Showing papers in "Natural Computing in 2010"
TL;DR: A binary version of the gravitational search algorithm, based on the law of gravity and mass interactions, is introduced and the experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
Abstract: Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
702 citations
TL;DR: This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants, and proposes a methodology based on the information given by the transition diagram associated with an SN P system which establishes the soundness and completeness of the system with respect to the problem it tries to resolve.
Abstract: This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants. A methodology based on the information given by the transition diagram associated with an SN P system is presented. The analysis of the diagram cycles codifies invariants formulae which enable us to establish the soundness and completeness of the system with respect to the problem it tries to resolve. We also study the universality of asynchronous and sequential SN P systems and the capability these models have to generate certain classes of languages. Further, by making a slight modification to the standard SN P systems, we introduce a new variant of SN P systems with a special I/O mode, called SN P modules, and study their computing power. It is demonstrated that, as string language acceptors and transducers, SN P modules can simulate several types of computing devices such as finite automata, a-finite transducers, and systolic trellis automata.
408 citations
TL;DR: The aim of this paper is to present a new memetic algorithm for GTSP with a powerful local search procedure that can solve both symmetric and asymmetric instances.
Abstract: The generalized traveling salesman problem (GTSP) is an extension of the well-known traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The recent studies on this subject consider different variations of a memetic algorithm approach to the GTSP. The aim of this paper is to present a new memetic algorithm for GTSP with a powerful local search procedure. The experiments show that the proposed algorithm clearly outperforms all of the known heuristics with respect to both solution quality and running time. While the other memetic algorithms were designed only for the symmetric GTSP, our algorithm can solve both symmetric and asymmetric instances.
135 citations
TL;DR: A comprehensive review of recent research on the representation and analysis of metabolic pathways by using Petri nets is presented in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net.
Abstract: In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net.
110 citations
TL;DR: A hybrid approach called Evolutionary Swarm Cooperative Algorithm based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is presented to deal with moving optima of optimization problems in dynamic environments.
Abstract: A hybrid approach called Evolutionary Swarm Cooperative Algorithm (ESCA) based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is presented. ESCA is designed to deal with moving optima of optimization problems in dynamic environments. ESCA uses three populations of individuals: two EA populations and one Particle Swarm Population. The EA populations evolve by the rules of an evolutionary multimodal optimization algorithm being used to maintain the diversity of the search. The particle swarm confers precision to the search process. The efficiency of ESCA is evaluated by means of numerical experiments.
76 citations
TL;DR: This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle swarm Optimization, which effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels.
Abstract: Particle Swarm Optimization is a population-based method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The improvement of the swarm is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of inertia and the attraction of the members who lead the swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels.
68 citations
TL;DR: An overview of existing approaches to encoding information on DNA strands for biocomputing, with a focus on the notion of Watson–Crick (WK) palindromes, and obtains a closed form for, as well as several properties of WK palINDromes.
Abstract: This paper provides an overview of existing approaches to encoding information on DNA strands for biocomputing, with a focus on the notion of Watson---Crick (WK) palindromes. We obtain a closed form for, as well as several properties of WK palindromes: The set of WK-palindromes is dense, context-free, but not regular, and is in general not closed under catenation and insertion. We obtain some properties that link the WK palindromes to classical notions such as that of primitive words. For example we show that the set of WK-palindromic words that cannot be written as the product of two nonempty WK-palindromes equals the set of primitive WK-palindromes. We also investigate various simultaneous Watson---Crick conjugate equations of words and show that the equations have, in most cases, only Watson---Crick palindromic solutions. Our results hold for more general functions, such as arbitrary morphic and antimorphic involutions.
53 citations
TL;DR: The proposed algorithm for the solution of the Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOVRP), combines a Honey bees Matingoptimization algorithm with the Multiple Phase Neighborhood Search–Greedy Randomized Adaptive Search Procedure (MPNS–GRASP) and the Expanding Neighborhood Search (ENS) algorithm.
Abstract: Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm with the Multiple Phase Neighborhood Search---Greedy Randomized Adaptive Search Procedure (MPNS---GRASP) and the Expanding Neighborhood Search (ENS) algorithm. Besides these two procedures, the proposed algorithm has, also, two additional main innovative features compared to other Honey Bees Mating Optimization algorithms concerning the crossover operator and the workers. Two sets of benchmark instances are used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen classic instances proposed by Christofides, the average quality is 0.029% and in the second set with the twenty large scale vehicle routing problems the average quality is 0.40%.
46 citations
TL;DR: The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pare to front is evaluated through experiments on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem.
Abstract: In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper presents an approach using two sets of nondominated solutions. The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem. The diversity of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed through a comparative study with the reported results in the literature.
38 citations
TL;DR: Results of the paper provide a basis for programming unconventional devices based on biological substrates and also shed light on behavioral patterns of the plasmodium.
Abstract: Plasmodium of Physarum polycephalum is a large cell capable of solving graph-theoretic, optimization and computational geometry problems due to its unique foraging behavior. Also the plasmodium is a unique biological substrate that mimics universal storage modification machines, namely the Kolmogorov---Uspensky machine. In the plasmodium implementation of the storage modification machine data are represented by sources of nutrients and memory structure by protoplasmic tubes connecting the sources. In laboratory experiments and simulation we demonstrate how the plasmodium-based storage modification machine can be programmed. We show execution of the following operations with the active zone (where computation occurs): merge two active zones, multiply active zone, translate active zone from one data site to another, direct active zone. Results of the paper bear two-fold value: they provide a basis for programming unconventional devices based on biological substrates and also shed light on behavioral patterns of the plasmodium.
36 citations
TL;DR: Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.
Abstract: Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.
TL;DR: This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems and is proved to have significantly better performance than four other successful variants of PSO.
Abstract: This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge to the global optimum. The PS2O algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), PS2O is proved to have significantly better performance than four other successful variants of PSO.
TL;DR: Two approaches based on metabolic and stochastic P systems, together with their associated analysis methods, for modelling biological systems are presented and their use is illustrated through two case studies.
Abstract: This paper presents two approaches based on metabolic and stochastic P systems, together with their associated analysis methods, for modelling biological systems and illustrates their use through two case studies.
TL;DR: Experimental results indicate that PSO-based clustering approaches outperform K-means, K-harmonic means, and fuzzy c-mean clustering algorithms.
Abstract: Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate that these approaches outperform K-means, K-harmonic means, and fuzzy c-means clustering algorithms.
TL;DR: In this article, an all-optical flip-flop memory with the help of Terahertz Optical Asymmetric Demultiplexer (TOAD) is proposed and described.
Abstract: The memory device is very important as they store various values either temporary or permanently. Optical flip-flop memories form a fundamental building block for all-optical packet switches in the next generation communication networks. All-optical flip-flop memory with the help of Terahertz Optical Asymmetric Demultiplexer (TOAD) is proposed and described. Principles and possibilities of all-optical circuits for TOAD based S---R, J---K, D and T flip-flop are reported. Numerical simulation confirming described method is also given in this paper.
TL;DR: It is proved that the LMO (Sigma, E) is a boolean algebra of recognizable languages with finite variation, and that it is properly contained in the recognizable languages, with the exception of the trivial case of complete commutativity.
Abstract: In this paper, we analyze a model of 1-way quantum automaton where only measurements are allowed ( MON -1qfa). The automaton works on a compatibility alphabet $$(\Sigma, E)$$ of observables and its probabilistic behavior is a formal series on the free partially commutative monoid $$\hbox{FI}(\Sigma, E)$$ with idempotent generators. We prove some properties of this class of formal series and we apply the results to analyze the class $${\bf LMO}(\Sigma, E)$$ of languages recognized by MON -1qfa's with isolated cut point. In particular, we prove that $${\bf LMO}(\Sigma, E)$$ is a boolean algebra of recognizable languages with finite variation, and that $${\bf LMO}(\Sigma, E)$$ is properly contained in the recognizable languages, with the exception of the trivial case of complete commutativity.
TL;DR: The obtained results were compared with those produced by two alternative proposals in the literature, and they indicate that these techniques tend to generate complementary results, as a consequence of the use of distinct similarity metrics.
Abstract: Query expansion is a technique utilized to improve the performance of information retrieval systems by automatically adding related terms to the initial query. These additional terms can be obtained from documents stored in a database. Usually, this task is performed by clustering the documents and then extracting representative terms from the clusters. Afterwards, a new search is performed in the whole database using the expanded set of terms. Recently, the authors have proposed an immune-inspired algorithm, namely BIC-aiNet, to perform biclustering of texts. Biclustering differs from standard clustering algorithms in the sense that the former can detect partial similarities in the attributes. The preliminary results indicated that our proposal is able to group similar texts effectively and the generated biclusters consistently presented relevant words to represent a category of texts. Motivated by this promising scenario, this paper better formalizes the proposal and investigates the usefulness of the whole methodology on larger datasets. The BIC-aiNet was applied to a set of documents aiming at identifying the set of relevant terms associated with each bicluster, giving rise to a query expansion tool. The obtained results were compared with those produced by two alternative proposals in the literature, and they indicate that these techniques tend to generate complementary results, as a consequence of the use of distinct similarity metrics.
TL;DR: It is shown here that there is no standard spiking neural P system that simulates Turing machines with less than exponential time and space overheads, and a universal spiking Neural P system is constructed with exhaustive use of rules that simulating Turing machines in linear time and has only 10 neurons.
Abstract: It is shown here that there is no standard spiking neural P system that simulates Turing machines with less than exponential time and space overheads. The spiking neural P systems considered here have a constant number of neurons that is independent of the input length. Following this, we construct a universal spiking neural P system with exhaustive use of rules that simulates Turing machines in linear time and has only 10 neurons.
TL;DR: The concept of swarm intelligence as mentioned in this paper is defined as the emergent and collective intelligence of groups of simple and autonomous agents, where each agent is a peer, acting independently, interacting with its brethren, and other features of its environment according to relatively simple rules.
Abstract: It has long been recognised that nature provides many stunning and intriguing examples of group behaviour. Different species of bird swarm in varieties of numbers and in varieties of formations; ants collective hunt food in huge numbers; schools of fish adopt close knit and ever-changing formations as they bewilder predators and bewitch prey. The activities of so-called swarms of organisms in many cases lead to beneficial effects for the organisms in question, and this has led to the concept of swarm intelligence, which encapsulates the idea that the behaviour of a swarm can exhibit useful, functional and intelligent outcomes which seem well beyond the capabilities, as far as we understand, of any individual in the swarm. A common way to define swarm intelligence is as the emergent and collective intelligence of groups of simple and autonomous agents. Implicit in such a definition is the fact that there is assumed to be no central controller—no ‘master’ agent that directs and conducts the activities of its fellows. Each agent is a peer, acting independently, interacting with its brethren, and other features of its environment, according to relatively simple rules. That such a system, with no central controller, can exhibit purposeful and robust behaviour is one of the appealing factors for scientists and engineers, suggesting strategies for building systems that are robust to failures. E.g. when systems rely on a centralised controller, damage to the controller will clearly be disastrous. Swarm intelligence is of great interest to scientists and engineers for two other main reasons. First, scientists need to understand swarms—to understand how the interactions within swarms help social animals achieve their various goals, to understand how swarm behaviour evolved, and so on. Secondly, and of most import to us here, is the fact that the study of natural swarm intelligence leads directly to novel algorithms that have a wide variety of applications. In
TL;DR: The vector-based PSO uses a novel approach to locate and maintain niches by using additional vector operations to determine niche boundaries, and is compared to two other niching techniques for particle swarm optimization.
Abstract: Several techniques have been proposed to extend the particle swarm optimization (PSO) paradigm so that multiple optima can be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based, that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often requires user-specified parameters. The proposed technique, known as the vector-based PSO, uses a novel approach to locate and maintain niches by using additional vector operations to determine niche boundaries. As the standard PSO uses weighted vector combinations to update particle positions and velocities, the niching technique builds upon existing knowledge of the particle swarm. Once niche boundaries have been calculated, the swarm can be organized into subswarms without prior knowledge of the number of niches and their corresponding niche radii. This paper presents the vector-based PSO with emphasis on its underlying principles. Results for a number of functions with different characteristics are reported and discussed. The performance of the vector-based PSO is also compared to two other niching techniques for particle swarm optimization.
TL;DR: This paper has communicated the conversion from Binary to MTN and vice-versa including the mixed MTN with details of optoelectronic circuit implementation.
Abstract: With the demand of the super fast processing and handling of huge volume of data the scientific workers in the field of computer and optics felt the importance of optical computation with multivalued logic. One of the most important number system suitable for optical computation with multivalued logic is the modified trinary number (MTN) system because of its carry and borrow-free operations. At this juncture to avail the advantages of both the Binary and MTN system the conversion from one system to another is most important. In this paper we have communicated the conversion from Binary to MTN and vice-versa including the mixed MTN with details of optoelectronic circuit implementation.
TL;DR: The relevant concepts and algorithms are introduced and the state of the art in evolutionary and immune-inspired information filtering is reviewed to promote the interplay between information filtering and biologically inspired computing and boost developments in this emerging interdisciplinary field.
Abstract: In recent years evolutionary and immune-inspired approaches have been applied to content-based and collaborative filtering. These biologically inspired approaches are well suited to problems like profile adaptation in content-based filtering and rating sparsity in collaborative filtering, due to their distributed and dynamic characteristics. In this paper we introduce the relevant concepts and algorithms and review the state of the art in evolutionary and immune-inspired information filtering. Our intention is to promote the interplay between information filtering and biologically inspired computing and boost developments in this emerging interdisciplinary field.
TL;DR: HFPN and MP equivalence is proved by means of a theorem which holds under quite general hypotheses and is compared with Metabolic P Systems for biopathways modelling.
Abstract: In this work we give a formalization of Hybrid Functional Petri Nets, shortly HFPN, an extension of Petri Nets for biopathways modelling, and we compare them with Metabolic P Systems. An introduction to both the formalisms is given, together with highlights about respective similarities and differences. Their equivalence is thus proved by means of a theorem which holds under quite general hypotheses. The case study of the lac operon gene regulatory mechanism in the glycolytic pathway of Escherichia coli is modeled by an MP system which provides the same dynamics of an equivalent HFPN model.
TL;DR: All the found automata can be candidate to an automatic search for collision-based universal cellular automata simulating Turing machines in their space-time dynamics using gliders and glider guns.
Abstract: This paper deals with the spontaneous emergence of glider guns in cellular automata. An evolutionary search for glider guns with different parameters is described and other search techniques are also presented to provide a benchmark. We demonstrate the spontaneous emergence of an important number of novel glider guns discovered by an evolutionary algorithm. An automatic process to identify guns leads to a classification of glider guns that takes into account the number of emitted gliders of a specific type. We also show it is possible to discover guns for many other types of gliders. Significantly, all the found automata can be candidate to an automatic search for collision-based universal cellular automata simulating Turing machines in their space-time dynamics using gliders and glider guns.
TL;DR: This paper exhibits a non-regular circular language generated by a circular simple system thus disproving a known result in this area and proves that the class of g-marked systems generating regular circular languages is closed under a complement operation applied to systems.
Abstract: Circular splicing has been introduced to model a specific recombinant behaviour of circular DNA, continuing the investigation initiated with linear splicing. In this paper we focus on the relationship between regular circular languages and languages generated by finite circular splicing systems. We survey the known results towards a characterization of the intersection between these two classes and provide new contributions on the open problem of finding this characterization. First, we exhibit a non-regular circular language generated by a circular simple system thus disproving a known result in this area. Then we give new results related to a restrictive class of circular splicing systems, the marked systems. Precisely, we review in a graph theoretical setting the recently obtained characterization of marked systems generating regular circular languages. In particular, we define a slight variant of marked systems, that is the g-marked systems, and we introduce the graph associated with a g-marked system. We show that a g-marked system generates a regular circular language if and only if its associated graph is a cograph. Furthermore, we prove that the class of g-marked systems generating regular circular languages is closed under a complement operation applied to systems. We also prove that marked systems with self-splicing generate only regular circular languages.
TL;DR: This work applies two immune-inspired algorithms, namely opt-ai net and omni-aiNet, to train multi-layer perceptrons (MLPs) to be used in the construction of ensembles of classifiers, to investigate the influence of the diversity of the set of solutions generated by each algorithm, and if these solutions lead to improvements in performance when combined inEnsembles.
Abstract: This work applies two immune-inspired algorithms, namely opt-aiNet and omni-aiNet, to train multi-layer perceptrons (MLPs) to be used in the construction of ensembles of classifiers. The main goal is to investigate the influence of the diversity of the set of solutions generated by each of these algorithms, and if these solutions lead to improvements in performance when combined in ensembles. omni-aiNet is a multi-objective optimization algorithm and, thus, explicitly maximizes the components' diversity at the same time it minimizes their output errors. The opt-aiNet algorithm, by contrast, was originally designed to solve single-objective optimization problems, focusing on the minimization of the output error of the classifiers. However, an implicit diversity maintenance mechanism stimulates the generation of MLPs with different weights, which may result in diverse classifiers. The performances of opt-aiNet and omni-aiNet are compared with each other and with that of a second-order gradient-based algorithm, named MSCG. The results obtained show how the different diversity maintenance mechanisms presented by each algorithm influence the gain in performance obtained with the use of ensembles.
TL;DR: The concept of the potential network is introduced as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity function, and has implications for both immunology and artificial immune systems.
Abstract: Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex network theory, particularly in the area of small-world and scale-free topologies. Computational studies which attempt to understand the structure---function relationship usually proceed by defining a representation of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the networks that can arise--furthermore, we show that although simple topologies can be produced via representation and affinity measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting scale-free functionality. In this paper, we introduce the concept of the potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free and regular topologies, finding that a key immunological property--tolerance--is promoted by bi-partite and heterogeneous topologies. The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial immune systems.
TL;DR: The present paper develops a new security solution, based on quantum cryptography, that ensures privacy of the measured data field in the presence of an intruder who listens to messages broadcast in the field.
Abstract: Security in sensor networks, though an important issue for widely available wireless networks, has been studied less extensively than other properties of these networks, such as, for example, their reliability. The few security schemes proposed so far are based on classical cryptography. In contrast, the present paper develops a new security solution, based on quantum cryptography. The scheme developed here comes with the advantages quantum cryptography has over classical cryptography, namely, effectively unbreakable keys and therefore effectively unconditionally secure messages. Our security system ensures privacy of the measured data field in the presence of an intruder who listens to messages broadcast in the field.
TL;DR: It is observed that transducer generated languages define a class of languages which is a proper subclass of recognizable picture languages, but it contains the class of all factorial local two-dimensional languages.
Abstract: We consider sets of two-dimensional arrays, called here transducer generated languages, obtained by iterative applications of transducers (finite state automata with output). Each transducer generates a set of blocks of symbols such that the bottom row of a block is an input string accepted by the transducer and, by iterative application of the transducer, each row of the block is an output of the transducer on the preceding row. We show how these arrays can be implemented through molecular assembly of triple crossover DNA molecules. Such assembly could serve as a scaffold for arranging molecular robotic arms capable of simultaneous movements. We observe that transducer generated languages define a class of languages which is a proper subclass of recognizable picture languages, but it contains the class of all factorial local two-dimensional languages. By taking the average growth rate of the number of blocks in the language as a measure of its complexity, we further observe that arrays with high complexity patterns can be generated in this way.
TL;DR: Optical architectures that use exponential space for solving instances of the (non-necessarily-binary) permanent are presented, the first work to specifically focus on such hard on average problems.
Abstract: Optical architectures that use exponential space for solving instances of the (non-necessarily-binary) permanent are presented This is the first work to specifically focus on such hard on average problems Two architectures are suggested the first is based on programmable masks, and the second on preprepared fixed number of masks