Michal Tomczyk

Michal Tomczyk
Poznan University of Technology · Institute of Computing Science

Ph.D.

About

13
Publications
619
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115
Citations
Introduction
Assistant at Institute of Computing Science, Poznan University of Technology. A member of Laboratory of Intelligent Decision Support Systems. Main fields of interests are: multi-objective optimization, evolutionary algorithms, decision support & preference learning. Works published in: IEEE TEVC, Omega, COR, GDN. Page: www.cs.put.poznan.pl/mtomczyk
Additional affiliations
July 2015 - present
Poznan University of Technology
Position
  • Professor (Assistant)

Publications

Publications (13)
Article
We propose a novel co-evolutionary algorithm for interactive multiple objective optimization, named CIEMO/D. It aims at finding a region in the Pareto front that is highly relevant to the Decision Maker (DM). For this reason, CIEMO/D asks the DM, at regular intervals, to compare pairs of solutions from the current population and uses such preferenc...
Article
We introduce a family of interactive evolutionary algorithms for Multiple Objective Optimization (MOO). In the phase of preference elicitation, a Decision Maker (DM) is asked to compare some pairs of solutions from the current population. Such holistic information is represented by a set of compatible instances of achievement scalarizing or quasi-c...
Conference Paper
We propose a novel robust indicator-based algorithm, called IEMO/I, for interactive evolutionary multiple objective optimization. During the optimization run, IEMO/I selects at regular intervals a pair of solutions from the current population to be compared by the Decision Maker. The successively provided holistic judgements are employed to divide...
Article
We propose a decomposition-based interactive evolutionary algorithm for multiple objective optimization. During an evolutionary search, a Decision Maker is asked to compare pairwise solutions from the current population. Using the Monte Carlo simulation, the proposed algorithm generates from a uniform distribution a set of instances of the preferen...
Article
We propose a family of algorithms, called EMOSOR, combining Evolutionary Multiple Objective Optimization with Stochastic Ordinal Regression. The proposed methods ask the Decision Maker (DM) to holistically compare, at regular intervals, a pair of solutions, and use the Monte Carlo simulation to construct a set of preference model instances compatib...
Chapter
We present a new interactive evolutionary algorithm for Multiple Objective Optimization (MOO) which combines the NSGA-II method with a cone contraction method. It requires the Decision Maker (DM) to provide preference information in form of a reference point and pairwise comparisons of solutions from a current population. This information is repres...
Article
Full-text available
We present a set of interactive evolutionary multiple objective optimization (MOO) methods, called NEMO-GROUP. All proposed approaches incorporate pairwise comparisons of several decision makers (DMs) into the evolutionary search, though evaluating the suitability of solutions for inclusion in the next population in different ways. The performance...
Article
Full-text available
This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalariz...
Conference Paper
We present an interactive evolutionary multiple objective optimization (MOO) method incorporating preference information of several decision makers into the evolutionary search. It combines NSGAII, a well-known evolutionary MOO method, with some interactive valuebased approaches based on the principle of ordinal regression.We introduce several vari...