Trans-Canada Slimeways

Slime mould Physarum polycephalum builds up sophisticated networks to transport nutrients between distant parts of its extended body. The slime mould’s protoplasmic network is optimised for maximum coverage of nutrients yet minimum energy spent on transportation of the intra-cellular material. In laboratory experiments with P. polycephalum we represent Canadian major urban areas with rolled oats and inoculated slime mould in the Toronto area. The plasmodium spans the urban areas with its network of protoplasmic tubes. The authors uncover similarities and differences between the protoplasmic network and the Canadian national highway network, analyse the networks in terms of proximity graphs and evaluate slime mould’s network response to contamination.

The Grand Challenges in Natural Computing Research

An important premise of Natural Computing is that some form of computation goes on in Nature, and that computing capability has to be understood, modeled, abstracted, and used for different objectives and in different contexts. Therefore, it is necessary to propose a new language capable of describing and allowing the comprehension of natural systems as a union of computing phenomena, bringing an information processing perspective to Nature. To develop this new language and convert Natural Computing into a new science it is imperative to overcome three specific Grand Challenges in Natural Computing Research: Transforming Natural Computing into a Transdisciplinary Discipline, Unveiling and Harnessing Information Processing in Natural Systems, Engineering Natural Computing Systems.

Asynchronous P Systems

In this paper, the authors propose a new approach to fully asynchronous P systems, and a matching complexity measure, both inspired from the field of distributed algorithms. The authors validate the proposed approach by implementing several well-known distributed depth-first search (DFS) and breadth-first search (BFS) algorithms. Empirical results show that the proposed P algorithms have shorter descriptions and achieve a performance comparable to the corresponding distributed algorithms.

Phylogenetic Differential Evolution

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

Forward and Backward Chaining with P Systems

One of the concepts that lie at the basis of membrane computing is the multiset rewriting rule. On the other hand, the paradigm of rules is profusely used in computer science for representing and dealing with knowledge. Therefore, establishing a “bridge” between these domains is important, for instance, by designing P systems reproducing the modus ponens-based forward and backward chaining that can be used as tools for reasoning in propositional logic. In this paper, the authors show how powerful and intuitive the formalism of membrane computing is and how it can be used to represent concepts and notions from unrelated areas.

Local Search with P Systems

Local search is currently one of the most used methods for finding solutions in real-life problems. It is usually considered when the research is interested in the final solution of the problem instead of the how the solution is reached. In this paper, the authors present an implementation of local search with Membrane Computing techniques applied to the N-queens problem as a case study. A CLIPS program inspired in the Membrane Computing design has been implemented and several experiments have been performed. The obtained results show better average times than those obtained with other Membrane Computing implementations that solve the N-queens problem.

MP Modelling of Glucose-Insulin Interactions in the Intravenous Glucose Tolerance Test

The Intravenous Glucose Tolerance Test is an experimental procedure used to study the glucose-insulin endocrine regulatory system. An open problem is to construct a model representing simultaneously the entire regulative mechanism. In the past three decades, several models have appeared, but they have not escaped criticisms and drawbacks. In this paper, the authors apply the Metabolic P systems theory for developing new physiologically based models of the glucose-insulin system, which can be applied to the IVGTT. Ten data-sets obtained from literature were considered and an MP model was found for each, which fits the data and explains the regulations of the dynamics. Finally, each model is analysed to define a common pattern which explains, in general, the action of the glucose-insulin control system.

Incorporation of Preferences in an Evolutionary Algorithm Using an Outranking Relation

The incorporation of preferences into Evolutionary Algorithms (EA) presents some relevant advantages, namely to deal with complex real-world problems. It enables focus on the search thus avoiding the computation of irrelevant solutions from the point of view of the practical exploitation of results (thus minimizing the computational effort), and it facilitates the integration of the DM’s expertise into the solution search process (thus minimizing the cognitive effort). These issues are particularly important whenever the number of conflicting objective functions and/or the number of non-dominated solutions in the population is large. In EvABOR (Evolutionary Algorithm Based on an Outranking Relation) approaches preferences are elicited from a decision maker (DM) with the aim of guiding the evolutionary process to the regions of the space more in accordance with the DM’s preferences. The preferences are captured and made operational by using the technical parameters of the ELECTRE TRI method. This approach is presented and analyzed using some illustrative results of a case study of electrical networks.

PSO-CGO

In this paper the authors present PSO-CGO, a novel particle swarm algorithm for cluster geometry optimization. The proposed approach combines a steady-state strategy to update solutions with a structural distance measure that helps to maintain population diversity. Also, it adopts a novel rule to update particles, which applies velocity only to a subset of the variables and is therefore able to promote limited modifications in the structure of atomic clusters. Results are promising, as PSO-CGO is able to discover all putative global optima for short-ranged Morse clusters between 30 and 50 atoms. A comprehensive analysis is presented and reveals that the proposed components are essential to enhance the search effectiveness of the PSO.

Elementary Active Membranes Have the Power of Counting

P systems with active membranes have the ability of solving computationally hard problems. In this paper, the authors prove that uniform families of P systems with active membranes operating in polynomial time can solve the whole class of PP decision problems, without using nonelementary membrane division or dissolution rules. This result also holds for families having a stricter uniformity condition than the usual one.