Real-world Application of Bio-inspired Metaheuristics [details]

Organized and chaired by Dr. Andrew Lewis and Dr. Marcus Randall.

This special session will seek to present a selection of contributed papers describing the application of bio-­‐inspired metaheuristics to real problems of industrial and scientific research and development. In particular, applications that illustrate the means taken for practical application to challenging problems are of particular interest. It is planned that the session will allow not only presentation of these papers but also extended time for their discussion to promote interchange of innovative ideas between participants.
Evolutionary Multi-Objective Optimization [details]

Organized and chaired by Prof. Dr. Günter Rudolph and Dr. Heike Trautmann.

 

 In many real-world applications one is faced with the problem that several objective functions have to be optimized simultaneously leading to a multi-objective optimization problem (MOP). In the recent past, bio-inspired evolutionary methods specialized for generating trade-off solutions of MOPs -- Evolutionary Multiobjective Algorithms (EMOAs) -- have caught the interest of many researchers and have become an important and very active research field. Reasons for this include that these randomized set oriented methods are applicable to a wide range of MOPs including black-box optimization tasks, while in particular no differentiability assumptions are required and problem characteristics such as nonlinearity, multimodality or stochasticity can be handled as well. Furthermore, EMOA are capable of delivering a finite size approximation of the solution set (the so-called Pareto set) in one run of the algorithm.
Probabilistic modeling and optimization for emerging networks [details]

Organized and chaired by Dr. Jianguo Ding and Dr. Xinhui Wang.

Recent years, with the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, clouding computing, or mobile Internet, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus uncertainty is an unavoidable characteristic, which comes from unexpected hardware defects, unavoidable software errors, incomplete management information and dependency relationship between the entities among the complex networks. Due to the complexity of emerging networks, it is not always possible to build precise models in modeling and optimization (local and global) for networks. This session will provide a forum to researchers to propose theories and technologies on the probabilistic models and optimization strategies in the areas of network performance, network management, network security, etc. for emerging networks.
Dynamic Optimization [details]

Organized and chaired by Dr. Emilia Tantar and Dr. Alexandru-Adrian Tantar and Dr. Peter Bosman.

This session, as a first aim, considers providing a thorough understanding of what dynamic problems stand for. At the same time, the session aims at offering an overview of the existing paradigms and their adaptation for real-life dynamic problems. A particular interest, besides tracking the optimum (or set of best compromise solutions in multi-objective), is taken for learning, anticipation and resilience to extreme noise or significant severity of change. A need for ways of measuring the quality of a solution when dealing with dynamic problems (performance measures and comparison) is also of a special interest.

 

 

Genetic Programming [details]

Organized and chaired by Dr. Leonardo Trujillo and Dr. Edgar Galvan.

The field of Genetic Programming (GP), studies the development of evolutionary algorithms that can synthesize computer programs that can automatically solve a specific computational problem. Recent works have illustrated the power and flexibility of GP with the development of successful applications in diverse domains. However, important open questions still need to be addressed in order to overcome some of the limitations of current GP-based methods, which can only be achieved with a deeper understanding of the fundamental dynamics of a GP search. In this special session, we invite research contributions that study theoretical aspects of GP search and innovative applications.

 

 

Hybrid Probabilistic Models for Real Parameter Optimization and their Applications [details]

Organized and chaired by Dr. Arturo Hernández-Aguirre.

The goal of this special session is to present strategies for real parameter optimization based on probabilistic models with real parameters. The growing interest in such models is supported by a well established probability theory, however, for practical purposes hybridizations with evolutionary algorithms and/or numeric algorithms have resulted in faster and more robust algorithms. This session means a meeting point for the presentation and the discussion of algorithms based on three paradigms wisely integrated: probability, evolutionary, and numerical.

 

 

Evolutionary Computation for Vision, Graphics, and Robotics [details]

Organized and chaired by Prof. Gustavo Olague and Prof. Humberto Sossa.

The aim of this special session is to provide with a forum where professionals in the areas of computer vision, computer graphics, and robotics whose interest on evolutionary algorithms could be reflected in a lively open environment. Nowadays, evolutionary algorithms are increasingly been applied to solve problems in the above mention domains of research. We strive to provide with a channel where the maturity of the research area is reflected on high quality research work.