Aleksander Byrski

AGH University of Science and Technology in Kraków | AGH · Institute of Computer Science

In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-po...

Ant Colony Optimization (ACO) is an acclaimed method for solving combinatorial problems proposed by Marco Dorigo in 1992 and has since been enhanced and hybridized many times. This paper proposes a novel modification of the algorithm, based on the introduction of a two-dimensional pheromone into a single-criteria ACO. The complex structure of the p...

Gas composition and light mode of industrial greenhouses are some of the most determining factors in the process of growing vegetable crops in greenhouse conditions. The intelligentisation of information technologies for monitoring and control based on artificial intelligence methods can increase the efficiency of agrotechnical procedures for green...

Efficient solutions of the N-body problem make it possible to conduct large-scale physical research on the rules governing our universe. Vast amount of communication needed in order to make each body acquainted with the information on position of other bodies renders the accurate solutions very quickly inefficient and unreasonable. Many approximate...

Following the introduction of the socio-cognitive caste-based algorithms into the classic evolutionary metaheuris-tics, in this paper we focus on similar task regarding agent-based universal optimization methods. We tackle EMAS and DE algorithms and enrich them also with TOPSIS-inspired mechanism. Besides giving the details of the methods and the b...

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background a...

Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain based model. Now the research continues on introducing autonomous hybridization into EMAS. This paper focuses on a proposed hybrid version of the EMAS, a...

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background a...

In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration...

Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain based model. Now the research continues on introducing autonomous hybridization into EMAS. This paper focuses on a proposed hybrid version of the EMAS, a...

Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2...

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the...

The necessity of the collision-free determination of movement trajectory can be observed among both animals and people. In a completely autonomous way, unrelated units make decisions on choosing their movement trajectory while moving in a common space. There is no explicit communication between the individual participants of the movement, and there...

The paper presents an idea of training an artificial neural network a relation between different parameters observed for a population in a metaheuristic algorithm. Then such trained network may be used for controlling other algorithms (if the network is trained in such way, that the knowledge gathered by it becomes agnostic regarding the problem)....

The main goal of the research presented in this paper was to estimate the performance of applying neural networks trained with the usage of a chaotic model, that may serve as hashing functions. The Lorenz Attractor chaotic model was used for training data preparation, and Scaled Conjugate Gradient was used as a training algorithm. Networks consiste...

The ongoing period of the pandemic makes everybody focused on the matters related to fighting this immense problem posed to the societies worldwide. The governments deal with the threat by publishing regulations which should allow to mitigate the pandemic, walking at thin ice as the decision makers do not always know how to properly respond to the...

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of i...

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the...

Biclustering is a technique of detecting meaningful patterns in tabular data. It is also one of the fields in which evolutionary algorithms have risen to the very top in terms of speed and accuracy. In this short paper we summarize the results of porting one of the leading evolutionary-based biclustering methods EBIC to Julia-an emerging high-end p...

Despite fast progress in the automotive industry, the number of deaths in car accidents is constantly growing. One of the most important challenges in this area, besides crash prevention, is immediate and precise notification of rescue services. Automatic crash detection systems go a long way towards improving these notifications, and new cars curr...

The paper focuses on complex metaheuristic algorithms, namely multi-objective hierarchical strategy, which consists of a dynamically evolving tree of interdependent demes of individuals. The main contribution presented in this paper is the introduction of elitism in a form of an archive, locally into the demes and globally into the whole tree and d...

Socio-cognitive computing is a paradigm developed for the last several years, it consists in introducing into metaheuristics mechanisms inspired by inter-individual learning and cognition. It was successfully applied in hybridizing ACO and PSO metaheuristics. In this paper we have followed our previous experiences in order to hybridize the acclaime...

Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multiple areas, including development of biomarkers for cancer, disease subtype identification, or gene-drug interactions among others. In this paper we introduce EBIC.JL - an...

Different hybrid optimization metaheuristics (see the works of Talbi for classification) either assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a local search inside an evolutionary algorithm—a memetic algorithm) or creating a chain of algorithms. In this paper, such a chain combination of two algorithms (n...

This book constitutes the refereed post-conference proceedings of the First IFIP TC 5 International Conference on Computer Science Protecting Human Society Against Epidemics, ANTICOVID 2021, held virtually in June 2021. The 7 full and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are concerned with a...

Using agent-based systems for computing purposes, where agent becomes not only driver for realizing computing task, but a part of the computing itself is an interesting paradigm allowing for easy yet robust design of metaheuristics, making possible easy parallelization and developing new efficient computing methods. Such methods as Ant Colony Optim...

Personalized pharmacotherapy has become a new paradigm of safe and efficient treatment and its role is specifically seen nowadays in the COVID-19 era. The therapies explored for SARS-CoV-2 are based on repurposed marketed antiviral drugs that have known cardiac safety issues. It gives a perfect example of the need to develop a tool that helps to op...

Hierarchical Genetic Strategy (HGS) is a general-purpose optimization metaheuristic based on multi-deme evolutionary-like optimization, while demes are parts of adaptive dynamically changing tree. The paper focuses on adaptation of the classic HGS algorithm for multi-criteria optimization problems, coupling the HGS with Particle Swarm Optimization...

Efficient information flow in the complex, often microscale simulation systems such as the social, artificial life, or traffic ones poses a significant challenge. It is difficult to implement a highly scalable system due to algorithmic problems, which significantly hamper the efficiency, especially in the case of maintaining a synchronized state in...

The problem of efficient pedestrian simulation, when large-scale environment is considered, poses a great challenge. When the simulation model size exceeds the capabilities of a single computing node or the results are expected quickly, the simulation algorithm has to use many cores and nodes. The problem considered in the presented work is the tas...

Metaheuristics have significant computing requirements, in particular Ant Colony Optimization (ACO) processes a population of individuals (agents/ants) roaming in a graph, leaving the pheromone trails and getting inspired by its amount perceived on the edges. If the considered problem instance is large or the time is crucial, one can try to leverag...

This book constitutes revised selected papers from the 21st International Symposium on Trends in Functional Programming, TFP 2020, which was held in Krakow, Poland, during February 13-14, 2020. The 11 full papers presented in this volume were carefully reviewed and selected from 22 submissions. They were organized in topical sections named: domain-...

The parallelization of metaheuristics and care for the efficient use of the available infrastructure is very popular in the case of population-based algorithms (e.g., evolutionary ones), as many of them have structures intrinsically easy for parallelization. However, swarm computing algorithms (ACO in particular) must use certain global knowledge i...

In this paper a socio-cognitive ACO-type algorithm is proposed for multi-criteria TSP problem optimization. This algorithm is rooted in psychological inspirations and follows other socio-cognitive swarm intelligence methods proposed up to now. This paper presents the idea and shows the applicability of the proposed algorithm based on selected bench...

The task of automated intruder detection and interception is often considered as a suitable application for groups of mobile robots. Realistic versions of the problem include representing uncertainty, which turns it into NP-hard optimization tasks. In this paper we define the problem of indoor intruder interception with probabilistic intruder motio...

High-performance computing systems make it possible to implement large-scale simulations of natural phenomena. However, in order to develop efficacious and efficient solutions, easy-to-use software platforms and applications are required. Up until now, the most popular solutions in this area were based on the message passing interface. Next, the au...

Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, hig...

The Iterated Prisoner’s Dilemma (IPD) game is a one of the most popular subjects of study in game theory. Numerous experiments have investigated many properties of this game over the last decades. However, topics related to the simulation scale did not always play a significant role in such experimental work. The main contribution of this paper is...

The aim of this research is to build an open schema model for a digital sources repository in a relational database. This required us to develop a few advanced techniques. One of them was to keep and maintain a hierarchical data structure pushed into the repository. A second was to create constraints on any hierarchical level that allows for the en...

Nature-inspired metaheuristics are very popular these days; their creation is usu- ally justified based on the “no free lunch” theorem by Wolpert and MacReady. How- ever, the creation of novel metaheuristics should be realized with care, not only for the sake of creation (cf. Sörensen reports on superficial metaheuristics); in other words, the insp...

The parallel implementation of complex, often micro-scale simulation systems such as social, artificial life, or traffic systems poses a significant challenge for scientists and often requires the use of supercomputer devices. At the same time, it is quite difficult to develop a software system capable of being scaled up to hundreds or thousands of...

In this paper a novel hybridization of agent-based evolutionary system (EMAS, a metaheuristic putting together agency and evolutionary paradigms) is presented. This method assumes utilization of particle swarm optimization (PSO) for upgrading certain agents used in the EMAS population, based on agent-related condition. This may be perceived as a me...

Modern, highly concurrent, and large-scale systems require new methods for design, testing, and monitoring. Their dynamics and scale require real-time tools that provide a holistic view of the whole system and the ability to show a more detailed view when needed. Such tools can help identify the causes of unwanted states, which is hardly possible w...

A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized with socio-cognitive inspirations, turned out to generate interesting results when compared to classic ACO. Even though it does not always find better solutions to the considered problems, it usually finds sub-optimal solutions. Moreover, instead of a trial-and-error...

Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying paral...

The scalable implementation of a microscopic simulation, presented in our previous work, opens new areas of applications for traffic simulation, namely short term traffic forecasting. It can be used for real-time prediction of local, exceptional situations. Moreover it can be used as a hypothesis verification tool for evaluating different strategie...

Mobile Computing and Mobile Cloud Computing are the areas where intensive research is observed. The "mobility" landscape (devices, technologies, apps etc.) evolves so fast that definitions and taxonomies don't catch up so dynamic changes and there is still an ambiguity in definitions and common understanding basic ideas and models. In our research...

This workshop seeks to integrate results from different domains of computer science, computational science, and mathematics. We welcome simulation papers, either hard simulations using finite element or finite difference methods, or soft simulations by means of evolutionary computations, and related methods. The workshop focuses on simulations perf...

Volunteer computing is a very appealing way of utilizing vast available resources in an efficient way. However currently available platforms supporting this computing style are either difficult to use or not available at all, being the results of e.g. finished scientific projects. In this paper a novel, lightweight volunteer computing platform is p...

Hybridizing agent-based paradigm with evolutionary or memetic computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones. In the article, an evolutionary multi-agent system (EMAS) is applied to solve difficult discrete bench...

Considering the “no free lunch theorem” (Wolpert and Macready, IEEE Trans Evolut Comput 67(1), 1997, [297]), it is still important to try to test how the examined metaheuristic works when applied to different well-known problems. That is why several well-known benchmark functions are considered in the following experimental study (Digalakis and Mar...

Development of systems based on the already presented ideas of agent-based computing requires relevant tools supporting their design and execution, adequate to the required problem scale. The preliminary analysis of the features of the available agent-based platform and simulation frameworks has been conducted. It showed clearly that available fram...

When facing lack of algorithms suited for many important class of computing problems, promising results can be obtained using heuristic-based techniques, utilizing different nature-oriented, physical or social inspirations. Such algorithms are very often characterized as belonging to of computational intelligence methods [104, 251], because of its...

In the 1970s, there was a growing interest in the systems, where a task to be solved was decomposed into smaller parts (subtasks), in order to solve them separately and later integrate the solution. This approach may be described as distributed problem solving, and it is usually easy to implement in parallel environments such as multi-core machines...

Agents introduce a conceptual model for distributed problem solving, declaring autonomy as a core feature of its building blocks. This makes them aware of the environment, support interactions among them and the environment and realize different actions focused on fulfilling the goal assigned the by the user or designer. It seems that implementatio...

AgE environment has been developed as an open-source project at the Intelligent Information Systems Group of AGH-UST. AgE provides a platform for the development and execution of distributed agent-based applications—mainly simulation and computational systems. The most advanced version of the environment was developed using Java (jAgE), but there w...

Having shown that EMAS approaches are effective in solving selected benchmark and real-life problems, it would be interesting to take an insight into the exact features of the most important mechanism of EMAS, i.e. the distributed selection based on existence of non-renewable resource. Such experiments could help to understand it and tune the compu...

As stated in the previous chapter, agent-based metaheuristics, such as EMAS and iEMAS have already proved to be good techniques for solving difficult search problems (see, e.g. [44–47, 92, 263] to point out a few). However, in order to make sure that such a complex tool may be useful, both formal analysis and experimental verification should be con...