Paweł B. Myszkowski

Wrocław University of Science and Technology | WUT · Department of Computational Intelligence

The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approx...

multi- and single objective optimisation metaheuristic

The Planning and Scheduling (PS) problem plays a vital role in several domains, such as economics, military, management, finance, and games, where finding the optimal plan and schedule to achieve specific goals is essential. In this article, we present a Genetic Algorithm for the Planning and Scheduling (GAPS) problem in the StarCraft II Build Orde...

The paper presents a redefinition of Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) as a many-objective optimization problem. In effect, it brings the problem closer to real-world applications. Five objectives have been defined: cost, duration, average cash flow, average usage of resources and skill overuse. To summarize MS-...

This paper presents a method that took second place in the GECCO 2020 Competition on Niching Methods for Multimodal Optimization. The method draws concepts from combinatorial multi–objective optimization, but also adds new mechanisms specific for continuous spaces and multi–modal aspects of the problem. GAP Selection operator is used to keep a high...

The paper introduces a novel many-objective evolutionary method, with a diversity-based selection operator and aims to fill the “gaps” in the Pareto Front approximation and to increase its spread. It shows, that guiding the evolution process towards the least explored parts of a space increases overall diversity, but can also lead to increased conv...

This article presents the results of a survey regarding architects' expectations towards software for automated floor plan generation (AFPG) and optimisation processes in architectural design. More than 150 practising architects from Poland and abroad took part in the survey. Survey results were then extracted, ordered and interpreted with the use...

The paper presents the ELISi multi-criteria optimisation application for AFPG based on Hybrid Evolutionary Algorithm (HEA). The research aims to create functional computational design tool for architects, mimicking the workflow of architectural design process. The article includes explanation of the proposed approach: problem representation, geneti...

This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE datase...

In this paper a modified selection operator is presented in combination with classical Non-dominated Sorting Genetic Algorithm II (NSGA-II). It is shown that various modifications can lead to increased convergence, spread or uniformity of achieved Pareto fronts. A clone prevention method is used to increase the spread of resulting sets. Furthermore...

In recent years interest in multiobjective optimization has flourished. Many Quality Measures (QM) have been developed to allow comparison of results gained by many methods. Unfortunately significant amount of various QMs along with the lack of imposed taxonomy have caused vagueness in the naming conventions. Hence a cohesive taxonomy is proposed t...

The article presents the application of Hybrid Evolutionary and Greedy-based algorithms to the problem of Automated Floor Plan Generation. The described optimization issue is part of a wider domain of Computer-Aided Architectural Design. The article covers the extensive description of the representation domain model (architectural canonical guideli...

MS-RCPSP problem, instances 100_10_27_9_D2. The comparison of Pareto Fronts gained by NSGA-II and NTGA. Details in publication: Laszczyk M., Myszkowski P.B. "Improved selection in evolutionary multi–objective optimization of Multi–Skill Resource–Constrained project scheduling problem", Information Sciences, 2019, https://doi.org/10.1016/j.ins.2019....

The chapter describes the design guidelines and technical specification for the implementation of an application supporting the architectural design of floor plans in a computational way with the use of Genetic Algorithms. The chapter begins with a brief background of ongoing research and an outline of the problem. The chapter focuses on the algori...

The article discusses new approach for possible theoretical solution for the support of architectural design of floor plans in a computational way, presents theoretical design guidelines regarding the application supporting the architectural design process and briefly describes specification for the first implementation of the application prototype...

This article is an overview focused on functionality and usability of selected contemporary approaches for the computational floor plan generation of architectural objects. This article describes current solutions for generative architectural design and focuses on their usability from the point of view of architectural design practice. Recent resea...

Paper presents a hybrid Differential Evolution and Greedy Algorithm (DEGR) applied to solve Multi-Skill Resource-Constrained Project Scheduling Problem. The specialized indirect representation and transformation of solution space from discrete (typical for this problem), to continuous (typical for DE-approaches) are proposed and examined. Furthermo...

The paper describes an application of Greedy Randomized Adaptive Search Procedure (GRASP) in solving Multi–Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP). Proposed work proposes a specific greedy–based local search and schedule constructor specialised to MS-RCPSP. The GRASP is presented as the better option to classical heuristic...

In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving Multi--Skill Resource Constrained Project Scheduling Problem (MS--RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with Ant Colony Optimization (ACO). Furthermore, a novel approach for updating ph...

In this paper, we define a new practical technology-driven Resource Constrained Scheduling Problem (t-RCPSP). We propose three approaches, applying constructive heuristics to tackle effectively the practical application of RCPSP. In the RCPSP formulation, the constraints are defined to design the tasks in the spaces constructed by non- and renewabl...

In this paper novel project scheduling difficulty estimations are proposed for Multi--Skill Resource--Constrained Project Scheduling Problem (MS--RCPSP). The main goal of introducing the complexity estimations is an attempt of estimation the project complexity before launching the optimization process. What is more, the dataset instance generator i...

Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) benchmark iMOPSE dataset: consists of 36 instances and solution validator. iMOPSE dataset described in: Myszkowski P.B., Skowroński M., Sikora K., "A new benchmark dataset for Multi-Skill Resource-Constrained Project Scheduling Problem", Proceedings of the 2015 Federated Confe...

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating phe...

In this article two approaches of Tabu Search in Multi–Skill Resource–Constrained Project Scheduling Problem (MS–RCPSP) have been proposed, based on different neighbourhood generation methods. The first approach assumes swapping resources assigned to pair of tasks, while the second one proposes assigning any resource that could perform given task....

In this article some novel scheduling heuristics for Multi–Skill Resource–Constrained Project Scheduling Problem have been proposed and compared to state-of-the-art priority rules, based on task duration, resource salaries and precedence relations. New heuristics stand an aggregation of known methods, but are enhanced by skills domain. The goal of...

In this paper specialized genetic operators for Evolutionary Algorithms in the Multi{Skill Resource Constrained Scheduling Problem have been proposed. The problem objective is to assign resources to tasks to make the ?nal schedule as short or/and cheap as possible (multi{objective optimization). In our approach domain knowledge has been applied to...

In this paper specialized genetic operators for Evolutionary Algorithms in the Multi-Skill Resource- Constrained Scheduling Problem have been proposed. The problem objective is to assign resources to tasks to make the final schedule as short or/and cheap as possible (multi{objective optimization). In our approach domain knowledge has been applied t...

We proposed an evolutionary algorithm (EA) usage to image clustering applied to Document Search Engine (DSE). Each document is described by its visual content (including images), preprocessed and clustered by EA. Next, such clusters are core of DSE. However, number of documents and attached images make EA ineffective in such task. Using the natural...

The paper presents a method of information extraction from overview maps. The idea is based on recognizing text located on the map and on finding locations corresponding to the extracted text labels using the GeoNames ontology. The method consists of three phases. The first one performs map image processing in order to recognize text labels. The ne...

Choosing model parameters is an important issue for solving real word problems. Wrong parameter values result in low performance of employed model. Usually, parameters are chosen manual, but one can employ metaheuristics for searching the parameter space in more systematic and automated way. In this paper we test a few optimisation methods such as...

This paper presents approaches to hierarchical clustering of images using a GHSOM in application as image search engine. It is analysed some hierarchical clustering and SOMs variants. Experiments are based on benchmark ICPR and MIRFlickr image datasets. As quality of gained solution the external and the internal measures are analysed.

This paper describes our experiments in the field of evolutionary algorithms for rule extraction applied to automating image annotation and classification problems. Presented approach is based on classical evolutionary algorithm with binary representation of ’if-then’ rules. We want to show that some search space reduction techniques make possible...

This paper presents document search model based on its visual content. There is used hierarchical clustering algorithm - GHSOM. Description of proposed model is given as learning and searching phase. Also some experiments are described on benchmark image sets (e.g. ICPR, MIRFlickr) and created document set. Paper presents some experiments connected...

This paper shows an evolutionary algorithm application to generate profitable strategies to trade futures contracts on foreign exchange market (Forex). Strategy model in approach is based on two decision trees, responsible for taking the decisions of opening long or short positions on Euro/US Dollar currency pair. Trees take into consideration only...

This paper describes our last research results in the field of evolutionary algorithms for rule extraction applied to classification (and image annotation). We focus on the data mining classification task and we propose evolutionary algorithm for rule extraction. Presented approach is based on binary classical genetic algorithm with representation...

This paper presents an application of coevolutionary algorithms to rule discovery on stock market. We used genetic programming techniques with coevolution in financial data mining process. There were tested a various approaches to include coevolution aspects in task of build trading rule (buy and sell decision). Trading rules are based on technical...

This paper presents the system for automatic emotion detection from music data stored in MIDI format files. First, the piece of music is divided into independent segments that potentially represent different emotional states. For this task the method of segmentation is used. The most important part is a features extraction from the music data. On t...

The paper presents an agent called Learning Assistant, which is responsible for defining individual learning paths for pupils in e-learning environment. The Assistant is able to infer using metadata described pupils and didactic materials; this inference is a basis for building the individual learning path for each pupil. To build a learning path f...

This chapter presents a new evolutionary approach to the Graph Coloring Problem (GCP) as a generalization of some scheduling problems: timetabling, scheduling, multiprocessor scheduling task and other assignment problems. The proposed evolutionary approach to the Graph Coloring Problem utilizes information about the conflict localization in a given...

This paper presents a new approach to the graph coloring problem (GCP) which utilizes information about conflict localization in a given coloring. In this context a partial fitness function (pff) and its usage to specialize genetic operators and phenotypic measure of diversity in population are described. Particular attention is given to the invest...

The timetable problem is known as multi-dimensional NP-complete, involving combinatorial optimization and it is a representative of the multi-constrained class. In this paper we present an application of evolutionary algorithm, a rules induction algorithm in evolution process, called IBIS (Iteration Build Solution). IBIS uses the matrix representat...

The university course timetabling problem is hard and time-consuming to solve. Profits from full automatisation of this process can be invaluable. This paper describes architecture and operation of two automatic timetabling systems. Both are based on evolutionary algorithms, with specialised genetic operators and penalty-based evaluation function....