This study evolves and categorises a population of conceptual designs by their ability to handle physical constraints. The design process involves a trade-off between form and function. The aesthetic considerations of the designer are constrained by physical considerations and material cost. In previous work, we developed a design grammar capable of evolving aesthetically pleasing designs throu...

Evolutionary algorithms have been successfully used to create controllers for many animats. However, intuitive fitness functions like the survival time of the animat, often do not lead to interesting results because of the bootstrap problem, arguably one of the main challenges in evolutionary robotics: if all the individuals perform equally poorly, the evolutionary process cannot start. To over...

The notion of multi-authority attribute based encryption was introduced by Chase in TCC 2007. In this paper, we improve Chase’s scheme to allow encryptors to determine how many attributes are required for each ciphertext from related attribute authorities. The proposed scheme can be seen as a multi-trapdoor construction. Furthermore, we apply the LMSSS to outsource the decryption of multi-autho...

In this paper we investigate the applicability of Multi-Objective Optimization (MOO) in Evolutionary Art. We evolve images using an unsupervised evolutionary algorithm and we use two aesthetic measures as fitness functions concurrently. We use three different pairs from a set of three aesthetic measures and we compare the output of each pair to the output of other pairs, and to the output of ex...

Remarkable advantages of asynchronous circuits in comparison with their synchronous counterparts results in vast effort in designing such circuits. This paper proposes optimized asynchronous circuit design approach by exploiting potent evolutionary circuit design method. The evolutionary algorithm applies fast and accurate hazard detection technique as a fitness function. Outcomes of proposed m...

An objective of transfer learning is to improve and speedup learning on target tasks after training on a different, but related source tasks. This research is a study of comparative Neuro-Evolution (NE) methods for transferring evolved multi-agent policies (behaviors) between multi-agent tasks of varying complexity. The efficacy of five variants of two NE methods are compared for multi-agent po...