Experimental Research in Evolutionary Computation


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Introduction

Given that the study of multimodal multiobjective optimization MMO is still in its emerging stages, although many real-word applications are likely to be amenable to treatment as a MMOP, to date the researchers have ignored such formulations. This special session is devoted to the novel approaches, algorithms and techniques for solving MMOPs.

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The main topics of the special session are:. The Brain Storm Optimization BSO algorithm is a new kind of swarm intelligence algorithm, which is based on the collective behavior of human being, that is, the brainstorming process. There are two major operations involved in BSO, i. The designed optimization algorithm will naturally have the capability of both convergence and divergence.

The BSO algorithm can be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone. This special session aims at presenting the latest developments of BSO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence.

Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special session. Potential topics include, but are not limited to:. The volume of cybercrime is increasing daily with increasing use of the internet for email and social media purposes.

The use of neural networks for tackling cybercrime is an active area of research. For example, conventional neural network-based solutions have been proposed to detect image tampering, source camera attribution of an anonymous crime image, explicit content detection, virus detection, etc.

The New Experimentalism

Recently, researchers are focusing more on unsupervised solutions. New types of cybercrimes are also emerging e.

Evolutionary Computation in Combinatorial Optimization

Interestingly, the role of evolutionary computing in tackling cybercrime is relatively underexplored. This special session aims to bring together researchers from both academia and industry in the application of evolutionary computation and neural networks for combating cybercrime. This session also will welcome research which focuses on the risk of a neural network for spreading new kind of cybercrimes e. The session will attract researchers working in cybersecurity, evolutionary computation, and neural networks.

Top Authors

Of particular interest will be research that combines evolutionary computing with neural network approaches. The main topics of this special session include, but are not limited to, the following:. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. There is also a need to explore big data in biomedical and healthcare research.

Experimental evolution

An increasing flood of data characterises human health care and biomedical research. Healthcare data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. An interesting aspect is to integrate different data sources in the data analysis process which requires exploiting the existing domain knowledge from available sources.

This special session aims to bring together the current research progress from both academia and industry on data analysis for biomedical and healthcare applications. It will attract healthcare practitioners who have access to interesting sources of data but lack the expertise in using the data mining effectively.

Special attention will be devoted to handle feature selection, class imbalance, and data fusion in biomedical and healthcare applications. With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. Pigeon Inspired Optimization PIO is a recently developed intelligent bio-inspired algorithm and become popular to deal with a variety of optimization problems.

It is capable of addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. This special session aims at presenting the latest developments of PIO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special Session.

Review ARTICLE

This special session focuses on both practical and theoretical aspects of Evolutionary Scheduling and Combinatorial Optimization. Examples of evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimization, particle swarm optimization, evolutionary based hyper-heuristics, memetic algorithms. Novel hybrid approaches that combine machine learning and evolutionary computation to solve difficult ESCO problems are highly encouraged. Examples include using machine learning to improve surrogate-assisted evolutionary algorithms, and designing evolutionary algorithms for reinforcement learning and transfer learning.

We welcome the submissions of quality papers that effectively use the power of EC techniques to solve hard and practical scheduling and combinatorial optimization problems. Papers with rigorous analyses of EC techniques and innovative solutions to handle challenging issues in scheduling and combinatorial optimisation problems are also highly encouraged.

Topics of interest include, but not limited to:.

Experimental Research in Evolutionary Computation The New Experimentalism Natural Computing Series

The field of evolutionary multi-objective optimization has developed rapidly over the last 20 years, but the design of effective algorithms for addressing problems with more than three objectives called many-objective optimization problems, MaOPs remains a great challenge. First, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimization, results in the underperformance of traditional Pareto-based algorithms.

Also, the aggravation of the conflict between convergence and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimization algorithms.


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Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics and test functions tailored for experimental and comparative studies of evolutionary many-objective optimization EMaO algorithms.

List of topics, but are not limited to:. Worldwide, the healthcare industry would continue to thrive and grow, because diagnosis, treatment, disease prevention, medicine, and service affect the mortal rates and life quality of human beings. Two key issues of the modern healthcare industry are improving healthcare quality as well as reducing economic and human costs. The problems in the healthcare industry can be formulated as scheduling, planning, predicting, and optimization problems, where evolutionary computation methods can play an important role. Although evolutionary computation has been applied to scheduling and planning for trauma system and pharmaceutical manufacturing, other problems in the healthcare industry like decision making in computer-aided diagnosis and predicting for disease prevention have not properly formulated for evolutionary computation techniques, and many evolutionary computation techniques are not well-known to the healthcare community.

This special session aims to promote the research on evolutionary computation methods for their application to the healthcare industry. The topics of this special session include but are not limited to the following topics:. Services computing is becoming more and more prominent in the Internet environment with the rapid growth of services available on the internet.

Cloud computing has become a scalable services consumption and delivery platform in the field of Services Computing. This raises issues for service providers such as Web service composition and service location allocation, resource allocation and scheduling, etc.

Computational Intelligence CI has been successfully applied to many challenging real-world problems. The scope of this special session includes both new theories and methods on how to solve the challenging services computing and cloud computing problems. It is consequently a broader, more flexible and more capable paradigm than GBML. From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects.

For this purpose, this special session focuses on, but is not limited to, the following areas in EML:. Many of the tasks carried out in data mining and machine learning, such as feature subset selection, associate rule mining, model building, etc. Thus it is very natural that Evolutionary Computation EC , has been widely applied to these tasks in the fields of data mining DM and machine learning ML , as an optimization technique. On the other hand, EC is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process.

Therefore, the data mining and machine learning techniques can also be used to analyze these data for improving the performance of EC. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice. We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:.

To shape a low carbon energy future has been a crucial and urgent task under Paris Global Agreement.


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Numerous optimisation problems have been formulated and solved to effectively save the fossil fuel cost and relief energy waste from power system and energy application side. However, some key problems are of strong non-convex, non-smooth or mixed integer characteristics, leading to significant challenging issues for system operators and energy users. Evolutionary computation is immune from complex problem modeling formulation, and is therefore promising to provide powerful optimisation tools for intelligently and efficiently solving problems such as smart grid and various energy systems scheduling to reduce carbon consumptions.

This special session intends to reflect the state-of-the-art advances of evolutionary optimisation approaches for solving emerging problems in complex modern power and energy system. The submissions are encouraged to be focus on smart grid scheduling with integration of new participants such as renewable generations, plug-in electric vehicles, distribution generations and energy storages, multiple time-spacial energy reductions and other energy optimisation topics. Potential submission topics include:.

In machine learning and data mining, the quality of the input data, i. Feature selection, feature extraction or construction and dimensionality reduction are important and necessary data pre-processing techniques to increase the quality of the feature space. However, they are challenging tasks due to the large search space and feature interactions. This special session aims to use Evolutionary Computation for feature reduction, covering ALL different evolutionary computation paradigms.

Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:. Transfer learning aims to transfer knowledge acquired in one problem domain, i. Transfer learning has recently emerged as a popular learning framework in data mining and machine learning.

Experimental Research in Evolutionary Computation Experimental Research in Evolutionary Computation
Experimental Research in Evolutionary Computation Experimental Research in Evolutionary Computation
Experimental Research in Evolutionary Computation Experimental Research in Evolutionary Computation
Experimental Research in Evolutionary Computation Experimental Research in Evolutionary Computation
Experimental Research in Evolutionary Computation Experimental Research in Evolutionary Computation
Experimental Research in Evolutionary Computation Experimental Research in Evolutionary Computation

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