Computation in biology and in conventional computer architectures seem to share some features, yet many of their important characteristics are very different. To address this,  introduced systemic computation, a model of interacting systems with natural characteristics. Following this work, here we introduce the first platform implementing such computation, including programming language, compiler and virtual machine. To investigate their use we then provide an implementation of a genetic algorithm applied to the travelling salesman problem and also explore how SC enables self-adaptation with the minimum of additional code.https://dl.acm.org/ft_gateway.cfm?id=1274037&dwn=1
We present what we believe is the first attempt to physically reconstruct the exploratory mechanism of genetic regulatory networks. Feedback plays a crucial role during developmental processes and its mechanisms have recently become much clearer due to evidence from evolutionary developmental biology. We believe that without similar mechanisms of interaction and feedback, digital genomes cannot guide themselves across functional search spaces in a way that fully exploits a domain's resources, particularly in the complex search domains of real-world physics. Our architecture is designed to let evolution utilise feedback as part of its mechanism of exploration.https://dl.acm.org/ft_gateway.cfm?id=1277154&dwn=1
This paper presents a kinetic data structure (KDS) for solutions to the all nearest neighbors problem and the closest pair problem in the plane. For a set P of n moving points where the trajectory of each point is an algebraic function of constant maximum degree s, our kinetic algorithm uses O(n) space and O(n log n) preprocessing time, and processes O(n2β22s+2(n)log n) events with total processing time O(n2β22s+2(n)log2 n), where βs(n) is an extremely slow-growing function. In terms of the KDS performance criteria, our KDS is efficient, responsive (in an amortized sense), and compact.
Our deterministic kinetic algorithm for the all nearest neighbors problem improves by an O(log2 n) factor the previous randomized kinetic algorithm by Agarwal, Kaplan, and Sharir. The improvement is obtained by using a new sparse graph representation, the Pie Delaunay graph, to reduce the problem to one-dimensional range searching, as opposed to using two-dimensional range searching as in the previous work.https://dl.acm.org/ft_gateway.cfm?id=2462378&dwn=1
By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.https://dl.acm.org/ft_gateway.cfm?id=1273497&dwn=1
Several practical problems in industry are difficult to optimize, both in terms of scalability and representation. Heuristics designed by domain experts are frequently applied to such problems. However, designing optimized heuristics can be a non-trivial task. One such difficult problem is the Facility Layout Problem (FLP) which is concerned with the allocation of activities to space. This paper is concerned with the block layout problem, where the activities require a fixed size and shape (modules). This problem is commonly divided into two sub problems; one of creating an initial feasible layout and one of improving the layout by interchanging the location of activities. We investigate how to extract novel heuristics for the FLP by applying an approach called Cooperative Coevolutionary Gene Expression Programming (CCGEP). By taking advantage of the natural problem decomposition, one species evolves heuristics for pre-scheduling, and another for allocating the activities onto the plant. An experimental, comparative approach investigates various features of the CCGEP approach. The results show that the evolved heuristics converge to suboptimal solutions as the problem size grows. However, coevolution has a positive effect on optimization of single problem instances. Expensive fitness evaluations may be limited by evolving generalized heuristics applicable to unseen fitness cases of arbitrary sizes.https://dl.acm.org/ft_gateway.cfm?id=1569997&dwn=1