Biologically inspired computing

In this paper, we will examine numerous optimization approaches in the field of computer science engineering in depth, shedding light on their applications, strengths, and weaknesses. Optimization algorithms are important tools in computer science engineering, with applications spanning from machine learning to computer vision, data mining, robotics, and more. In principle, optimization algorithms strive to locate the best possible solution among a group of possibilities while taking certain objectives and restrictions into account. They are the foundation of problem-solving approaches, providing a systematic and efficient approach to dealing with multiple difficulties. The efficiency and efficacy of each algorithm vary from one another, and each algorithm has advantages and limits that rely on the applications they are used with. We intend to provide a comprehensive view of optimization algorithms. We will cover their many types, delving into their real-world applications and painstakingly analyzing their strengths and weaknesses. In addition, we will investigate the complexities of each algorithm, giving light on the specific characteristics and settings in which they shine. This work seeks to serve as a basic resource for computer science engineering academics and practitioners, developing a deeper understanding of optimization algorithms and stimulating more inquiry in this dynamic field.

Over the past few decades, the studies on algorithms inspired by nature have shown that these methods can be efficiently used to eliminate most of the difficulties of classical methods. Nature inspired algorithms are widely used to solve optimization problems with complex nature. Various research works are carried out and algorithms are presented based on that during last few decades. Recently, some new algorithms inspired from nature are proposed to further improve the solutions obtained by the algorithms presented before. In this paper, a survey of five recently introduced Nature inspired algorithms is carried out. They include Firefly algorithm (FA), Cuckoo Search (CS), and Bat Inspired Algorithm (BA). Each of these algorithms are introduced and applied on various numerical optimization functions by various authors. We have tried to review and study the papers published by the authors and present a conclusion of this survey based on the results obtained.

The paper reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including Firefly algorithm, PSO algorithms and ABC algorithm. By implementing these algorithms in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the flashing behaviour of fireflies in nature. There are many noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Mathematical optimization or programming is the study of such planning and design problems using mathematical tools. Nowadays, computer simulations become an indispensable tool for solving such optimization problems with various efficient search algorithms. Nature-inspired algorithms are among the most powerful algorithms for optimization. Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behaviour of fireflies. A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flash phenomenon of the fireflies. In this context, three types of metaheuristics called Artificial bee Colony algorithm, Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. A series of computational experiments using each algorithm were conducted. The stimulation result of this experiment were analyzed and compared to the best solutions found so The Firefly algorithm in each noisy non linear optimization function seems to perform better and efficient.

A model of face representation, inspired by the biology of the visual system, is compared to experimental data on the perception of facial similarity. The face representation model uses aggregate primary visual cortex (V1) cell responses topographically linked to a grid covering the face, allowing comparison of shape and texture at corresponding points in two facial images. When a set of relatively similar faces was used as stimuli, this Linked Aggregate Code (LAC) predicted human performance in similarity judgment experiments. When faces of perceivable categories were used, dimensions such as apparent sex and race emerged from the LAC model without training. The dimensional structure of the LAC similarity measure for the mixed category task displayed some psychologically plausible features but also highlighted differences between the model and the human similarity judgements. The human judgements exhibited a racial perceptual bias that was not shared by the LAC model. The results s...

Bom in Ireland and brought up in Glasgow, Scotland. James Arthur came to New York in 1871. Trained in mechanics and gearcutting, he pursued a career in the manufacture and repair of machinery, during the course of which he founded a number of successful businesses and received patents on a variety of mechanical devices. His mechanical interests evolved early into a lifelong passion for horology, the science of measuring time, and he both made some remarkable clocks and assembled an important collection of old and rare timepieces. Early in this century James Arthur became associated with the American Museum of Natural History, and began to expand his interest in time to evolutionary time, and his interest in mechanisms to that most precise and delicate mechanism of them all. the human brain. The ultimate expression of his fascination with evolution and the brain was James Arthur's bequest to the American Museum permitting the establishment of the James Arthur Lectures on the Evolution of the Human Brain. The first James Arthur Lecture was delivered on March 15. 1932. two years after Mr. Arthur's death, and the series has since continued annually, without interruption.

Nature is the best tutor and its designs and strengths are extremely massive and strange that it gives inspiration to researches to imitate nature to solve hard and complex problems in computer sciences. Bio Inspired computing has come up as a new era in computation covering wide range of applications. This paper gives overview of most predominant and successful classes of bio inspired optimization methods involving evolutionary and swarm based algorithms inspired by natural evolution and collective behavior in animals respectively.

Evolutionary algorithms rarely deal with ontogenetic, non-inherited alteration of genetic information because they are based on a direct genotype-phenotype mapping. In contrast, several processes have been discovered in nature which alter genetic information encoded in DNA before it is translated into amino-acid chains. Ontogenetically altered genetic information is not inherited but extensively used in regulation and development of phenotypes, giving organisms the ability to, in a sense, re-program their genotypes according to environmental cues. An example of post-transcriptional alteration of gene-encoding sequences is the process of RNA Editing. Here we introduce a novel Agent-based model of genotype editing and a computational study of its evolutionary performance in static and dynamic environments. This model builds on our previous Genetic Algorithm with Editing, but presents a fundamentally novel architecture in which coding and non-coding genetic components are allowed to co...

The stability of synchronized states ͑frequency locked states͒ in networks of phase oscillators is investigated for several network topologies. It is shown that for some topologies there is more than one stable synchronized state according to the sign of coupling strength between oscillators. It is also shown that in some cases the synchronized state corresponds to zero order parameter.

A robotic swarm can perform various tasks. However, a human is required to task the swarm. Human control over the swarm can be enabled through a set of influential agents which can be either leaders or predators. In the presence of multiple tasks, the swarm may need to split into sub-swarms to accomplish the task and regroup as a swarm to execute larger tasks. The response of the swarm in the presence of influential agents depends on the swarm dynamics. A precise measure of influence using leaders or predators or a combination of leaders and predators to achieve the mission is not adequately studied. In this paper, we analyze the effect of using only leaders, only predators and a combination of leaders and predators on three swarm models namely, shepherding model, Couzin's model and a physicomimetic models while they perform foraging tasks and carry out Monte-Carlo simulations to evaluate the performance of the influential agents on different swarms. We also propose a novel way to split a swarm into smaller sub-swarms using influential agents. Our results show that the predator based swarm splitting and steering to a task based on shepherding model performs far better than any other combination of leaders and predators. This result is consistent even when the number of agents is increased to 500.

In this position paper, we synthesize "within the system" models of human influence over bio-inspired swarms, summarizing observations from previous experiments and identifying methods of influence that have not yet been explored. We describe (a) differences among agents that can be controlled by a human and those that can't, (b) agents that are aware of the type of other agents and those that aren't, and the effects of attraction, repulsion, and orientation on humanguided swarm behavior. We also briefly discuss the interaction effort required to manage swarms.

Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of self-directed learning, during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the pattern theory of self in order to characterize different aspects of an agent’s self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent’s self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations.

In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent’s self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent’s self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution.

Resumen Es por todos conocida la rápida evolución de las tecnologías móviles en estos últimos años. Hace tan solo una década nadie podría augurar la desorbitada cantidad de dispositivos (smartphone) que existen en la actualidad. Su rápida expansión y uso ha provocado un elevado aumento en la demanda de aplicaciones orientadas a estos dispositivos, ya sean videojuegos, aplicaciones básicas y comunes (como podría serlo una calculadora o un cronómetro) o algunas más complejas, pero no por ello menos útiles, como es el caso de una calculadora vocal. Siguiendo esta corriente de expansión y visto que el empleo de estos dispositivos móviles es tremendamente dificultoso o imposible para personas cuyo sentido de la visión es reducido o nulo, se ha decidido desarrollar una aplicación que nos permita realizar operaciones básicas, sirviéndonos para ello únicamente de la voz.

Dr. Epstein has taken on the urgent task of relating phenomenology, cognition, and neurophysiology. He has done substantial theorizing, based on some substantial empirical work, although he modestly warns that it is only a working-paper model. I think it is an important article, for several reasons. First, it is heartening to find more neuroscientists calling for clarification of phenomenology so that we can see what a physiological model of consciousness must account for. Second, Epstein gives equal ontological status to all the levels of analysis that he deals with-phenomenology, cognition, and neurophysiology. He is not a naïve reductionist. Third, and perhaps most important, Epstein is the first to make a serious attempt at neurologizing William James's idea that there are qualitatively different types of information in awareness (James, 1890, chapter 9). Fourth, Epstein explores the particularly fecund idea that mentally ''moving around'' in a complex conceptual structure literally makes use of the same neural mechanisms that are used for spatial orientation and navigation. This is a much more neurologically specific development of a general hypothesis previously proposed, but so far only developed at the linguistic, cognitive, and computer-simulation levels (contributors include Lakoff, Johnson, Feldman, Grady, Regier, Bailey, and Narayan: for brief overview and citations, see Lakoff & Johnson 1999, appendix, pp. 569-583). The general theory hypothesizes that the neural systems which subserve physical operations on physical entities also contribute to mental operations on abstract entities (concepts and categories). By this theory, for example, the same neuronal systems that control the hand to transform the position of a berry from on the bush to in the mouth would also play a role in mentally extracting a single element from a narrative and putting it into an appropriate category. They suggest it is more than a figure of speech to say that we ''grasp ideas'' or ''chew on'' them. This notion, which I call ''The Perceptual-Motor Basis of Abstract Thought,'' has many important implications for how we categorize the world (Johnson, 1987). Epstein has taken a bold step toward making it testable. Beyond these general endorsements, I will highlight a few of the issues Epstein Commentary on Russell Epstein (2000). The neural-cognitive basis of the Jamesian stream of thought.

Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.

In this paper, an integer quadratic programming model is proposed for workload balancing in the examination job assignment problem. The proposed model can be classified as part of the 'Generalised Assignment Problem' or GAP, which is known as an NP-hard problem. To solve the proposed model, a relatively new member of the swarm intelligence family known as the Bees Algorithm (BA) is used. The proposed mathematical model is also solved for nine sets of randomly generated test problems with a very well-known classical optimiser (CPLEX 10.1-MIQP) to present the competitive performance of the BA algorithm. The obtained results show that the proposed BA algorithm is able to generate better solutions with much shorter computational times.

In this paper, an integer quadratic programming model is proposed for workload balancing in the examination job assignment problem. The proposed model can be classified as part of the 'Generalised Assignment Problem' or GAP, which is known as an NP-hard problem. To solve the proposed model, a relatively new member of the swarm intelligence family known as the Bees Algorithm (BA) is used. The proposed mathematical model is also solved for nine sets of randomly generated test problems with a very well-known classical optimiser (CPLEX 10.1-MIQP) to present the competitive performance of the BA algorithm. The obtained results show that the proposed BA algorithm is able to generate better solutions with much shorter computational times.

Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.

El estudio enfatiza la importancia de comprender los sistemas vivos, tanto del carbono como in silico, como organismos complejos que se autoorganizan y colaboran desde estructuras descentralizadas para mantener un orden del no-equilibrio. Se presentan ejemplos que ilustran las capacidades de las especies colectivas y se explora la proposición de la creación de nuevas organizaciones basadas en economías del mutualismo y del cuidado para generar diversidad y soluciones a problemas. Asimismo, se aboga por abandonar narrativas reduccionistas y jerárquicas en favor de explorar la autoorganización social como una característica en los sistemas vivos, fomentando la creatividad y el conocimiento computacional para imaginar nuevos mundos posibles y narrativas de vida alternativas.

Se subraya la autoorganización como un mecanismo fundamental para promover la convivencia y la coevolución con el entorno, facilitando la distribución equitativa de recursos necesarios para vivir y aumentar los grados de libertad. Se destaca la importancia de superar las imposiciones y explotaciones mediante organizaciones emergentes que fomentan la autonomía, alejándose de pensamientos colonialistas y antropocéntricos. Esta visión propone la construcción de múltiples futuros más diversos y sostenibles, en los que puedan surgir cambios e innovaciones esenciales en las sociedades para buscar posibilitar aún más la vida.

The study highlights the importance of understanding living systems, both carbon and in silico, as complex organisms that self-organize and collaborate from decentralized structures to maintain a non-equilibrium order. Examples are presented that illustrate the capabilities of collective species and explore the proposition of creating new organizations based on economies of mutualism and care to generate diversity and solutions to problems. It also argues for abandoning reductionist and hierarchical narratives in favor of exploring social self-organization as a feature in living systems, fostering creativity and computational knowledge to imagine new possible worlds and alternative life narratives.

Self-organization is emphasized as a fundamental mechanism to promote coexistence and coevolution with the environment, facilitating the equitable distribution of resources necessary for living and increasing degrees of freedom. It draws attention to the importance of overcoming impositions and exploitations through emerging organizations that promote autonomy, moving away from colonialist and anthropocentric thoughts. This vision proposes the construction of multiple, more diverse and sustainable futures, in which essential changes and innovations can emerge in societies to seek to make life even more possible.

Neuromorphic image sensors produce activity-driven spiking output at every pixel. These low-power consuming imagers which encode visual change information in the form of spikes help reduce computational overhead and realize complex real-time systems; object recognition and pose-estimation to name a few. However, there exists a lack of algorithms in event-based vision aimed towards capturing invariance to transformations. In this work, we propose a methodology for recognizing objects invariant to their pose with the Dynamic Vision Sensor (DVS). A novel slow-ELM architecture is proposed which combines the effectiveness of Extreme Learning Machines and Slow Feature Analysis. The system can perform 10, 000 classifications per second, and achieves 1% classification error for 8 objects with views accumulated over 90 degrees of 2D pose.

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. In this paper a hybrid fuzzy clustering method based on FCM and fuzzy PSO (FPSO) is proposed which make use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results.

This paper presents an improved algorithm for path planning using Artificial Bee Colony Algorithm. This algorithm is used to find collision free shortest path from the start position to destination. The environment considered here is a two dimensional space consisting of both static and dynamic obstacles. The ABC algorithm used is inspired by the collective behavior of bees to find better food sources around the hive .The path generated by the original algorithm may be shorter but may not be optimized. So, the final path is optimized using triangle inequality method.

Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a large extent unsolved. In this perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial intelligence systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence.

This article discusses the possibilities of using FPGAs in order to construct fast PLCs that execute serialcyclic program control loop. The PLCs bistable function blocks of the IEC 61131-3 standard with particular emphasis on the possible FPGA implementations are presented. The FPGA hardware support is implemented in such a way that it does not interfere with the normal, serial-cyclic program execution. It enables a significant reduction in the timing of the result execution after loading new input data in the memory cells. Moreover, such an implementation of bistable function blocks allows for seamless use in any programming language. Both the IL text language and the graphical languages (LD and FBD) can freely use the advantages of such a solution.

The lack of transparency of neural networks stays a major break for their use. The Layerwise Relevance Propagation technique builds heat-maps representing the relevance of each input in the model s decision. The relevance spreads backward from the last to the first layer of the Deep Neural Network. Layer-wise Relevance Propagation does not manage normalization layers, in this work we suggest a method to include normalization layers. Specifically, we build an equivalent network fusing normalization layers and convolutional or fully connected layers. Heatmaps obtained with our method on MNIST and CIFAR 10 datasets are more accurate for convolutional layers. Our study also prevents from using Layerwise Relevance Propagation with networks including a combination of connected layers and normalization layer.

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al.

There are many successful applications of Backpropagation (BP) for training multilayer neural networks. However, they have many shortcomings. Learning often takes insupportable time to converge, and it may fall into local minima at all. One of the possible remedies to escape from local minima is using a very small learning rate, but this will slow the learning process. The proposed algorithm is presented for the training of multilayer neural networks with very small learning rate, especially when using large training set size. It can apply in a generic manner for any network size that uses a backpropgation algorithm through optical time. This paper studies the performance of the Optical Backpropagation algorithm OBP (Otair & Salameh, 2004a, 2004b. 2005) on training a neural network for online handwritten character recognition in comparison with backpropagation BP.

One of the recently proposed algorithms in the field of nature-inspired algorithm is the Honey Bees Mating Optimization (HBMO) algorithm by Haddad and Afshar. The HBMO algorithm simulates the marriage behaviour of bees. The solutions are incrementally constructed by the HBMO algorithm. Therefore, the HBMO algorithm is a swarm-based constructive optimization algorithm. However, no study has reported the modification of HBMO for feature selection. Therefore, we try to develop a discrete honey bees mating optimization (DHBMO) as the modification of HBMO for solving discrete optimization problem especially feature selection to improve the performance of machine vision for predicting water stress in plants. We test the algorithm to solve feature selection problem using single and multi-objectives optimization to optimize water usage in plants for environmental sustainability. The results showed DHBMO has better performance compared to the existed nature-inspired optimization algorithms such as genetic algorithms (GA), simulated annealing (SA) and discrete-particle swarm optimization (discrete-PSO).

Darwin's theory of evolution explains the early history that led to the origin of man. The theory of the hypercell organisms or "Homo Proteus", as I call the Man-Machine-Hybrid, continues where Darwin's theory left off and deals with the course of evolution beyond humans themselves. Just as certain unicellular organisms ushered in the prodigious development of plants and animals nearly 1.8 billion years ago, a no less astounding development of new life forms, i.e. that of the hypercell organisms, was ushered in by early man. Their additional organs are all formed by man, just like all organs of multicellular animals and plants continue to derive from a single cell (the germ cell). This book is my attempt to demonstrate the underlying continuity in the transition to hypercell organisms and that, despite the altered external appearance, the same fundamental principles remain valid for them as well. This perspective allows interesting conclusions to be drawn about our image of humankind. - Hans Hass

In this paper, we develop computing machinery within the framework of the String Multiset Rewriting calculus (SMSR), as dened by Barbuti et al. [4], to solve the SAT problem in linear time regarding the number of variables of a given conjunctive normal form. This shows that SMSR can be considered a computational model capable of signicantly reducing the time requirement of classical decision problems.

Recent advances in robotics technology provide great support for robotic leg prostheses to realize full biomechanical functionalities of the contralateral leg. In order to reproduce the biomechanical behaviors of the contralateral leg, this paper addresses biologically inspired deadbeat control of robotic leg prostheses under different terrain conditions including level ground, stairs ascent and descent. The proposed control method is based on the ground reactive force (GRF) of the contralateral leg during walking. The trajectories of center-of-mass (CoM) are encoded by the corresponding polynomial splines. Then, the control of the robotic leg prosthesis is designed by replicating the motion of the user's contralateral leg. Compared to most existing results, our approach does not require any pre-knowledge of the exact physical parameters. Finally, experiments are conducted to show that the prosthesis can help the user walk smoothly under various terrain conditions.

Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a large extent unsolved. In this perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial intelligence systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence.

We present a socially inspired approach that allows agents in Multi-Agent Systems to speed up their own learning process through communication. Thereby, they are able to trade off impact of knowledge by mutation dependent on the recent performance of the interacting agents. This is inspired by social interaction of humans, where the opinions of experts have greater impact on the overall opinion and are incorporated more exactly than those of newbies. The approach is successfully evaluated in a simulation in which mobile robots have to accomplish a task while taking care of timely recharging their resources.

Imitation is not only a powerful means to drastically downsize the exploration space when learning behavior. It also helps to align the learning efforts of a robot group towards a common goal. However, one prerequisite in imitation, the decision of which robot to imitate, is often factored out in current research. In our work we address this question by providing a means to measure the similarity between two robots. Based on this similarity a robot can choose which robot to imitate. The affinity of two robots with respect to imitation is most reasonably measured by calculating their behavioral difference, since the goal of imitation is learning new behavior. This is accomplished by each robot individually constructing an Affordance Network which is a Bayesian network upon its conditional affordance probabilities in the environment. An affordance represents the interaction possibilities an object provides to the robot. These Affordance Networks are then compared with a new metric.

Information extraction is systematic process of extracting structured information from documents which has both unstructured and semi structured data set. Data available over the web is unstructured which is processed and delivered that may be challenging due to massive data over web. Bigdata analytics approach is used in the computation field where massive data is managed and processed as information. Data from various sources like industries, institutes are processed using algorithms in efficient means employing web of things or Internet of things used to mine such a large data. Bio inspired algorithms have evolved from application of heuristic approaches to meta-heuristic and hyper-heuristic methodologies. Bio inspired techniques are categorized into human inspired algorithms, Swarm Intelligence algorithms, evolutionary algorithms and ecology based algorithms. Genetic algorithms are purely heuristic in nature and are employed for computation and extracting information and from bi...

Bom in Ireland and brought up in Glasgow, Scotland. James Arthur came to New York in 1871. Trained in mechanics and gearcutting, he pursued a career in the manufacture and repair of machinery, during the course of which he founded a number of successful businesses and received patents on a variety of mechanical devices. His mechanical interests evolved early into a lifelong passion for horology, the science of measuring time, and he both made some remarkable clocks and assembled an important collection of old and rare timepieces. Early in this century James Arthur became associated with the American Museum of Natural History, and began to expand his interest in time to evolutionary time, and his interest in mechanisms to that most precise and delicate mechanism of them all. the human brain. The ultimate expression of his fascination with evolution and the brain was James Arthur's bequest to the American Museum permitting the establishment of the James Arthur Lectures on the Evolution of the Human Brain. The first James Arthur Lecture was delivered on March 15. 1932. two years after Mr. Arthur's death, and the series has since continued annually, without interruption.

In this paper, an investigation of snake robot movement scenario has been discussed and analyzed. Angular velocity, snake robot movement trends and motion shape are involved in this investigation by implementation of serpentine locomotion. Experiment the real snake to extract motion and force data is quite difficult to handle. Thus a new model of the test bed for robot handling and gathering data are proposed in this research. Grid like elastic strings with a particular tension are installed on the transparent table to extract the snake motion direction data to enable the snake force calculation. Then shape measurement belt and kinect sensor will establish the motion data. A snake robot is developed in this paper to perform the preparatory experimental work to go for the real snake experiment involving snake locomotion in future.

It presents an original model of human creativity, as well as a series of personal considerations about creativity within Artificial Intelligence (AI). It is believed that when human creativity it will be known, it can be implemented in AI, such as a number of major challenges such as pattern recognition, process simulation, optimal control, and so on.

Routing algorithm is the key element in networks performance and reliability, therefore, there is a need for an algorithm to manage traffic flows and deliver packets from the source to the destination in a realistic time. A modification algorithm has been presented called Subset Nodes Based AntNet. In the Subset Nodes Based AntNet, a routing tables maintained at each node is used to get a subset table which helps in reducing number of choices available to each Ant to select the next node and reach the destination. The parameters used to measure the performance of the proposed routing algorithms are: cost, number of iterations and number of Ants. The experiment results show that the proposed Subset Nodes Based AntNet is best in minimizing the cost, number of iterations and number of Ants number in comparison to the original AntNet.

In the field of Evolutionary Computation (EC), many algorithms have been proposed to enhance the optimisation search performance in NP-Hard problems. Recently, EC research trends have focused on memetic algorithms that combine local and global optimisation search. One of the state-of-the-art memetic EC methods named Bacterial Memetic Algorithm (BMA) has given good optimisation results. In this paper, the objective is to improve the existing BMA optimisation performance without significant impact to its processing cost. Therefore, we propose a novel algorithm called Average Edit Distance Bacterial Mutation (AEDBM) algorithm that improves the bacterial mutation operator in BMA. The AEDBM algorithm performs edit distance similarity comparisons for each selected mutation elements with other bacterial clones before assigning the selected elements to the clones. In this way, AEDBM will minimise bad (similar elements) bacterial mutation to other bacterial clones and thus improve the overall optimisation performance. We investigate the proposed AEDBM algorithm on commonly used datasets in fuzzy logic system analysis. We also apply the proposed method to train a robotic learning agent's perception-action mapping dataset. Experimental results show that the proposed AEDBM approach in most cases gains consistent mean square error optimisation performance improvements over the benchmark approach with only minimal impact to processing cost.

Advances in computer technology, coupled with the intention to utilize the limited resources to its best possible way while conforming to the prescribed objective, has led to a wealth of different optimization approaches in engineering problems. Of particular note is the rise of using evolutionary algorithms in obtaining the optimal solutions in the engineering design problems. This paper sets out to compare the optimization performances of three recently developed evolutionary algorithms, namely, bat algorithm, cuckoo search algorithm, and flower pollination algorithm in constrained engineering optimization. Three infamous constrained real world problems, specifically, spring design, welded beam design, and pressure vessel design, are considered in this study. The promising optimization capabilities of all reviewed evolutionary algorithms are shown in the performance assessment.

h i g h l i g h t s • Surveys algorithms applicable to swarm robotic systems for target search and tracking. • Identifies variations of the search and tracking problem addressed in the literature. • Discusses desired capabilities of search and tracking algorithms for robot swarms.

The ability to detect floor regions from an image enables a variety of applications such as indoor scene understanding, mobility assessment, robot navigation, path planning and surveillance. In this work, we propose a framework for estimating floor regions in cluttered indoor environments. The problem of floor detection and segmentation is challenging in situations where floor and non-floor regions have similar appearances. It is even harder to segment floor regions when clutter, specular reflections, shadows and textured floors are present within the scene. Our framework utilizes a generic classifier trained from appearance cues as well as floor density estimates, both trained from a variety of indoor images. The results of the classifier is then adapted to a specific test image where we integrate appearance, position and geometric cues in an iterative framework. A Markov Random Field framework is used to integrate the cues to segment floor regions. In contrast to previous settings that relied on optical flow, depth sensors or multiple images in a calibrated setup, our method can work on a single image. It is also more flexible as we avoid assumptions like Manhattan world scene or restricting clutter only to wall-floor boundaries. Experimental results on the public MIT Scene dataset as well as a more challenging dataset that we acquired, demonstrate the robustness and efficiency of our framework on the above mentioned complex situations.

One of the main constraints in wireless sensor networks limited battery power which plays a great influence on lifetime and the quality of wireless sensor networks. Clustering is an effective technique that can greatly expand lifetime, energy efficiency and quality of service in wireless sensor networks (WSNs) through data aggregation at the cluster head. In this paper, We suggest a clustering algorithm Based on HBMO (Honey Bee Mating optimization) to optimize clustering process by considering the energy and communication costs and other factors. The performance evaluation of our proposed technique is carried out with respect to GA algorithm. The simulation results show the proposed algorithm can decrease the energy consumption of the networks and work over its comparatives in terms of networks lifetime and average energy consumption.

This paper addresses the problem of steering a swarm of autonomous agents out of an unknown maze to some goal located at an unknown location. This is particularly the case in situations where no direct communication between the agents is possible and all information exchange between agents has to occur indirectly through information "deposited" in the environment. To address this task, an -greedy, collaborative reinforcement learning method using only local information exchanges is introduced in this paper to balance exploitation and exploration in the unknown maze and to optimize the ability of the swarm to exit from the maze. The learning and routing algorithm given here provides a mechanism for storing data needed to represent the collaborative utility function based on the experiences of previous agents visiting a node that results in routing decisions that improve with time. Two theorems show the theoretical soundness of the proposed learning method and illustrate the importance of the stored information in improving decision-making for routing. Simulation examples show that the introduced simple rules of learning from past experience significantly improve performance over random search and search based on Ant Colony Optimization, a metaheuristic algorithm.

In this paper we propose a Lossless data compressor in high level throughput using re programable FPGA technology.Real time data compression is expected to play a crucial role in high rate data communication applications. Most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we proposed an FPGA-based data compressor on the concept of CAM and Dictionary based compression technique has been proposed in this paper. It has been implemented on a Xilinx Spartan3-II FPGA formed by several millions of gates, and with high computational power and compact size, which make this reconfigurable device very appealing for onboard, real-time data processing.

In this paper we propose a Lossless data compressor in high level throughput using re programable FPGA technology.Real time data compression is expected to play a crucial role in high rate data communication applications. Most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we proposed an FPGA-based data compressor on the concept of CAM and Dictionary based compression technique has been proposed in this paper. It has been implemented on a Xilinx Spartan3-II FPGA formed by several millions of gates, and with high computational power and compact size, which make this reconfigurable device very appealing for onboard, real-time data processing.

Propulsion of swimming robots at the surface and underwater is largely dominated by rotary propellers due to high thrust, but at the cost of low efficiency. Due to their inherently high speed turning motion, sharp propeller blades and generated noise, they also present a disturbance to maritime ecosystems. Our work presents a bio-inspired approach to efficient and ecofriendly swimming with moderate to high thrust. This paper describes the concept, development and experimental validation of the novel anguilliform robot MAR. With 15 elements making up the 0.5 m long propulsive section and driven by a single, speed-controlled brushless DC motor (BLDC), the robot creates a smooth continuous traveling wave for propulsion. Steering and autonomy are realized by an actuated head with integrated batteries that serves as a front-rudder. Almost neutral buoyancy paired with individually actuated pectoral fins furthermore enable submerged swimming and diving maneuvers. MAR accomplished high thrusts at a moderate power consumption in first performance tests. The achieved maximum velocity and the speed related efficiency (defined as the achieved speed over the power consumption m/Ws) did not fulfill the expectations in the first tests (in comparison to commercial rotary thrusters), which can be largely attributed to the spatial limitations and an imperfect test setup. Nevertheless, the potential towards highly efficient and high thrust propulsion is visible and will be further investigated in future efforts.

In this article, a new hybrid algorithm based on Honey Bees Mating Optimization (HBMO) combined with the Tabu Search (TS) for null steering beamformer in adaptive antenna array is presented. The proposed method HBMO/TS is applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. Moreover, the proposed algorithm is tested and compared with two other well-known approaches that are the Least Mean Squares (LMS) and Genetic Algorithm (GA). The abovementioned methods have been performed considering uniform linear antenna array and achieved by controlling only the phase of each array element. Results obtained prove the effectiveness of our proposed approach HBMO/TS.