Amazing geometry of genetic space or are genetic algorithms convergent?

TL;DR

This paper discusses the potential for convergence in genetic algorithms, emphasizing the importance of crossover operators and proposing that soft selection schemes are superior, aiming to convince readers of genetic algorithms' inherent success potential.

Contribution

It provides arguments and insights into genetic algorithms' convergence, highlighting the effectiveness of soft selection over other schemes.

Findings

Soft selection is proven to be superior to other selection schemes.

Arguments suggest genetic algorithms are inherently bound for success.

Focus on crossover operators enhances understanding of genetic algorithm convergence.

Abstract

There is no proof yet of convergence of Genetic Algorithms. We do not supply it too. Instead, we present some thoughts and arguments to convince the Reader, that Genetic Algorithms are essentially bound for success. For this purpose, we consider only the crossover operators, single- or multiple-point, together with selection procedure. We also give a proof that the soft selection is superior to other selection schemes.