Free download. Book file PDF easily for everyone and every device. You can download and read online Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach book. Happy reading Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach Bookeveryone. Download file Free Book PDF Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach Pocket Guide.

In this special session, we will discuss new parallel and distributed evolutionary computation EC in the smart city era, in term of both reduction in execution time and improvements in accuracy of the achieved solutions. Granular computing GC focuses on the knowledge representation and reasoning with information granules, and fuzzy sets and rough sets are two crucial branches of GC.

Evolutionary computation for granular computing emphasizes the utility of different evolutionary algorithms to various facets of granular computing, ranging from theoretical analysis to real-life applications. The main motivation for applying evolutionary algorithms to granular computing tasks in the knowledge reasoning is that they are robust and adaptive search methods, which can perform a global search in the space of candidate solutions. The benefits of exploring the combination of granular computing and evolutionary computation will have an impact in multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.

The goal of this special section aims at providing a specific opportunity to review the state-of-the-art of evolutionary computation for granular computing. Deep learning has shown significantly promising performance in addressing real-world problems. The achievements of such algorithms owe to its deep structures. However, designing an optimal deep structure requires rich domain knowledge on both the investigated data and the general data analysis domain, which is not necessarily held by the end-users.

In addition, the problem of searching for the optimal structure could be non-convex and non-differentiable, and existing accurate methods are incapable of well addressing it. Evolutionary computation EC approaches have shown superiority in addressing real-world problems due largely to their powerful abilities in searching for global optima, and requiring no rich domain knowledge. However, most of the existing EC methods currently work only on relatively shallow structures, and cannot provide satisfactory results in searching for deep structures.

In this regard, evolutionary deep learning, would be a great research topic. The theme of this special session aims to bring together researchers investigating methods and applications in evolutionary deep learning. Many real-world engineering optimization problems not only require the simultaneous optimization of a number of objective functions, but also need to track the changing optimal solutions. Here, where either the objective functions or the constraints change over time, an optimization algorithm should be able to find, and track the changing set of optimal solutions and approximate the time-varying true Pareto front.

Therefore, the DMOO algorithm also has to deal with the problems of a lack of diversity and outdated memory. EThe main goal of this session is to emphasize the newest techniques in solving dynamic multi-objective optimization problems and handling current issues. Therefore, session aims at providing a forum for researchers in the area of DMOO to exchange new ideas and submit their original and unpublished work.

Compare to other prominent swarm intelligence paradigms, as for example Particle Swarm Optimization PSO , Ant Colony Optimization ACO , Firefly, and so on, SOMA is distinguished by competitive-cooperative phases, inherent self-adaptation of movement over the search space, as well as by discrete perturbation mimicking the mutation process known from the classical evolutionary computing techniques. The SOMA perform significantly well in both continuous as well as discrete domains. The SOMA has been used successfully on various tasks as the real-time plasma reactor control, aircraft wings optimisation, chaos control, large scale, combinatorial and permutative optimisation tasks.

This special session is concern about original research papers discussing new results on and with SOMA, as well as its novel improvements tested on widely accepted benchmark tests. This session aims to bring together people from fundamental research, experts from various applications of SOMA to develop mutual intersections and fusion.

Also, a discussion of possible hybridisation amongst them as well as real-life experiences with computer applications will be carried out to define new open problems in this interesting and fast-growing field of research. Most of modern engineering and scientific applications are concerned with big optimization problems in terms of number of variables more than thousands , objectives, constraints, data, uncertainties and so on.

The goal of this special session is to come up with cutting-edge evolutionary and meta-heuristic approaches to deal with big optimization problems such as parallel design and implementation, decomposition methods, model-based optimization, surrogate-based optimization, cross-domain, exascale and ultra-scale optimization, deep learning architectures, optimization under uncertainties, and mixed optimization.

This special session focuses on, but not limited to, the following areas:. Recently with the development of IoT and high-performance computing, the importance of cyber-physical systems that connect the real and a virtual world is increasing. For example, Japan government promotes "Society 5. System modeling, signal processing, and filtering are the fundamental techniques in the cyber-physical system and an interaction between the real and a virtual world.

Such system techniques should have the adaptability to the nonlinearity of the physical system and time-varying environments. The evolutionary approach will be a successful methodology in this fields. This special session promotes the theoretical discussion on applications of evolutionary computation to control, modeling and filtering for dynamical systems and cyber-physical systems, developments of novel filtering algorithm using evolutionary computation, and data assimilation methods for simulation studies. The design of experiments by using evolutionary algorithms is also important for this fields to build efficient simulation scenarios.

Authors are invited to submit their original work including the following but not limited to topics:. This simple idea, which is somewhat standard in computer science, allows to open up new research perspectives and challenges in both the fundamental level of our understanding of multi-objective problems and concerning designing and implementing new efficient algorithms for solving them.

Many different DMOEAs variants have been proposed, studied and applied to various application domains in recent years. However, DMOEAs are still in their very early infancy, since only a few basic design principles have been established compared to the vast body of literature dedicated to other well-established approaches e. Thus, the topics of interest include but are not limited to the following aspects:. Locally, populations called fireworks exploit local landscape by a simple sampling method called explosion operation.

Globally, fireworks exchange condensed information and collaboratively decide parameters of their explosion. FWA achieved overwhelming success on both benchmark objective functions and real-world problems. Recent research includes many effective variants and huge amount of successful applications.

FWA framework has revealed competitive performance with other SI optimization methods. We are expecting researches on theoretical analysis and improvement of FWA and application of all kinds of practical situations. Full papers are invited on recent advances in the development of FWA.

The session seeks to promote the discussion and presentation of novel works related but not limited to the following issues:. Swarm intelligence, as a crucial aspect of the artificial intelligence domain, has become an increasingly important modern computational intelligence method in artificial intelligence and computer science.

In swarm intelligence, the nascent collective intelligence of groups of simple agents possess a powerful global search capability, and has been demonstrated to be able to determine the optimal solution within a rational time by numerous study fields using swarm intelligence algorithms, such as GA, MA, ACO, PSO, ABC, SSO, etc. Swarm intelligence algorithms play a paramount role in optimizing the increasing problems in related complex systems.

Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. This special session will provide a cardinal opportunity to present the latest scientific results and methods on the collaboration of Swarm Intelligence in Operations Research, Management Science and Decision Making, to discuss and exchange the latest developments in Swarm Intelligence, and to explore the future directions in Swarm Intelligence. Authors are invited to submit their original and unpublished work in the areas including, but not limited to:.

Smart logistics refers to the efficient and effective design, planning and control of the supply chain processes though intelligent technologies, such as software to improve the design of networks, software to automate scheduling, routing, and dispatching, material handling systems, etc. Respectively, the relevant research methods involve clustering, stochastic dual dynamic programming, planning and optimization. In recent years, evolutionary computation EC techniques have been introduced to the area of logistics.

Examples include applying single-objective and multi-objective evolutionary algorithms to facility layout decision problems and vehicle routing problems. This special session aims at presenting the latest research on EC applications to logistics. Real-world applications of EC on logistics are highly recommended. The topics include but are not limited to:.

By bringing together multiple energy systems, including electricity, thermal sources and fuels, and other critical infrastructures, such as transportation, water and communication, we can improve their efficiency, reliability and resiliency. Currently, most energy systems and critical infrastructures are operated independently. Multiple energy systems integration MESI focus on the coordination and optimization of those energy systems and critical infrastructures in the operation and planning stages.

Various operating conditions, include normal operations, typical interruptions and extreme events, are considered to maximize the value of each unit of energy we use and enhance the ability of those systems and infrastructures to withstand and recover from typical and catastrophic disturbances. Most optimization problems we encounter in MESI could be nonconvex and contain large number of integer variables which cannot be solved efficiently using existing mathematic programming methods.

The fields of computer vision CV and image processing IP have tried to automate tasks that the human visual system can do, with the aim of gaining a high-level understanding of images and videos. CV algorithms have been successfully applied to a large number of real-world problems ranging from remote sensing to medical image analysis, video surveillance, human-robot interaction, and computer-aided design.

In turn, evolutionary computation EC methods have shown to be more efficient than classical optimization approaches for discontinuous, non-differentiable, multimodal and noisy problems. The scope of this special session covers, but is not limited to, the application of EC paradigms to:. Evolvable systems encompass understanding, modelling and applying biologically inspired mechanisms to physical systems.

Having showcased examples from analogue and digital electronics, antennas, MEMS chips, optical systems, carbon nanotubes as well as quantum circuits in the past, we are looking for papers that apply techniques and applications of evolvable systems to these hardware systems. Within the scope of this special session are Evolutionary Systems for Semiconductor Design, Simulation and Fabrication. Evolutionary Robotics and Machine Learning. Evolutionary Computing Systems for Artificial Intelligence.

Evolutionary Substrates for Unconventional Computing. Topics include:. During last years, a wide range of population-based meta-heuristics have been proposed with the aim of dealing not only with benchmark optimization problems, but also with real-world applications. Population-based approaches keep a set of solutions with the aim of exploring the search space in an efficient way.

Usually, a diverse set of solutions is maintained, meaning that several regions are explored simultaneously. However, one common problem of population-based meta-heuristics is that for some test cases they might exhibit a tendency to converge quickly towards some regions. One of the most frequent problems that these types of meta-heuristics have to deal with is premature convergence, which arises when every member of the population is located at a sub-optimal area of the decision space from where they cannot escape. A significant number of methods have been proposed in order to preserve the diversity in a set of solutions.

This special session aims to attract the most relevant advances produced in the following topics, including but not limited to:. Topics such as the ones of multi-objective process optimisation, decision-making, real-time performance optimisation are pivotal for the realisation of the Industry 4. Whether the manufacturing application is about digitisation systems, robotics, digital manufacturing or fundamental understanding of advanced processes and complex materials, evolutionary optimisation is ideally placed to offer algorithms and methods to address challenges specific to the manufacturing sector.

2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms

In this session, we invite contributions that demonstrate new applications, results, as well as new algorithms that address challenges specific to advanced manufacturing systems. Specific topics of interest include, but not limited to:. Quantum computing QC represents a broad topic encompassing a large number of approaches, technologies and techniques focusing on the usage, application, design and understanding of quantum computing systems.

Evolutionary Computing EC has been on several occasions directly linked to quantum computing such as quantum evolutionary computation or evolutionary design for quantum computer design, etc. Because quantum computing evolves in the very large Complex Hilbert space, evolutionary methods are a prime tool for exploration and exploitation of quantum properties.

The aim of this special session on quantum and evolutionary computing is to provide a platform for researchers of various fields to discuss the latest advances in related fields, technologies and approaches linking and using quantum and evolutionary approaches. The scope of this special session covers among others but not limited to the following topics:.

Research work is welcome concerning complex real-world applications of evolutionary computation EC in the energy domain. The problems can be focused on different parts of the energy chain e. Problems dealing with uncertainty, dynamic environments, many-objectives, and large-scale search spaces are important for the scope of this special session.

This special session aims at bringing together the latest applications of EC to complex optimization problems in the energy domain. Therefore, participants are also welcome to submit the results of their algorithm to our session. Topics must be related to EC in the energy domain including, but not limited to:. Transportation serves as an important task in modern human life and industry activities. Optimization for intelligent transportation systems has shown to be a difficult problem. The worldwide division of labor, the connection of distributed centers, and the increased mobility of individuals, furthermore, lead to an increased demand for efficient solutions to solve the problems in transportation networks.

Evolutionary computation plays a significant role and has gained promising results in optimization of transportation networks. The aim of this special session is to promote research and reflect the most recent advances of evolutionary computation in in intelligent transportation systems. From global trajectory optimization to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites, computational intelligence CI techniques have become an important — and in many cases inevitable — tool for tackling these kinds of problems, providing useful and non-intuitive solutions.

This special session intends to collect many, diverse efforts made in the application of computational intelligence techniques, or related methods, to aerospace problems. In particular, evolutionary methods specifically devised, adapted or tailored to address problems in space and aerospace applications or evolutionary methods that were demonstrated to be particularly effective at solving aerospace related problems are welcome.

Evolutionary computation EC algorithms have aroused great attentions from both the academic and industrial communities in recent years due to their promising performance in many real-world optimization problems. In order to promote traditional centralized EC algorithms to solve the complicated optimization problems in big data era, using distributed technology to enhance EC algorithms is a promising approach.

Distributed EC DEC algorithms pose several new challenges: the design of DEC algorithms, the selection of distributed architectures, distributed resource scheduling method, and the deployment of the EC algorithms on distributed computing platform. This Special Session is to draw the attentions of researchers in both the communities of distributed technology and EC to exchange their latest advances in theories and technologies of EC, distributed technology, and the works on extending DEC approaches to real-world applications.

Authors are invited to submit their original and unpublished work with the topics including, but not limited to:. In the past two decades, many evolutionary algorithms have been developed and successfully applied for solving a wide range of optimization problems. Although these techniques have shown excellent search capabilities when applied to small or medium sized problems, they still encounter serious challenges when applied to large scale problems, i.

This is due to the Curse of dimensionality, as the size of the solution space of the problem grows exponentially with the increasing number of decision variables, there is an urgent need to develop more effective and efficient search strategies to better explore this vast solution space with limited computational budgets. In recent years, research on scaling up EAs to large-scale problems has attracted significant attention, including both theoretical and practical studies.

Existing work on tackling the scalability issue is getting more and more attention in the last few years. More specifically, we encourage interested researchers to submit their original and unpublished work on:.

IEEE CEC 12222 Special Sessions

Differential evolution DE is one of the most promising research areas in evolutionary computation. Over the past decades, DE-related algorithms have frequently demonstrated superior performance in various challenging tasks. Meanwhile, the remarkable efficacy of DE in real-world applications significantly boosts its popularity.

Top 5 Algorithms used in Data Science - Data Science Tutorial - Data Mining Tutorial - Edureka

However, the lack of systematic benchmarking of the DE-related algorithms in different problem domains, the existence of many open problems in DE, and the emergence of new application areas call for an in-depth investigation of DE. This special session aims at bringing together researchers and practitioners to review and re-analyze past achievements, to report and discuss latest advances, and to explore and propose future directions in this research area.

Authors are invited to submit their original and unpublished work in the areas including but not limited to:. Multi-agent systems MAS are computerized systems composing of multiple interacting and autonomous agents within a common environment of interest for problem-solving. The development of intelligent agents that are capable of adapting to the complex or dynamic environment has attracted increasing attentions over the past decades. In computational intelligence, evolutionary computation EC , in particular, has been shown to provide a reliable and flexible contender over traditional mathematical approaches for solving complex optimization problems, especially if near global optimum solutions are sought.

According to the recent studies, EC based techniques, including evolutionary algorithms, swarm intelligence, evolutionary reinforcement and transfer learning, are already starting to show up in developing more significant intelligence among multiple agents in MAS. Actionable Knowledge Discovery AKD is an importance task for data analysis in real-world applications.

To reach that, multiple aspects, e. Evolutionary Computation EC already shows its powerful ability for dealing with optimization problems in various fields and searching the global optimal solution. The aim of this special session is to provide a forum to disseminate and discuss recent and significant research effort on computational intelligence and other intelligence techniques for the AKD. This session is open to any high quality submission from researchers who work on mainly computational intelligence methods for the AKD.

The scope of this section includes, but is not limited to the following topics:. Cybersecurity aims at preventing and detecting cyber attacks on Internet-connected systems which include data, software, and hardware, in order to maintain the confidentiality, integrity, and availability of those assets.

Utilizing various evolutionary computation EC and machine learning techniques to tackle numerous problems related to cybersecurity have received increasing attention due to the success of such techniques to tackle problems in many other domains. This interdisciplinary special session aims at providing a focused discussion forum for utilizing EC based techniques to automatically tackle different cybersecurity-related problems and other types of network-based attacks.

It also aims at promoting both practical applications and theoretical development of EC techniques for information and network security domains. The scope of this special session covers, but not limited to, the following topics:. The impact of optimization in communication networks, such as Internet and mobile wireless networks, on the modern economy and society has been growing steadily. At the present, new technologies like 5G cellular mobile radio systems, optical Internet, and network virtualization and automation are in widespread use, allowing fast data communications, new services and applications.

With the advent of computer systems, computational intelligence approaches have been developed for systematic design, optimization, and improvement of different communication network systems. The aim of the special session is to promote research and reflect the most recent advances of evolutionary computation, including evolutionary algorithms, deep learning, neural network, fuzzy systems, metaheuristic techniques and other intelligent methods, in the solution of problems in communication networks.

Nonlinear equation systems NESs frequently arise in many physical, electronic, and mechanical processes. Very often, a NES may contain multiple roots. For solving NESs, several classical methods, such as Newton-type methods, have been proposed. However, these methods have some disadvantages in the sense that they are heavily dependent on the starting point of the iterative process, can easily get trapped in a local optimal solution, and require derivative information. Moreover, these methods tend to locate just one root rather than multiple roots when solving NESs.

Solving NESs by EAs is a very important area in the community of evolutionary computation, which is challenging and of practical interest. However, systematic work in this area is still very limited. The aim of special session is to facilitate the development of EAs for locating multiple roots of NESs. Bilevel programming problems BLPPs are non-convex optimization problems with two levels, namely upper level and lower level. For such hierarchical structure, we need to fix an upper decision variable as a parameter and to solve the lower optimization problem.

Such requirements make the bilevel optimization problems difficult to solve and time consuming. In the context of multi-objective bilevel optimization problems, there does not exist too many studies. Most of the existing algorithms focus on approximate solution strategies and K-K-T conditions. However, these algorithms are very time consuming and strongly problem dependent. In order to handle such problems efficiently and effectively, there is a need for theoretical as well as methodology advancements to solve single bilevel and multi-objective bilevel optimization problems.

This special session on will bring together researchers working on the following topics:. This special session solicits original research papers or reviews that would shape and advance design, manufacture and engineering management in the Industry 4. Computational intelligence CI utilises a set of nature-inspired modelling and optimisation approaches to complex real-world problems. Papers addressing how to create designs and build machines smartly, thereby leading to a step improvement in manufacturing autonomy and industrial efficiency, performance and competitiveness, would be particularly welcome.

Topics include, but are not limited to:. Gene Expression Programming GEP is a popular evolutionary algorithm for automatic generation of computer programs. Over the past decades, GEP has undergone rapid advancements and developments. The real-world applications of GEP are also multiplying fast, including regression, classification, combinatorial optimization, data mining and knowledge discovery. The aim of this special session is to provide a forum for researchers in this field to exchange the latest advances in theories, technologies, and practice of Gene Expression Programming.

Topics of interest include, but are not limited to, GEP in the following aspects:. Estimation of Distribution Algorithm EDA is a special kind of evolutionary algorithm that works by constructing a probability model to estimate the distribution of the predominant individuals in the population. In a border sense, there are also some other evolutionary computation EC or swarm intelligence SI algorithms that work by implicitly constructing a probability distribution in the solution space.

For example, in ant colony optimization ACO , ants deposit pheromone on paths, which can be seen as an implicit probability model. Such implicit probability distribution construction behavior provides a more feasible way to build promising probability distribution models, and thus further extends the concept of Probability Distribution Based Evolutionary Algorithms.

There are a lot of applications in probability distribution based evolutionary algorithms, for example, task scheduling, routing, mixed-variable optimization, etc. Constructing suitable probability distribution models and studying the theory behind the probability distribution are important for the research of probability distribution based evolutionary algorithms, which are promising in solving such real-world applications.

The aim of this special session is to promote the research on theories and applications in this filed. This session aims to bring together both theoretical developments and applications of Computational Intelligence to software engineering SE , i. All bio-inspired computational paradigms and machine learning techniques are welcome, such as genetic and evolutionary computation, including multi-objective approaches, fuzzy logic, intelligent agent systems, neural networks, cellular automata, artificial immune systems, swarm intelligence, and others, including machine learning techniques.

This special session aims to provide a forum for the presentation of the latest data, results, and future research directions on evolutionary methods and machine learning in software engineering. The special session invites submissions in any of the following areas:. Original research papers are solicited in related areas of biologically-inspired algorithm based evolutionary computation for robotics.

Submissions to the special session should be focused on theoretical results or innovative applications of biologically-inspired algorithms associated with evolutionary computation for robot and vehicle systems. Specific topics for the special session include but are not limited to:. Swarm Intelligence SI algorithms consist of a population of semi-autonomous agents coupled with a social interaction mechanism.

Despite the characteristically-simple rules governing each individual agent, an intelligent collective behaviour emerges as a result of the social interactions among agents. Often, the emergence of such collective intelligence is contingent upon employing an appropriate configuration of the algorithm. However, the optimal configuration s for the algorithms are typically problem dependent and may change throughout the search process. Thus, determining an appropriate configuration a priori may lead to sub-optimal performance. To address the shortcomings of a priori configuration, adaptive SI algorithms aim to modify their configurations during the search process based on various observations.

The purpose of this special session is to provide a forum for researchers to disseminate their original research in the field of adaptive swarm intelligence algorithms. Topics of interest include, but are not limited to, adaptive swarm intelligence algorithms in the following aspects:. Extensive research has been developed in computational intelligence and evolutionary computing, ranging from theoretical foundations, principles, to practical applications across various domains including medical, industry and education.

It has been widely recognized that the use of Sensor technologies and UAV Platforms is increasing among researchers and developers. It is required that sensors can perform a rapid assessment and analysis of collected data to provide real time feedback to the end users. UAVs are required to perform autonomous paths optimization for different research purposes.

Researchers are exploring potential novel evolutionary computation solutions for real time analysis. The proposed session aims at demonstrating the latest research and development on evolutionary computation and their applications in sensors development and unmanned aerial vehicle UAV platform optimization. List of main topics include but not limited to:.

Although Evolutionary Algorithms are very good at mimicking adaptation within a species to optimize solutions for difficult problems, creating algorithms that can mimic the development of two or more species from a common ancestor has been a challenge. There are versions of Evolutionary Algorithms that have some characteristics of speciation, but none that match natural processes. Such algorithms would be a good step in the development of a general purpose Evolutionary Algorithm and would help in understanding the principles of evolution.

In regards to this research, we consider a population to be distinct and a separate species if it is made up of individuals that are unable to produce viable offspring with individuals from the other population or if offspring are produced, they are sterile. The short term goal, which is reasonable for this special session, is to have individuals of differing species choose not to mate and if they do produce offspring, the offspring do not continue to reproduce.

In this way, the gene pools for each of the species will be isolated. The purpose of this special session is to bring together people working on Evolutionary Algorithms that tend toward or have the potential for speciation. Some possible topics of interest include:. Of late, scientists have stressed upon the hybrid metaheuristics, which being a judicious combination of several other metaheuristics, algorithms from mathematical programming, constraint programming, or machine learning algorithms, have been found to be more robust and failsafe.

The advent of the quantum computing paradigm has also given an impetus to evolving time efficient hybrid metaheuristics, where the conjoined principles of quantum mechanics successfully enhance the real time performance of the hybrid metaheuristics. Clustering or cluster analysis partitions a dataset into a meaningful group of similar objects.

However, the existing methods require an a priori knowledge about the number of clusters present in the dataset. Automatic clustering, on the other hand, aims to find out the optimal number of clusters from a dataset without having any prior knowledge about the number of clusters. This special session aims to bring together recent advances in methodological approaches and applied techniques related to the use of hybrid metaheuristics for automatic clustering of data and its analysis.

We are soliciting contributions on but not limited to the following:. The aim of this special session is designing a multi-disciplinary program for automated design of new materials requiring knowledge of physical chemistry, evolutionary optimization grammatical evolution , Python programming, 3D graphics and mathematics for calculating fitness functions and 3D positions and energies of individual atoms. The prediction of new nanostructures requires knowledge of physical chemistry and the ability to select a suitable method of evolutionary optimization.

The first such predictor was designed by Organov USPEX , which combines the knowledge of quantum physics and evolutionary optimization. Since quantum physics does not contain a structural description of atoms, this predictor is capable of designing structures with hundreds of atoms on supercomputers. The combination of the structural description of atomic nuclei and grammatical evolution does not have this limitation. The topics of this special session include:.

Cites per any

Crowdsourcing refers to the practice of involving a crowd or group of people for accomplishing some large-scale task in an efficient or innovative way. Due to involvement of malicious crowd workers, it is sometimes difficult to utilize the crowdsourced opinions in decision making.

Different evaluation criteria such as cost, time and accuracy are to be optimized to build an effective crowdsourcing framework. Therefore, evolutionary and other metaheuristic optimization techniques can be used in solving these complex problems. Moreover, machine learning techniques like deep neural networks, support vector machines, random forest, Bayesian learning, Markov chains, and probabilistic graphical models can be employed in different problems like aggregating crowd opinions, classifying crowd workers, performing fusion of crowdsourced solutions, etc.

Thus combination of evolutionary algorithms and machine learning methods can solve various crowdsourcing problems in different domains, including recommender systems, social networks, education, e-commerce and healthcare. This special session aims to bring together researchers from both academia and industry to share the ideas of the application of machine learning techniques and evolutionary computation in real-life problems that employ crowdsourcing.

The potential topics of interest include, but not limited to:. Evolutionary Computation EC is a huge and expanding field, attracting more and more interests from both academia and industry. For the discrete domain and application scenarios, we want to pick the best algorithms. Actually, we want to do more, we want to improve upon the best algorithm.

Upcoming Events

This requires a deep understanding of the problem at hand, the performance of the algorithms we have for that problem, the features that make instances of the problem hard for these algorithms, and the parameter settings for which the algorithms perform the best. Benchmarking is the engine driving research in the fields of EAs for decades, while its potential has not been fully explored.

The goal of this special session is to solicit original works on the research in benchmarking: Works which contribute to the domain of benchmarking of discrete algorithms from the field of Evolutionary Computation, by adding new theoretical or practical knowledge. Papers which only apply benchmarking are not in the scope of the special session. This special session wants to bring together experts on benchmarking, evolutionary computation algorithms, and discrete optimization. Research on single objective optimization algorithms often forms the foundation for more complex scenarios, such as niching algorithms and both multi-objective and constrained optimization algorithms.

Traditionally, single objective benchmark problems are also the first test for new evolutionary and swarm algorithms. Additionally, single objective benchmark problems can be transformed into dynamic, niching composition, computationally expensive and many other classes of problems.

8th Workshop on Evolutionary Computation for the Automated Design of Algorithms

It is with the goal of better understanding the behavior of swarm and evolutionary algorithms as single objective optimizers that we are introducing the Digit Challenge. Specifically, the challenge was to solve 10 hard problems to 10 digits of accuracy. One point was awarded for each correct digit, making the maximum score , hence the name. Contestants were allowed to apply any method to any problem and take as long as needed to solve it. Out of the 94 teams that entered, 20 scored points and 5 others scored In a similar vein, we propose the Digit Challenge.

Another difference is that the score for a given function is the average number of correct digits in the best 25 out of 50 trials. All population-based methods are acceptable. CEC Special Session on big optimization. Theoretical studies that enhance our understandings on the behaviors of memetic computing. Adaptive systems and meme coordination. Novel manifestations of memes for problem-solving.

Cognitive, brain, individual learning, and social learning inspired memetic computation Self-design algorithms in memetic computing. Memetic frameworks using surrogate or approximation methods Memetic automaton, cognitive and brain inspired agent based memetic computing Data mining and knowledge learning in memetic computation paradigm Memetic computing for expensive and complex real-world problems Evolutionary multi-tasking. The topics covered include, but are not limited to, the use of EC for the following: Parameter control and tuning Architecture design, e.

Examples include but are not limited to: Adaptation in games Automatic game testing Coevolution in games Comparative studies e. The main topics of the special session are: Evolutionary algorithms for multimodal multiobjective optimization Hybrid algorithms for multimodal multiobjective optimization Adaptable algorithms for multimodal multiobjective optimization Surrogate techniques for multimodal multiobjective optimization Machine learning methods helping to solve multimodal multiobjective optimization problems Memetic computing for multimodal multiobjective optimization Niching techniques for multimodal multiobjective optimization Parallel computing for multimodal multiobjective optimization Design methods for multimodal multiobjective optimization test problems Decision making in multimodal multiobjective optimization Related theory analysis Applications.

The main topics of this special session include, but are not limited to, the following: Malware detection using neural networks and evolutionary computation Internet fraud detection and prediction using neural networks and evolutionary computation Intrusion detection using neural networks and evolutionary computation Digital rights management using neural networks and evolutionary computation Explicit content filtering using neural networks and evolutionary computation Application of convolutional neural networks for multimedia security Image and video forensics using convolutional neural networks Cybercrime risk due to neural networks e.

Kennedy, and Manoranjan Mohanty Scope and Topics Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. The main topics of this special session include, but are not limited to, the following: Information fusion and knowledge transfer in biomedical and healthcare applications. Data Analysis of the biomedical data including genomics.

Text mining for medical reports. Statistical analysis and characterization of biomedical data. Information Retrieval of Medical Images Single cell sequencing analysis Medical imaging and genomics. The topics of this special session include but are not limited to the following topics: Evolutionary computation in resource allocation for hospital location planning, aeromedical retrieval system planning, etc.

Application of evolutionary computation for job scheduling, such as ambulance scheduling, nurse scheduling, job scheduling in medical device and pharmaceutical manufacturing, etc. Multiple-criteria decision-making for computer-aided diagnosis using expert systems. Web self-diagnostic system with the application of information retrieval and recommendation system.

Data-driven surrogate-assisted evolutionary algorithms in pharmaceutical manufacturing processes. Modeling and prediction in epidemic surveillance system for disease prevention. Route planning for disability robots. Topics of interest include, but not limited to: Evolutionary Web service composition Evolutionary Web service workflow optimisation Evolutionary Web service selection Evolutionary Web service location allocation Evolutionary Web service scheduling Evolutionary semantic Web service composition Evolutionary dynamic Web service composition Multi-objective Web service composition Evolutionary computation for resource allocation in Cloud computing Evolutionary computation for workflow management in Cloud Evolutionary computation for distributed Web service composition Novel representations and search operators for Service-oriented computing Cooperative coevolution for Service-oriented computing Evolutionary computation for Big Data As A Service Evolutionary computation for Internet of Things Services Hybrid algorithms between EC techniques and other CI and learning techniques such as neural networks and fuzzy systems for service and cloud computing.

This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters. Buy eBook. Buy Hardcover. Buy Softcover. FAQ Policy. About this book Data mining is a very active research area with many successful real-world app- cations. Show all.

From the reviews: "The book is targeted at researchers and postgraduate students. Gisele L. Data Mining Pages Pappa, Dr. Evolutionary Algorithms Pages Pappa, Dr. Show next xx. Recommended for you. Pappa Alex A.