForestMAS simulator can make decisive influence on land use policy. The national infrastructure of open acces is a treasury of knowledge provided by Slovenian universities and other research institutions. We expect, that the nature of open availability of this content will improve the visibility and impact of Slovenian research activities.
Project
The Big Data era poses a critically difficult challenge and striking development opportunities in High-Performance Computing (HPC): how to efficiently turn massively large data into valuable information and meaningful knowledge. Computationally effective HPC is required in a rapidly-increasing number of data-intensive domains, such as Life and Physical Sciences, and Socio-economical Systems. Modelling and Simulation (MS) offer suitable abstractions to manage the complexity of analysing Big Data in various scientific and engineering domains. Unfortunately, Big Data problems are not always easily amenable to efficient MS over HPC. Also, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements. Therefore, there is an urgent need for European co-ordination to facilitate interactions among data intensive MS and HPC experts, ensuring that the field, which is strategic and of long-standing interest in Europe, develops efficiently - from academic research to industrial practice. This Action will provide the integration to foster a novel, coordinated Big Data endeavour supported by HPC. It will strongly support information exchange, synergy and coordination of activities among leading European research groups and top global partner institutions, and will promote European software industry competitiveness. WG4: HPC-enabled Modelling for Socio-Economical and Physical Sciences Many types of decisions in society are supported by modelling and simulation. Some examples are political decisions based on predictive simulations of future climate changes, evacuation planning based on faster-than-real-time simulation of tsunamis, and financial market decisions based on mathematical models emulating current market conditions. In all of these situations, large amounts of data such as global geographical information, measurements of the current physical or financial state, and historical data are used both in the model building and model calibration processes. However, also in the predictive phase, there are many applications that not only benefit from, but require HPC due to the complexity of the models, the computational volume, and the amount of data that is being generated in the simulations. Some particularly challenging problem features are high-dimensionality (e.g. in finance or quantum physics) where the computational costs grow exponentially with the dimension, multi-scale physics (e.g. in climate and tsunami simulations) where scales that differ in orders of magnitude need to be resolved to capture the relevant physical processes, and computations under uncertainty, where the impact of uncertain measurements, parameters and models is quantified through multiple evaluations or extended models leading to an increased computational cost (e.g. in safety critical decision problems). Especially in physics, HPC has been successfully employed for a long time. However, existing codes and algorithms are not optimized for modern computer architectures and cannot efficiently exploit massively parallel systems. Furthermore, the increase in available computer power allows for expansion of the horizon of what one can simulate, but the complexity of the systems hampers productivity and progress. In socio-economical sciences the vast amounts of data expected from the fast growth of the internet of things will provide new challenges for the extraction of knowledge. HPC in a distributed model is going to play a major role in such activities and this Action tackles that approach. (Descriptions are provided by the Actions directly via e-COST.) See more at http://www.cost.eu/COST_Actions/ict/IC1406