Determination of weight coefficients for additive fitness function of genetic algorithm

TL;DR

This paper develops a method for analytically determining weight coefficients in a genetic algorithm's additive fitness function, enhancing search engine relevance without expert input.

Contribution

It introduces a formal description and analytical methods for calculating weight factors, supported by experimental validation and illustrative examples.

Findings

Graphical analysis of fitness function behavior

Effective weight configurations for genetic algorithm

Elimination of expert assessment in weight determination

Abstract

The paper presents a solution for the problem of choosing a method for analytical determining of weight factors for a genetic algorithm additive fitness function. This algorithm is the basis for an evolutionary process, which forms a stable and effective query population in a search engine to obtain highly relevant results. The paper gives a formal description of an algorithm fitness function, which is a weighted sum of three heterogeneous criteria. The selected methods for analytical determining of weight factors are described in detail. It is noted that expert assessment methods are impossible to use. The authors present a research methodology using the experimental results from earlier in the discussed project "Data Warehouse Support on the Base Intellectual Web Crawler and Evolutionary Model for Target Information Selection". There is a description of an initial dataset with data ranges for calculating weights. The calculation order is illustrated by examples. The research results in graphical form demonstrate the fitness function behavior during the genetic algorithm operation using various weighting options.