High shear melt granulation (HSMG) is an established technology for a production of densified granules. In this project, it was used as a novel solvent-free method for the preparation of cocrystals. Cocrystals produced by HSMG were compared to those prepared by Hot Melt Extrusion (HME) to investigate the influence of variable parameters and conditions on the process of cocrystal conversion. The potential for the active control of cocrystals polymorphism utilising the intrinsic properties of lipids was also investigated in this project. Different cocrystal pairs were prepared by both cocrystallisation methods using glycol derivative polymers. Thermal analysis, powder X-ray diffraction and Raman spectroscopy were used as analytical techniques to determine the cocrystal yield and purity. The results obtained from HSMG suggest that sufficient binder concentrations (above 12.5% w/w) in a molten state and continuous shearing force are necessary to achieve a complete cocrystals conversion. Further increase in binder concentration (15% w/w) was found to provide more regular shape and smooth surface to the prepared spherical granules. Cocrystals preparation by HME was achievable after introducing a mixing zone to the extruder configuration (Conf B and Conf C) providing densified extrudates containing pure cocrystals. In conclusion, HSMG was found as a versatile technique for the preparation of pure pharmaceutical cocrystals embedded in polymer matrix within a spherical shape granule of smooth surfaces, providing additional desirable characteristics. Intensive surface interaction, enhanced by sufficient mixing under optimal parameters, was found as a key influencing factor in cocrystallisation. Cocrystals polymorphism was actively controlled by employing the intrinsic properties of polymers and lipids.

The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.

The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in simulated evolution because evolutionary algorithms that can consistently achieve established benchmarks are lauded as successful, thus reinforcing this paradigm. A significant problem is that such objective approaches assume that intermediate stepping stones will increasingly resemble the final objective when in fact they often do not. The consequence is that while solutions may exist, searching for such objectives may not discover them. This problem with objectives is demonstrated through an experiment in this dissertation that compares how images discovered serendipitously during interactive evolution in an online system called Picbreeder cannot be rediscovered when they become the final objective of the very same algorithm that originally evolved them. This negative result demonstrates that pursuing an objective limits evolution by selecting offspring only based on the final objective. Furthermore, even when high fitness is achieved, the experimental results suggest that the resulting solutions are typically brittle, piecewise representations that only perform well by exploiting idiosyncratic features in the target. In response to this problem, the dissertation next highlights the importance of leveraging human insight during search as an alternative to articulating explicit objectives. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with a method called novelty search for the first time to facilitate the serendipitous discovery of agent behaviors. iii In this approach, the human user directs evolution by selecting what is interesting from the on-screen population of behaviors. However, unlike in typical IEC, the user can then request that the next generation be filled with novel descendants, as opposed to only the direct descendants of typical IEC. The result of such an approach, unconstrained by a priori objectives, is that it traverses key stepping stones that ultimately accumulate meaningful domain knowledge. To establishes this new evolutionary approach based on the serendipitous discovery of key stepping stones during evolution, this dissertation consists of four key contributions: (1) The first contribution establishes the deleterious effects of a priori objectives on evolution. The second (2) introduces the NA-IEC approach as an alternative to traditional objective-based approaches. The third (3) is a proof-of-concept that demonstrates how combining human insight with novelty search finds solutions significantly faster and at lower genomic complexities than fully-automated processes, including pure novelty search, suggesting an important role for human users in the search for solutions. Finally, (4) the NA-IEC approach is applied in a challenge domain wherein leveraging human intuition and domain knowledge accelerates the evolution of solutions for the nontrivial octopus-arm control task. The culmination of these contributions demonstrates the importance of incorporating human insights into simulated evolution as a means to discovering better solutions more rapidly than traditional approaches.

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