Tomasz Białaszewski

Designing FDI observers by improved evolutionary multi-objective optimization

Abstract The paper considers using evolutionary multi-objective optimization (EMO) for solving FDI design problems. In order to prevent genetic procedures from premature convergence and to increase the effectiveness of searching for sought optimal solutions, different types of niching allow taking care of their diversity in consecutive generations. Another problem results from considering design objectives of high dimensions, when the conception of Pareto-domination is not effective. The paper discusses the characteristics of several innovative mechanisms concerning the methods of selection and parental crossover, based on concepts of niches and genders.

Multi-Gender Genetic Optimization of Diagnostic Observers

Abstract A new evolutionary method of solving multiobjective optimization problems is presented. In this method the information about the gender (sex) of individuals is utilized to draw a distinction between different groups of objectives. In particular, this information extracted from the fitness of individuals is applied in genetic crossovers of parents during a process of optimization. Main features of this mechanism are described. The performance of the resultant evolutionary algorithm used in an exemplary multiobjective state-observer synthesis is presented which is based on genetic optimization with the use of three genders.

Solving Highly-Dimensional Multi-Objective Optimization Problems by Means of Genetic Gender

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 crossover in the performed evolutionary multi-objective optimization (EMO) processes.

Gender Approach to Multi-Objective Optimization of Detection Systems with Pre-selection of Criteria

Novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA) and illustrates its performance by means of examples of multi-objective optimization tasks.

Improving evolutionary multi-objective optimization using genders

In solving highly dimensional multi-objective optimization (EMO) problems by evolutionary computations the concept of Pareto-domination appears to be not effective. The paper discusses a new approach to EMO by introducing a concept of genetic genders for the purpose of making distinction between different groups of objectives. This approach is also able to keep diversity among the Pareto-optimal solutions produced.

Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

ABSTRACT 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 and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the ‘curse’ of dimensionality typical of many engineering designs.

Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems

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, such as those based on the true Pareto front, are difficult to calculate. Whereas, on the other hand, the proposed approximated quality criteria are easy to implement, computationally inexpensive, and sufficiently effective.

Designing FDI Observers by Improved Evolutionary Multi-Objective Optimization

: The paper considers using evolutionary multi-objective optimization (EMO) for solving FDI design problems. In order to prevent genetic procedures from premature convergence and to increase the effectiveness of searching for sought optimal solutions, different types of niching allow taking care of their diversity in consecutive generations. Another problem results from considering design objectives of high dimensions, when the conception of Pareto-domination is not effective. The paper discusses the characteristics of several innovative mechanisms concerning the methods of selection and parental crossover, based on concepts of niches and genders. Copyright©2006 IFAC