Due to the ubiquity of spiking neurons in neuronal processes, various simple spiking neuron models have been proposed as an alternative to conductance-based models (a.k.a. Hodgkin-Huxley-type models), known to be computationally expensive and difficult to treat mathematically. However, to the best of our knowledge, there is no equivalent in the literature of a simple and lightweight model for describing the voltage behavior of nonspiking neurons, which also are ubiquitous in a large variety of nervous tissues in both vertebrate and invertebrate species and play a central role in information processing. This letter proposes a simple model that reproduces the experimental qualitative behavior of known types of nonspiking neurons. The proposed model, which differs fundamentally from classic simple spiking models unable to characterize nonspiking dynamics due to their intrinsic structure, is derived from the bifurcation study of conductance-based models of nonspiking neurons. Since such neurons display a high sensitivity to noise, the model aims at capturing the experimental distribution of single-neuron responses rather than perfectly replicating a single given experimental voltage trace. We show that such a model can be used as a building block for realistic simulations of large nonspiking neuronal networks and is endowed with generalization capabilities, granted by design.

The nematode Caenorhabditis elegans (C. elegans) is a well-known model organism in neuroscience. The relative simplicity of its nervous system, made up of few hundred neurons, shares some essential features with more sophisticated nervous systems, including the human one. If we are able to fully characterize the nervous system of this organism, we will be one step closer to understanding the mechanisms underlying the behavior of living things. Following a recently conducted electrophysiological survey on different C. elegans neurons, this paper aims at modeling the three non-spiking RIM, AIY and AFD neurons (arbitrarily named with three upper case letters by convention). To date, they represent the three possible forms of non-spiking neuronal responses of the C. elegans. To achieve this objective, we propose a conductance-based neuron model adapted to the electrophysiological features of each neuron. These features are based on current biological research and a series of in-silico experiments which use differential evolution to fit the model to experimental data. From the obtained results, we formulate a series of biological hypotheses regarding currents involved in the neuron dynamics. These models reproduce experimental data with a high degree of accuracy while being biologically consistent with state-of-the-art research.

This paper investigates the emergent properties of a self-organized dynamic network for structured population-based metaheuristics. The system displays complex and emergent behavior whose most visible trait is the self-organization of the population into dynamic clusters. Furthermore, relevant variables that describe the system display 1/f noise, which is a characteristic of many complex systems. These properties were previously detected with a time-invariant population (i.e., individuals with fixed fitness vales). In this work, the investigation is extended to dynamic populations (time-varying fitness values), a scenario that models more accurately the behavior of population-based metaheuristics. Several types of fitness variation rules were tested. The experiments show that dynamic populations also display the self-organizing properties and the behavioral patterns of the stationary fitness version, as long as the intensity of the changes is kept below a certain level.

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This paper investigates a self-organized critical approach for dynamically load-balancing computational workloads. The proposed model is based on the Bak-Tang-Wiesenfeld sandpile: a cellular automaton that works in a critical regime at the edge of chaos. In analogy to grains of sand, tasks arrive and pile up on the different processing elements or sites of the system. When a pile exceeds a certain threshold, it collapses and initiates an avalanche of migrating tasks, i.e., producing load-balancing. We show that the frequency of such avalanches is in power-law relation with their sizes, a scale-invariant fingerprint of self-organized criticality that emerges without any tuning of parameters. Such an emergent pattern has organic properties such as the self-organization of tasks into resources or the self-optimization of the computing performance. The conducted experimentation also reveals that the system has a critical attractor in the point in which the arrival rate of tasks equals the processing power of the system. Taking advantage of this fact, we hypothesize that the processing elements can be turned on and off depending on the state of the workload as to maximize the utilization of resources. An interesting side effect is that the overall energy consumption of the system is minimized without compromising the quality of service.

This paper presents a study on the effects of dynamic and partially connected 2-dimensional topologies on the performance of the particle swarm optimization (PSO). The swarm is positioned on 2-dimensional grids of nodes and the particles move through the nodes according to a simple rule. Meanwhile, the von Neumann neighborhood is used to decide which particles influence each individual. Structures with growing size are tested on a classical benchmark and compared to several configurations such as lbest, gbest and the standard von Neumann configuration. The results show that the partially connected grids with von Neumann neighborhood structure performs more consistently when compared to lbest, gbest and the standard von Neumann topology.

In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture; i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.

Cloud computing is lately becoming a part of the tool-set that the scientist uses to perform compute-intensive tasks. In particular, cloud storage is an easy and convenient way of storing files that will be accessible over the Internet, but can also be used for distributing those files for performing computation on them. In this paper we describe how such a service commercialized by Dropbox is used for pool-based evolutionary algorithms. A prototype system is described and its performance measured over deceptive combinatorial optimization problems using two different substrates: WiFi and wired, finding that, for some type of problems and using commodity hardware, cloud storage systems can profitably be used as a platform for distributed evolutionary algorithms; however, performance is influenced by the type of underlying network. After introducing the method in a previous paper, in this paper we focus on measuring this influence, finding that wired is faster than WiFi for any number of nodes. We have also performed an experiment with a few more computers to see whether speedup keeps up with the number of nodes.

Cloud computing, in general, is becoming part of the toolset that the scientist uses to perform compute-intensive tasks. In particular, cloud storage is an easy and convenient way of storing files that will be accessible over the Internet, but also a way of distributing those files and performing distributed computation using them. In this paper we describe how such a service commercialized by Dropbox is used for pool-based evolutionary algorithms. A prototype system is described and its peformance measured over a deceptive combinatorial optimization problem, finding that, for some type of problems and using commodity hardware, cloud storage systems can profitably be used as a platform for distributed evolutionary algorithms. Preliminary results show that Dropbox is indeed a viable alternative for execution of pool-based distributed evolutionary algorithms, showing a good scaling behavior with up to 4 computers.

This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of the simulated annealing (Sim-Ann) method and the different parameters it requires, such as those related to: the neighbourhood; the cooling scheme; the initial temperature; the number of times the cooling scheme is applied; and the number of times we search for best individual before the temperature is cooled. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameter presenting values that are statistically most significant. Following this idea, the significance and relative importance of the parameters with respect to the obtained results, as well as suitable values for each of these, were obtained using ANOVA on four well known function optimization problems.

Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.

In this paper, we present a multiobjective ant colony optimization (MOACO) algorithm, called CHAC, designed to solve the problem of finding the path for a military unit that minimizes the cost in resources while maximizing safety. Unlike previous MOACO algorithms, CHAC uses a single colony and two different state transition rules: One that combines the heuristic and pheromone information of both objectives and another based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in different scenarios (maps with different degrees of difficulty), outperforming a greedy algorithm (taken as baseline), and yielding a good military behavior in the tactical sense. In comparison, the combined rule is slightly better than the rule based on dominance. © 2009 Wiley Periodicals, Inc.

In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimize both error types in classification: artificial neural networks and function trees ensembles created through multiobjective optimization. Since the multiobjective optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.

In this paper we present the Gossip-based Evolvable Agent Model (GossEvAg) within the context of parallel fine-grained Evolutionary Algorithms (EAs). It extends the Cellular Evolutionary Algorithm (CEA) definition with two novel features designed to work on Peer-to-Peer (P2P) networks: every individual is self-scheduled in a single thread and dynamically self-organizes its neighbourhood via newscasting, a gossip protocol. As a consequence of such multi-threading model, each Evolvable Agent (EvAg) updates asynchronously its state at random depending on the underlying platform scheduler. In order to assess the effects of asynchrony and the gossip protocol, we perform an experimental evaluation of the model for a set of discrete optimization problems. As a baseline for comparison we use two canonical genetic algorithms (GA): A steady-state GA (ssGA) and a generational GA (gGA). We also test two more topologies for the EvAg, a complete graph topology which allows panmixia and a Watts-Strogatz topology which has shown good theoretical and empirical results in related papers. We found that leaving the management of the EvAg to the underlying platform scheduler has an interesting emerging feature: the model is able to scale seamlessly in desktop computers without any effort from the practitioner. We measure how the algorithm speed scales by conducting the experiments in a Single and a Dual-Core Processor architectures.

In a connected world, spare CPU cycles are up for grabs, if you only make its obtention easy enough. In this paper we present a distributed evolutionary computation system that uses the computational capabilities of the ubiquituous web browser. Asynchronous Javascript and JSON (Javascript Object Notation, a serialization protocol) allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in a genetic algorithm experiment with little effort, or none at all. Since, in this case, computing becomes a social activity and is inherently impredictable, in this paper we will explore the performance of this kind of virtual computer by solving simple problems such as the Royal Road function and analyzing how many machines and evaluations it yields. We will also examine possible performance bottlenecks and how to solve them, and, finally, issue some advice on how to set up this kind of experiments to maximize turnout and, thus, performance. The experiments show that we we can obtain high, and to a certain point, reliable performance from volunteer computing based on AJAJ, with speedups of up to several (averaged) machines.

This paper presents a statistical parameter analysis of the ant colony optimization algorithm that was implemented to solve the bi-criteria military path-finding problem. Three parameters have been studied using analysis of variance (ANOVA) in order to identify their influence in the results and the most suitable values for them: number of ants, number of iterations and exploration/exploitation factor. In addition, a mean analysis has been performed in order to complete the conclusions obtained. The study has yielded optimal values for the parameters under study, and some internal relationships between them have been identified.

The design of computer architectures is a very complex problem. The multiple parameters make the number of possible combinations extremely high.Many researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter settings. Instead of exploring the search space, simulating many configurations, our method randomly selects some architecture configurations; those are simulated to obtain their performance, and then an artificial neural network is trained to predict the remaining configurations performance. Results obtained show a high accuracy of the estimations using a simple method to select the configurations we have to simulate to optimize the MLP. In order to explore the search space, we have designed a genetic algorithm that uses the MLP as fitness function to find the niche where the best architecture configurations (those with higher performance) are located. Our models need only a small fraction of the design space, obtaining small errors and reducing required simulation by two orders of magnitude.

This paper introduces a procedure based on genetic programming to evolve XSLT programs (usually called stylesheets or logicsheets). XSLT is a general purpose, document-oriented functional language, generally used to transform XML documents or, in general, solve any problem that can be coded as an XML document. The proposed solution uses a tree representation for the stylesheets as well as diverse specific operators in order to obtain, in the studied cases and a reasonable time, a XSLT stylesheet that performs the transformation.