dr inż. Tomasz Białaszewski | Gdańsk University of Technology

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dr inż. Tomasz Białaszewski

Contact:

email:
tombiala@pg.edu.pl
website:
https://mostwiedzy.pl/tomasz-bialaszewski

Positions:

Assistant professor

workplace:
Katedra Systemów Decyzyjnych i Robotyki
Budynek A Wydziału Elektroniki, Telekomunikacji i Informatyki, EA 205
phone:
(58) 347 14 57

Functions:

Deputy Head of Department

dr inż. Tomasz Białaszewski

Publications:

  1. Publication

    In this paper we propose new improved approximate quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO). In the performed comparative study we take into account the various EMO algorithms of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces. It is well known that useful executive criteria, such as those based on the true Pareto...

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  2. Publication

    This paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria,...

    Full text available to download

  3. Publication

    This paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria,...

    Full text available to download

  4. A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals...

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  5. Publication

    Paper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental...

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Projects: