Evolutionary Image Transition Based on Theoretical Insights of Random Processes

TL;DR

This paper explores how theoretical insights from evolutionary algorithms and random processes can be applied to create artistic image transitions, blending computational theory with creative visual effects.

Contribution

It introduces the concept of evolutionary image transition, combining mutation and random walk techniques for artistic image transformation based on theoretical evolutionary insights.

Findings

Demonstrates methods for artistic image transition using evolutionary algorithms.

Shows how random walk variants influence artistic effects.

Provides a framework connecting theoretical analysis to practical evolutionary art creation.

Abstract

Evolutionary algorithms have been widely studied from a theoretical perspective. In particular, the area of runtime analysis has contributed significantly to a theoretical understanding and provided insights into the working behaviour of these algorithms. We study how these insights into evolutionary processes can be used for evolutionary art. We introduce the notion of evolutionary image transition which transfers a given starting image into a target image through an evolutionary process. Combining standard mutation effects known from the optimization of the classical benchmark function OneMax and different variants of random walks, we present ways of performing evolutionary image transition with different artistic effects.