"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms

A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape analysis. Techniques already exist for measuring the "difficulty" of specific landscapes, but these are often designed solely with evolutionary algorithms in mind, and are generally specific to discrete optimisation. In this paper we develop an approach for comparing a wide range of continuous optimisation algorithms. Using a fitness landscape generation technique, we compare six different nature-inspired algorithms and identify which methods perform best on landscapes exhibiting specific features.Comment: 10 pages, 1 figure, submitted to the 11th International Conference on Adaptive and Natural Computing Algorithm

Mechanical properties of the concrete containing porcelain waste as sand

The demand of concrete have been increases on a daily bases which consume a lot of natural resource such as sand and gravel, there is an immediate need for finding suitable alternative which can be used to replace sand partially with another materials with high propor-tion . Ceramic waste is one of the strongest research areas that include the activity of replacement in all the sides of construction materi-als. This research aims to improve the performance of concrete using ceramic waste, and demonstrate the performance of mechanical properties to the concrete with partial replacement of sand by using waste porcelain. For these, we analyzed the mechanical properties of the concrete such as compressive strength, split tensile and flexural strength, the specimen were measured based on 10% ,20% ,30% ,40%, and 50% weight ratio of replace sand with waste porcelain at different time under water for 7 days , 28 days , 60 days . The optimum consideration were given to mechanical properties of the concrete, at different amount of ceramic waste as sand