A Cell-Division Search Technique for Inversion with Application to Picture-Discovery and Magnetotellurics

Solving inverse problems in natural sciences often requires a search pro- cess to find explanatory models that match collected field data. Inverse problems are often under-determined meaning that there are many poten- tial explanatory models for the same data. In such cases using stochastic search, through providing multiple solutions, can help characterise which model features that are most persistent and therefore likely to be real. Unfortunately, in some fields, large parameter spaces can make stochas- tic search intractable. In this work we improve upon previous work by defining a compact and expressive representation and search process able to describe and discover two and three dimensional spatial models. The search process takes place in stages starting with greedy search, followed by alternating stages of evolutionary search and a novel model-splitting process inspired by cell-division. We apply this framework to two prob- lems - magnetotellurics and picture discovery. We show that our improved representation and search process is able to produce detailed models with low error residuals.

Evolution of Images with Diversity and Constraints Using a Generator Network

Evolutionary search has been extensively used to generate artistic images. Raw images have high dimensionality which makes a direct search for an image challenging. In previous work this problem has been addressed by using compact symbolic encodings or by constraining images with priors. Recent developments in deep learning have enabled a generation of compelling artistic images using generative networks that encode images with lower-dimensional latent spaces. To date this work has focused on the generation of images concordant with one or more classes and transfer of artistic styles. There is currently no work which uses search in this latent space to generate images scoring high or low aesthetic measures. In this paper we use evolutionary methods to search for images in two datasets, faces and butterflies, and demonstrate the effect of optimising aesthetic feature scores in one or two dimensions. The work gives a preliminary indication of which feature measures promote the most interesting images and how some of these measures interact.

The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation

Evolutionary algorithms have been used in many ways to generate digital art. We study how evolutionary processes are used for evolutionary art and present a new approach to the transition of images. Our main idea is to define evolutionary processes for digital image transition, combining different variants of mutation and evolutionary mechanisms. We introduce box and strip mutation operators which are specifically designed for image transition. Our experimental results show that the process of an evolutionary algorithm in combination with these mutation operators can be used as a valuable way to produce unique generative art.

Evolutionary Image Transition Based on Theoretical Insights of Random Processes

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.