Uncertainty is present in industrial sectors such as transportation and production logistics, supply chain management, computer and telecommunication networks, or economics and finance. Thus, in order to cope with their stochastic components, simulation methods and techniques are frequently employed in the analysis of complex systems related to these sectors. However, simulation is not an optimization tool, so it needs to be combined with optimization methods whenever the goal is to maximize the system performance or to minimize the associated costs. A large number of these real-life optimization problems are NP-hard and large scale in nature, which makes it necessary the use of metaheuristic approaches to solve them in an efficient way. Simheuristic algorithms combine simulation methods with metaheuristics to efficiently deal with stochastic optimization problems. This session aims at presenting recent applications of simheuristic algorithms to the fields of logistics and transportation.

A lot of real-world optimization problems consist of maximizing or minimizing various objective functions which are often in conflict with each other. These functions are often complex and can be computationally costly to evaluate. Multi-objective optimization is the discipline that tries to find the optimal or efficient solutions, called Pareto, to these types of problems. Currently, multi-objective optimization using metaheuristics is an open and fruitful line of research, in which there are many aspects to study, both methodological and applied. This session aims to be a meeting point for researchers interested in this discipline.

Many practical search and optimization problems, especially those arising from real-life scenarios, are becoming increasingly large and complex. One way to find precise solutions to these problems in a timely manner is to use parallel techniques. Thus, the current accessibility of parallel architectures (multiprocessors, multicores, GPUs, etc.) raises new algorithmic design opportunities in the area and leads to highly efficient optimization software.

The aim of this special session at MAEB 2018 is to establish a meeting point for researchers working on the development and application of parallel methods. Contributions on new theoretical developments as well as practical applications of such algorithms are of interest. Topics of this special session include Parallel Evolutionary Algorithms, Parallel Metaheuristics and Master/Slave Models among others.

The process of transforming goods and services into more useful products is called Production. A productive system is a set of interrelated elements for the purpose of performing the function of producing. Production management is responsible for designing, coordinating, administering, governing and controlling all the operations, both productive and logistical or assistants, that are present in both the design and operation of production and logistics systems. Operations management, with its various approaches (material flow, process activities and capacity of resources), contributes to achieving the objectives set for the productive-logistic system in the following areas the most efficient way possible of consuming resources, by adopting the most efficient policies, strategies, decisions and timely actions. These objectives focus on aspects of quantity, quality, time and cost.

In this context, numerous complex resolution optimization problems arise due to (1) the dimensions of these problems when trying to address them in a comprehensive manner industrial and realistic environments, (2) the combinatory nature of many of them, and (3) the nature of of the objective which seeks to respond, adequately and at the same time, to criteria linked to efficiency of the system, its operating and distribution costs, and the times of receipt, execution and delivery of materials, services and products.
In the last decades, many problems related to Production and Logistics in the environment have been successfully treated with resolution techniques based on Metaheuristics (MHs), in general, and in Evolutionary and Bioinspired Algorithms (AEBs), in particular. These techniques, without other such as Dynamic Programming and Mathematical Programming, constitute an important part of the powerful and extensive toolbox to deal with optimization problems very adequately in the framework of the productive and logistic operations of the companies.

The session we propose is dedicated to the application of MHs and AEBs to the problems of optimisation of the design and management of production and logistics systems. It includes the following topics: Design of the productive system, Reliability, renovation and maintenance of productive-logistic systems, Management of stocks of component materials and products or Planning of operations.

Applications in medicine have been, for years, a source of motivación for researchers at Metaheuristics, Evolutionary and Bioinspired Algorithms (MAEB) and currently represent one of the áreas with the greatest presence in many areas of research. This special session focuses on the application of the MAEBs to the resolution of problems related to medicine and healthcare. In particular, Particularly interesting contributions can be found at the following topics (although they are not limited to):

• Modeling and simulation of medical processes
• Diagnosis and clinical therapy
• Medical images
• Signal processing in medicine
• Analysis of medical texts
• Data mining and medical records
• Clinical expert systems
• Descriptive analysis of pharmaceuticals
• Clinical studies based on genomics
• Patient-Centered Care
• Optimization of biometric systems
• Analysis of data collected with biometric systems

Scatter Search is a metaheuristic method to solve optimization problems. Although it was originally introduced in the 1970s, it has recently been tested on numerous difficult problems with great success. It belongs to the family of the so-called Evolutionary Algorithms, which are distinguished by being based on the combination of a set of solutions. Although it has similarities with Genetic Algorithms, it differs from them in fundamental principles, such as the use of systematic rather than random strategies. Scatter Search provides a flexible framework that enables the development of different implementations with varying degrees of complexity.

Path Relinking, usually referred as path re-chaining, is a methodology based on applying a local search algorithm to a given solution, called an initial solution, with the purpose of approaching another given solution, called a guide solution. This method has been applied in combination with other methodologies, such as Scatter Search, Tabu Search or GRASP.

In this session we accept works that apply Scatter Search or Path Relinking to the resolution of any type of problem or application considered difficult to solve.

Bioinformatics is today a broad area of research, development and application of computational tools to analyze the use of biological, medical or health data, including methods to acquire, store, organize, archive, analyze or visualize such data. Many aspects of current bioinformatics research are being developed by researchers in the field of evolutionary computing who use different bio-inspired computing algorithms in their search for solutions to different problems. The session will provide a meeting point for participants and researchers in evolutionary computing and, in general, bio-inspired methods who have a current or developing interest in bioinformatics problems.

Relevant areas include (but are not limited to) the following, where bio-inspired methods are used:

• Analysis of genomic sequences. Microarray data analysis.
• Protein sequence analysis: measures of sequence similarity, alignments, motifs, etc.
• Modeling and analysis of genetic regulation networks.
• Prediction of the structure (secondary and tertiary) of proteins or other macromolecules.
• Modeling of protein folding.
• Use of Artificial Life schemes such as Cellular Automata or Lindenmayer Systems in the modeling of biological processes.
• Hybrid approaches between evolutionary and heuristic algorithms (Baldwinian strategies, Lamarckian strategies, memetic algorithms,...) in bioinformatics applications.
• Multi-target approaches in bioinformatics applications.
• Genomics and proteomics data management and visualization strategies.
• Modeling of multicellular systems: development processes, tumor growth modeling, etc.

Global urbanization is continually reshaping our world. More than half of the world's population is currently living in urban areas and 75% of humanity is expected to live in cities by 2050. This transformation offers great opportunities for cultural and economic growth. However, it also brings with it a number of challenging problems such as overcrowding in metropolitan areas, the cost of living, environmental pollution and inadequate infrastructure, among others.

Smart cities seek to provide solutions to ever-growing needs by integrating information technologies and interconnected devices into urban environments. This allows for the collection and interpretation of large amounts of data, which are used to optimize various aspects of the operation of cities through the design and modeling of ad hoc solutions and systems. Intelligent transport systems, intelligent buildings, intelligent communications and energy networks are some of the most active research areas in this domain.

Computational Intelligence (CI) has played an important role in most of the complex systems in existence so far, and is also expected to play a leading role in intelligent cities. We talk about methodologies such as automatic learning, data science, artificial neural networks, evolutionary algorithms, swarm intelligence and fuzzy logic. They all offer computationally powerful techniques for modeling, analyzing and optimizing intelligent city systems. In fact, computational intelligence is an important way of building the "intelligent" part of the city. The interaction of such approaches with operations research and many other domains (civil engineering, urban planning, managers, companies...) can offer innovative and sustainable solutions to highly complex problems, such as those often posed by a modern city.

This special session will accept papers on any aspect of computational intelligence in intelligent city environments, both theoretical and applied:

• Computational intelligence in intelligent transport and logistics
• Computational intelligence in mobility and urban planning
• Computational intelligence in intelligent energy systems
• Computational intelligence on sustainability (environmental, social, economic)
• Computer intelligence in smart homes and the Internet of things
• Computational intelligence in intelligent health systems
• Computational intelligence for governance
• Computational intelligence for people and quality of life
• Computer intelligence for tourism and entertainment in the city
• Computational intelligence in circular economics
• Cyber-physical systems and the Internet of things together with IC
• IC for security, big data, open data and city software

Applications with a holistic view of the city, involving efficient learning methodologies and optimization for these types of problems, are especially welcome.