Tone mapping is a computational task of significance in the context of displaying high dynamic range images on low dynamic range devices. While a number of tone mapping algorithms have been proposed and are in common use, there is no single operator that yields optimal results under all conditions. Moreover, obtaining satisfactory mappings often requires the manual tweaking of parameters. This thesis proposes interactive evolution as a computational tool for tone mapping. An evolution strategy that blends the results from several tone mapping operators while at the same time adapting their parameters is proposed. As well, the results are adapted such that such that approximately uniform perceptual distances between offspring candidate solutions and the parent are ensured. The introduction of a perceptually based step size adaptation technique enhances the control of the variability between newly generated offspring, when compared to parameter space step size adaptation.

Tone mapping is a computational task of significance in the context of displaying high dynamic range images on low dynamic range devices. While a number of tone mapping algorithms have been proposed and are in common use, there is no single operator that yields optimal results under all conditions. Moreover, obtaining satisfactory mappings often requires the manual tweaking of parameters. This thesis proposes interactive evolution as a computational tool for tone mapping. An evolution strategy that blends the results from several tone mapping operators while at the same time adapting their parameters is proposed. As well, the results are adapted such that such that approximately uniform perceptual distances between offspring candidate solutions and the parent are ensured. The introduction of a perceptually based step size adaptation technique enhances the control of the variability between newly generated offspring, when compared to parameter space step size adaptation.

Across many scientific disciplines, there has emerged an open opportunity to utilize the scale and reach of the Internet to collect scientific contributions from scientists and non-scientists alike. This process, called citizen science, has already shown great promise in the fields of biology and astronomy. Within the fields of artificial life (ALife) and evolutionary computation (EC) experiments in collaborative interactive evolution (CIE) have demonstrated the ability to collect thousands of experimental contributions from hundreds of users across the glob. However, such collaborative evolutionary systems can take nearly a year to build with a small team of researchers. This dissertation introduces a new developer framework enabling researchers to easily build fully persistent online collaborative experiments around almost any evolutionary domain, thereby reducing the time to create such systems to weeks for a single researcher. To add collaborative functionality to any potential domain, this framework, called Worldwide Infrastructure for Neuroevolution (WIN), exploits an important unifying principle among all evolutionary algorithms: regardless of the overall methods and parameters of the evolutionary experiment, every individual created has an explicit parent-child relationship, wherein one individual is considered the direct descendant of another. This principle alone is enough to capture and preserve the relationships and results for a wide variety of evolutionary experiments, while allowing multiple human users to meaningfully contribute. The WIN framework is first validated through two experimental domains, image evolution and a new two-dimensional virtual creature domain, Indirectly Encoded SodaRace (IESoR), that is shown to produce a visually diverse variety of ambulatory creatures. Finally, an Android application built with WIN, "filters, allows users to interactively evolve custom image effects to apply to personalized photographs, thereby introducing the first CIE application available for any mobile device. Together, these collaborative experiments and new mobile application establish a comprehensive new platform for evolutionary computation that can change how researchers design and conduct citizen science online.

Interactive evolution is explored and applied for the automatic generation of 8-bit sound effects (SFX). Procedurally generating a type of content can result in greater accessibility, cut development costs, and more. However, a natural problem that follows this approach is user fatigue. An 8-bit SFX generator is developed, tested, and then evaluated to understand its capabilities, usability, and effectiveness of several applicable solutions for reducing user fatigue. Results indicate that the software is intuitive to learn and use while providing a decent variety to the generated content with a probable feeling of progression. The implemented solutions for abating user fatigue show promise towards making the software practically viable. However, there are still areas in the developed artifact that suggest for further study.

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