# Stochastic Global Optimization Algorithms: A Systematic Formal Approach

## TL;DR

This paper develops a comprehensive formal framework for stochastic global optimization algorithms, using advanced probability theory to analyze their structure, convergence, and combination methods.

## Contribution

It introduces a systematic formal approach with new concepts like join-kernels and optimization space for analyzing SGoals.

## Key findings

- Proves convergence conditions for certain SGoals.
- Represents algorithmic functions as kernels.
- Analyzes combination of stochastic methods.

## Abstract

As we know, some global optimization problems cannot be solved using analytic methods, so numeric/algorithmic approaches are used to find near to the optimal solutions for them. A stochastic global optimization algorithm (SGoal) is an iterative algorithm that generates a new population (a set of candidate solutions) from a previous population using stochastic operations. Although some research works have formalized SGoals using Markov kernels, such formalization is not general and sometimes is blurred. In this paper, we propose a comprehensive and systematic formal approach for studying SGoals. First, we present the required theory of probability (\sigma-algebras, measurable functions, kernel, markov chain, products, convergence and so on) and prove that some algorithmic functions like swapping and projection can be represented by kernels. Then, we introduce the notion of join-kernel as a way of characterizing the combination of stochastic methods. Next, we define the optimization space, a formal structure (a set with a \sigma-algebra that contains strict \epsilon-optimal states) for studying SGoals, and we develop kernels, like sort and permutation, on such structure. Finally, we present some popular SGoals in terms of the developed theory, we introduce sufficient conditions for convergence of a SGoal, and we prove convergence of some popular SGoals.