Chapter 15 - When Evolutionary Computing Meets Astro- and Geoinformatics

It is our great pleasure to welcome you to BADS 2011, the third Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems, co-located with ICAC 2011, the 8th IEEE/ACM International Conferenceon Autonomic Computing, Karlsruhe, Germany, June 14-18, 2011. BADS 2011 aims to provide a forum for exploring bio-inspired and self-* algorithms to build autonomic distributed systems; i.e., self-managing distributed systems that can organize, configure, optimize, scale, protect and/or heal themselves with minimal human intervention. Computing inspired by biology has a long history since 1950s. Its common objectives are to formulate theoretical models that are faithfully designed after biological principles, phenomena and processes and to exploit the models for developing computational tools (e.g., algorithms) and systems. Well-known bio-inspired algorithms include artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, reaction-diffusion algorithms, epidemic algorithms, cellular automata algorithms, gene expression/regulatory algorithms and Lindenmayer system. They have been successfully used as alternative and/or superior solutions to the problems that traditional algorithms cannot solve satisfactorily. In recognition of their achievements and potential, bio-inspired algorithms were named one of Scientific American magazine's 10 "World Changing Ideas 2010." The BADS workshop series focuses on distributed computing and examines the opportunities, current state-of-the-art, challenges and future outlook to create autonomic distributed systems with bio-inspired algorithms. We cover a broad range of aspects in distributed computing, including communication protocols, resource clustering, allocation and management, topology management, parallelism and concurrency, overlay construction and maintenance, decentralized information search and delivery, load distribution, synchronization, service selection, composition and deployment, labor division and task allocation, anomaly and misbehavior detection, malware propagation and detection. Following the success of its former editions in 2009 and 2010, BADS 2011 continues to focus on bio-inspired, autonomic distributed systems. In addition, it expands its themes to cover a general class of algorithms that exhibit self-* properties (e.g., self-configuring, self-organizing, self-managing, self-healing, self-scaling and selfadaptive properties) in the context of distributed computing. BADS 2011 has seven research papers selected through a rigorous review process. Each paper was reviewed and judged by at least three PC members on merits including correctness, originality, technical strength, quality of presentation and relevance to the workshop themes. We would like to sincerely thank the Program Committee members who have greatly contributed to the success of BADS 2011. BADS 2011 is organized in two sessions and one keynote presentation. The first session includes papers that present bio-inspired approaches for distributed computing. The second session focuses on solving optimization problems with distributed bio-inspired and self-* algorithms. The keynote presentation, entitled "Methods for Self-organizing Distributed Software", is given by Elisabetta Di Nitto, and focuses on the applications of self-organization in distributed systems. In nature, self-organization occurs such that a global behavior emerges from simple and local decisions made autonomously by each element of the system. This keynote overviews the benefits and challenges to apply the notion of self-organization for the development of distributed systems. It also presents several example applications such as energy efficiency and configuration optimization in cloud computing. The first session, "Bio-inspired Approaches for Distributed Computing", comprises four papers. The first paper, "Memetic Algorithm for Web Service Selection" by Ludwig, studies the service selection process, whose aim is to locate services of interest in service-oriented environments. The author proposes two approaches based on a genetic algorithm and a memetic algorithm to match consumers with services based on Quality of Service attributes. Both approaches are compared with an optimal assignment algorithm, called the Munkres algorithm, as well as with random approaches. The next paper, "OZMOS: Bio-Inspired Load Balancing in a Chord-based P2P Grid" by Brocco, proposes a load balancing mechanism, called Ozmos, that follows the principle of osmosis to relocate tasks between nodes in a P2P-based Grid. Bio-inspired agents are deployed to share information about the status of the Grid as well as to reschedule tasks between nodes. The efficacy of the proposed algorithm is illustrated in achieving system-wide load balance in Grids of different scales, with both homogenous and heterogeneous resources. The third paper, "Model-Driven Performance Engineering for Wireless Sensor Networks with Feature Modeling and Event Calculus" by Boonma and Suzuki, proposes an application development framework for wireless sensor networks. The proposed framework allows developers to graphically configure their applications and generate application code. With event calculus, the framework also estimates each application's performance such as per-node power consumption and network lifetime without running it on simulators and real networks. The proposed framework currently targets a shortest-path routing; however, it is a promising next step to estimate the performance of bio-inspired routing protocols such as pheromone-based chemotaxis routing. The last paper of the first session, "Description and Composition of Bio-Inspired Design Patterns: the Gradient Case" by Fernandez-Marquez, Arcos, Di Marzo Serugendo, Viroli and Montagna, investigates a group of bioinspired mechanisms in terms of design patterns. The authors show the relations between the different patterns and identify the boundaries of each mechanism. The mechanisms are organized into different levels. In basic level, Spreading, Aggregation and Evaporation are studied. Gradient is investigated in mid-level, and top-level mechanisms are Chemotaxis, Morphogenesis, and Quorum sensing. The second session, "Solving Optimization Problems with Distributed Evolutionary Algorithms", opens with a paper entitled "Protein Structure Prediction Using Particle Swarm Optimization and a Distributed Parallel Approach" by Kondov and Berlich. It demonstrates the efficiency of the standard Particle Swarm Optimization (PSO) algorithm to find the folded state of two proteins of different sizes starting from completely extended conformations. They illustrate that the predicted structure of the larger protein is in good agreement with the structure from the Protein Data Bank within the experimental resolution. The parallelization of PSO is shown to speed up the simulation linearly with the number of workers and it reduces the prediction time dramatically without loss of accuracy. The next paper, "Discrete Optimization Problem Solving with three Variants of Hybrid Binary Particle Swarm Optimization" by Singh V., Singh D., Tiwari and Shukla, presents three variants of the binary PSO algorithm in order to solve discrete optimization problems. This is achieved by introducing an additional step of crossover that varies in all the three variants. These hybrid algorithms show competitive results compared to other state-of-theart metaheuristics. The third paper, "Self-organized Invasive Parallel Optimization" by Mostaghim, Pfeiffer and Schmeck, proposes a new parallelization framework for optimization algorithms. In this approach, the self-organized resources are represented as a unified resource to the user who only specifies the optimization problem and his/her preferences to the system. The invasive approach starts with one resource and automatically divides the optimization task stepwise into smaller tasks, which are assigned to more resources. The job assignment pattern is decided on demand, i.e., the number of required resources is estimated during the optimization process. The authors examine their generic framework on multi-objective problems.

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

Nature has remedies for almost all problems. Though biological systems exhibits organised, complex and intelligent behaviour, they comprise of simple elements and governed by simple rules. Hence, mimicking such systems has been the attraction of researchers in the fields of computer science, neuroscience and biology for a long time. Generating complex behaviour from small agents working locally following simple rules is a highly cost-effective solution of the real life problem. Bio-inspired computing can be achieved through different models such as stochastic, ad hoc or discrete models; new paradigm inspired from nature like evolutionary approach and immune systems; and new platform, novel architecture and specially designed material such as artificial fuel cell. The consortium of bio-inspired computing are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing and quantum computing, etc. This article discusses consortium of bio-inspired computing along with applications and research scope. In spite of having advantages offered by partial simulation of natural intelligence, there are some limitations of the bio-inspired computing that need to be addressed. These challenges include creation of new model, techniques and platforms for bio-inspired computing. This article concludes with the challenges to be explored in the field.

Three bio-inspired optimization schemes, integrated into a robust frequency domain 2-D finite-element code, have been assessed by the design of two novel nonintuitive photonic crystal-type devices: an unbalanced power splitter and a micro-to-nano waveguide coupler. The bio-inspired schemes were taken from three biological-based classes of algorithms: genetic algorithm, evolution strategy, and artificial immune system. The results show that such schemes constitute valuable and powerful tools for the design of advanced and sophisticated photonic devices, where pixels concept was changed by photonic crytals structures to enable their future fabrication.

This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are many aspects of AIS that have direct parallels with SI and examine the role of AIS and SI in science and also in engineering, with the primary focus being on the immune system. We explore how in some ways, algorithms from each area are similar, but we also advocate, and explain, that rather than being competitors, AIS and SI are complementary tools and can be used effectively together to solve complex engineering problems.

Запропоновано метод оптимізації неперервних функції багатьох змінних, який втілює підходи методів рою часток та моделювання штучної імунної системи. Проведено дослідження з метою визначення оптимальних налаштувань алгоритму, що реалізує запропонований метод. Показано, що запропонований метод може бути застосований як складова інформаційної технології супроводу підтримки управлінських рішень та розв’язання задач оптимізації технологічних процесів у металургійному виробництві. Метою роботи є розробка оригінального гібридного методу глобальної оптимізації на основі методу штучної імунної системи та методу рою частинок, а також формулювання рекомендацій щодо налаштування його параметрів. Запропонований метод має прискорити прийняття рішень при керуванні технологічними процесами у металургійному виробництві та підвищити їх точність. Методологія забезпечення рішення полягає у поєднанні основних пошукових операторів еволюційних методів оптимізації на основі рою часток і моделювання штучної імунної системи людини. Використовуються традиційні для ройового пошуку кроки з пошуку рішення та обміну інформацією про знайдені локальні оптимуми. Такий пошук доповнюється принципом змагальності, запозиченим у методі штучної імунної системи, для чого популяція поділяється на менші рої або пошукові команди. Також запропоновані кроки контролю різноманіття в рої та радіусу розсіювання часток. Останнє дозволяє вирішувати задачі як безумовної, так і умовної оптимізації у неперервному просторі високої розмірності. Наукова новизна отриманих у роботі результатів полягає в формулюванні нового еволюційного методу оптимізації в неперервному просторі на основі ройового інтелекту, який на відміну від відомих раніше ройових методів використовує принцип змагання підгруп рою та оператор стиснення популяції, властивих методу моделювання штучних імунних систем. Емпірично встановленні параметри методу, що визначають його ефективність. Висновки. Застосування запропонованого еволюційного методу пошукової оптимізації до мінімізації тестових функцій у неперервному просторі розмірністю до 20 показало його ефективність у порівнянні з відомими раніше. В подальшому актуальним бачиться застосування викладеного алгоритму для розв’язання задач оптимізації технологічних процесів у металургійному виробництві. Зокрема, даний алгоритм вбачається ефективним для задач шихтування, оптимізації використання феросплавів у ливарному виробництві, а також при прогнозуванні механічних властивостей готової продукції.

Computer systems are characterized by an ever-growing complexity and a pronounced distributed nature. Since controlling highly distributed systems and managing the communication among them are far beyond the capabilities of a central entity, it is essential to develop new decentralized architectures. Such architectures, for example Grids, Clouds and P2P systems, are increasingly popular, but they need new types of algorithms to be efficiently managed. The recent paradigm of Organic computing (OC) concerns a large collection of intelligent (embedded) systems with potentially unlimited networks and spontaneous local interactions (Merkle et al. 2008; Muller-Schloer et al. 2011). The goal of OC is to develop new concepts and tools to manage the complexity of such systems, which must be designed to be adaptive, self-organized and manageable at the same time (Schmeck 2005). Adaptive OC systems must be capable of learning and adjusting their behaviour to dynamically changing environments. In addition, they should have self-x properties such as self-healing, selfdefining, self-configuring and self-optimizing. These properties are inspired by the mechanisms used by the autonomic nervous system to regulate the body without conscious input from the individual. Bio-inspired algorithms and techniques feature fault-tolerant and self-adaptive behaviours that help to boost the autonomic nature of distributed systems (Germain-Renaud and Rana 2009), and are proving effective to solve highly parallel and distributed problems. These techniques sometimes rely on the operations of agents, whose behaviour is inspired by biological systems, including ant colonies, bird flocks, honey bees, bacteria, and many more. In such systems, ‘‘swarm intelligence’’ emerges from the interaction of a large number of very simple agents. Bio-inspired algorithms have been successfully used as alternative and/or superior solutions to the problems that traditional algorithms cannot solve satisfactorily. In recognition of their achievements and potential, bio-inspired algorithms were named one of Scientific American magazine’s 10 ‘‘World Changing Ideas 2010’’. Bio-inspired algorithms and systems are applied to hard and large problems in a variety of areas. Some examples are optimization problems solved with genetic algorithms, routing strategies inspired by honey bee behaviour, ant-inspired resource discovery and data mining computations in Grid, Cloud and P2P frameworks, and so on. The papers included in this special issue show recent results in the area of bio-inspired distributed computing. The authors presented their preliminary works in the 3rd Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems, BADS 2011 (Folino et al. 2011), which was held together with the International Conference on Autonomic Computing (ICAC 2011) in Karlsruhe, Germany, in June 2011. The three papers were first selected among the papers presented in BADS 2011; then, they were extended and refined through two rounds of reviews. The paper ‘‘Protein structure prediction using distributed parallel particle swarm optimization’’, by Ivan Kondov, demonstrates the efficiency of the standard Particle swarm optimization (PSO) algorithm in the hard task of finding G. Folino (&) C. Mastroianni ICAR-CNR, Rendi, CS, Italy e-mail: folino@icar.cnr.it

The modeling of emergent swarm intelligence constitutes a major challenge and it has been tacked in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individuals determine their behaviors based on their internal contradictions whilst they associate and in-teract to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the development of individuals’ internal contradictions and the interactions taking place between in-dividuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configu-rations of individuals’ internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applica-bility. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.

Although different kinds of evolutionary algorithms (EAs) have been designed and achieved great success on many optimization problems, they are usually limited to some small-scale problems, e.g. with less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. Therefore, scaling EAs to large size problems have attracted more and more interest. Conventional EAs mimic the seemingly random natural processes by which species evolve. These evolution processes are slow or inefficient. Now, genetic engineering has enabled man to increase both the yields and quality of some crops fast by modifying some part of their genome precisely. In this paper, inspired by the ideas of the genetic engineering, we designed a local selection operator by decomposing the high-dimensional problem into some subcomponents and assigning a local fitness function to evaluate each subcomponent. Then a new differential evolution (DE) is proposed by inserting the local selection operator into the framework of DE. Numerical experiments were carried out to evaluate the performance of the new algorithm on a large number of benchmark functions. The results show that the new algorithm is effective and efficient for high-dimensional optimization.

The performance of deep learning malware classifiers depends heavily on hyperparameter configuration. Deep learning malware detection benefits from principled hyperparameter optimization. This study evaluates six bio inspired optimizers across four neural architectures and three widely used malware data representations. Artificial Immune System methods include Clonal Selection, Immune Network, and a Dendritic Cell inspired optimizer. Evolutionary methods include Genetic Algorithm, Particle Swarm, and Differential Evolution. Baselines for each deep learning model were implemented using domain informed choices rather than naïve defaults to avoid overstating optimization gains and to reflect realistic practitioner practice. All runs were under a strictly matched evaluation budget (∼60 function evaluations per run) with five independent seeds per permutation. On API call sequences, LSTM produced the highest test accuracy near 0.991, with Differential Evolution and Particle Swarm frequently leading, while Transformer on the same dataset reached about 0.990. On PE headers, ANN achieved about 0.989 with Differential Evolution and Immune Network. Opcode n grams formed the most challenging setting, with the best CNN accuracy near 0.75 under multi class family labels. Artificial Immune System algorithms often reached the highest accuracies but typically at greater runtime cost, whereas Evolutionary Algorithms, especially Differential Evolution and Particle Swarm, were faster to strong solutions. under a strictly matched evaluation budget (∼60 function evaluations per run) with five independent seeds per permutation. The optimizers all perform comparatively well to each other on each dataset, with no statistical difference in test accuracy. The experiment also quantify accuracy versus computing tradeoffs, highlight dataset model synergies, and provide guidance on selecting model optimizer pairs for deployment-oriented scenarios. The research notes ceiling effects on API/PE and a modest, matched budget; future work will examine larger budgets and more challenging datasets to further probe optimizer differences under controlled evaluation.

With the great advancement in wireless technology, number of wireless sensor network applications have increased in which different sensor nodes communicates with each other via sending data among themselves. Query for communication among sensor nodes can be framed in different forms leading into different computation cost. So, the generation and selection of query plan of minimum cost becomes combinatorial in nature which cannot be solved in polynomial time to achieve global optimal cost of data communication. One of the solution to address this problem is nature inspired algorithms. These algorithms have served to number of real life intricate problems. Amidst of all algorithms, bio-inspired algorithms have largely accepted to assist such problems. Artificial immune system (AIS), one of bio-inspired algorithm is inspired from natural human immune system has been explored here. Clonal selection process, one of AIS approach has been discussed in this chapter to generate optimal distributed query plans in distributed wireless sensor network.

Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter first presents a brief overview of EAs, focusing mainly on two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the main concepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. Finally, it discusses Multi-Objective EAs, based on the concept of Pareto dominance, and their use in several data mining tasks.

The article is devoted to the current and promising direction of the implementation of the Industry 4.0 program based on the introduction of artificial intelligence (AI) approaches in the industrial production of the oil and gas industry. A review of the application of modern bioinspired AI approaches for solving problems of control and diagnostics of industrial equipment is carried out. The application of the approach of artificial immune systems (AIS) and various modified AIS algorithms is substantiated in the creation of innovative and competitive applications for solving various specific engineering problems and creating a modern highly efficient technology for automating production processes at oil refineries. The theoretical foundations for the creation of an intelligent innovative technology based on a unified artificial immune system (UAIS) for distributed systems for complex objects control using modern computer technology have been developed. Definitions of UAIS and immunological homeostasis are given. Unification is carried out with the aim of reducing the variety of modified AIS algorithms and their optimal use for solving the assigned tasks for the technological processes control. The classification and assessment of the effectiveness of the application of modified AIS algorithms is carried out. The block diagram of an intelligent control system based on the developed UAIS is presented.

The inspiration from Biology and the Natural Evolution process has become a research area within computer science. For instance, the description of the artificial neuron given by McCulloch and Pitts was inspired from biological observations of neural mechanisms; the power of evolution in nature in the diverse species that make up our world has been related to a particular form of problem solving based on the idea of survival of the fittest; similarly, artificial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The first and second editions of the International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), were held in Granada, Spain, 2006, and in Acireale, Italy, 2007, respectively. As in these two previous editions, the aim of NICSO 2008, held in Tenerife, Spain, was to provide a forum were the latest ideas and state of the art research related to nature inspired cooperative strategies for problem solving were discussed. The contributions collected in this book were strictly peer reviewed by at least three members of the international programme committee, to whom we are indebted for their support and assistance. The topics covered by the contributions include nature-inspired techniques like Genetic Algorithms, Ant Colonies, Amorphous Computing, Artificial Immune Systems, Evolutionary Robotics, Evolvable Systems, Membrane Computing, Quantum Computing, Software Self Assembly, Swarm Intelligence, etc.

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.