Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Interactions etween ants, fruits and seeds in the cerrado : the role of ants in the biology of seeds and seedlings

Orientador: Paulo Sergio Moreira Carvalho de OliveiraTese (doutorado) - Universidade Estadual de Campinas, Instituto de BiologiaResumo: Nosso conhecimento a respeito dos sistemas de dispersão de sementes tem aumentado consideravelmente nos últimos anos. Um número crescente de estudos tem mostrado que a regeneração de plantas é freqüentemente muito mais complexa do que pensávamos, incluindo vários agentes ao longo de etapas subseqüentes do processo de dispersão de sementes. Por exemplo, formigas podem rearranjar a sombra de sementes que cai ao solo, o que pode influenciar as probabilidades de transição do estágio de semente para plântula durante o recrutamento. Neste trabalho reportamos informações mostrando que formigas são importantes agentes de dispersão secundária de sementes na maior savana da América do Sul, o cerrado. Formigas interagiram com diásporos caídos de muitas plantas dispersas primariamente por vertebrados frugívoros. Formigas freqüentemente limparam as sementes da polpa dos frutos, o que aumentou sua germinação. As sementes de Erythroxylum pelleterianum (Erythroxylaceae), Xylopia aromatica (Annonaceae) e Miconia rubiginosa (Melastomataceae) são dispersas primariamente por aves, mas a maior parte de seus frutos cai ao solo sob a planta-mãe. Formigas removeram grande parte destes diásporos caídos, e promoveram dispersão direcionada a microsítios ricos em nutrientes onde houve maior sobrevivência de plântulas, como demonstrado para E. pelleterianum. Contudo, este benefício por vezes foi alcançado à custa de perdas significativas de sementes para formigas granívoras, como em Xylopia aromatica. Aves são responsáveis pela dispersão de sementes a longas distâncias e colonização de novos sítios, enquanto formigas rearranjam a sombra de sementes numa escala menor, depositando-as em sítios onde a sobrevivência das plântulas é aumentada. Embora a maioria das formigas foi generalista em relação às características dos diásporos, formigas cortadeiras mostraram algumas preferências, especialmente por diásporos ricos em carboidratos. Nós sugerimos que estas preferências podem ser devidas às defesas químicas da folhagem de plantas do cerrado, que forçariam as formigas a depender de frutos carnosos para o cultivo de fungo no interior dos ninhos. Finalmente, nós mostramos que as interações formiga-diásporo são suscetíveis a efeitos de borda, que diminuem os benefícios obtidos por plantas dispersas secundariamente por formigas. Esta informação é especialmente relevante, uma vez que o cerrado está sendo convertido para agricultura a taxas alarmantes e efeitos de borda não haviam sido reconhecidos para o cerrado até o momentoAbstract: Our knowledge about seed dispersal systems has been improved considerably in the last few years. An increasing number of studies has shown that the process of plant regeneration is often much more complex than we realize, including several different agents across subsequent steps of seed dispersal. For instance, ants may reshape the seed shadow after seeds fall to the ground, and this may influence the transition probabilities from seed to the seedling stage in plant recruitment. Here we report data showing that ants are important agents of secondary seed dispersal in the largest South American savanna, the cerrado. Ants interacted with fallen diaspores of many plants primarily dispersed by vertebrate frugivores. Ants often cleaned the seeds from fruit matter, what increased seed germination. The seeds of Erythroxylum pelleterianum (Erythroxylaceae), Xylopia aromatica (Annonaceae) and Miconia rubiginosa (Melastomataceae) are primarily dispersed by birds, but most fruits fall to the ground under the parent tree. Ants removed a considerable number of fallen diaspores of these plants, and provided directed dispersal to nutrient-enriched microsites where seedling survival was increased, as shown for E. pelleterianum. However, this benefit sometimes is attained at the cost of significant seed loss to granivorous ants, as in the case of Xylopia aromatica. Birds are likely responsible for long-distance dispersal and colonization of new patches, while ants reshape the seed shadow at a finer scale, delivering seeds to specific sites where seedling survival is more likely. Although most ant taxa were generalist in relation to diaspore traits, leaf-cutter ants showed a preference pattern for some diaspores, particularly carbohydrate-rich ones. We suggest that such preference may be driven by the chemically-protected plant leaves of the cerrado, which would constrain leaf-cutter ants to rely on fleshy fruits for fungus culturing inside their nests. Finally, we showed that ant-diaspore interactions are susceptible to edge effects, which decrease benefits obtained by plants secondarily dispersed by ants. This information is particularly relevant, since the cerrado is currently being converted to cropland at an alarming rate, and so far edge effects had not been recognized in the cerradoDoutoradoEcologiaDoutor em Ecologi

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Reports on industrial information technology. Vol. 12

The 12th volume of Reports on Industrial Information Technology presents some selected results of research achieved at the Institute of Industrial Information Technology during the last two years.These results have contributed to many cooperative projects with partners from academia and industry and cover current research interests including signal and image processing, pattern recognition, distributed systems, powerline communications, automotive applications, and robotics

Modelling of reliable service based operations support system (MORSBOSS)

Philosophiae Doctor - PhDThe underlying theme of this thesis is identification, classification, detection and prediction of cellular network faults using state of the art technologies, methods and algorithms

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Evolutionary Reinforcement Learning: A Survey

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, there remain several crucial challenges, including brittle convergence properties caused by sensitive hyperparameters, difficulties in temporal credit assignment with long time horizons and sparse rewards, a lack of diverse exploration, especially in continuous search space scenarios, difficulties in credit assignment in multi-agent reinforcement learning, and conflicting objectives for rewards. Evolutionary computation (EC), which maintains a population of learning agents, has demonstrated promising performance in addressing these limitations. This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL). We categorize EvoRL methods according to key research fields in RL, including hyperparameter optimization, policy search, exploration, reward shaping, meta-RL, and multi-objective RL. We then discuss future research directions in terms of efficient methods, benchmarks, and scalable platforms. This survey serves as a resource for researchers and practitioners interested in the field of EvoRL, highlighting the important challenges and opportunities for future research. With the help of this survey, researchers and practitioners can develop more efficient methods and tailored benchmarks for EvoRL, further advancing this promising cross-disciplinary research field