The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems

10-12th November 2014, Singapore

List of IES 2014 Invited Sessions

SS1 - Intelligent and Expert Systems with Industrial Applications

Organizer:  Dr. Justin Pang Chee Khiang, National University of Singapore, Singapore

              Dr. Jun-Hong Zhou, Singapore Institute of Manufacturing Technology, A*STAR, Singapore

In an era of intensive competition, the new challenges faced industrial processes include maximizing productivity, ensuring high product quality, and reducing the production time while minimizing the production cost simultaneously.  As such, it is of paramount importance to design intelligent and expert systems to possess the knowledge base of human expertise for problem solving, robustifying, and clarifying uncertainties.  An intelligent systems approach synthesizes the tools and methods of control theory, operations research, and Artificial Intelligence (AI) to support the modeling, analysis, and design of systems.  This holistic and synergistic methodology also provides the essential capabilities to cope with the increasing complexities of the systems that we work and live in, including manufacturing, service, and socio-economic systems, etc.

​SS2 - Intelligent and Evolutionary Systems for Natural Language Processing

Organizers:  Dr. Erik Cambria, Nanyang Technological University, Singapore

                        Dr. Amir Hussain, University of Stirling, UK 

                        Dr. Yunqing Xia, Tsinghua University, China

​Proposal: (Click here)

As the Web rapidly evolves, Web users and Web contents are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s Web are perfectly suitable for human consumption,but remain hardly accessible to machines.

To this end, biologically and linguistically motivated computational paradigms that go beyond syntax are needed. Intelligent and evolutionary systems potentially has a large future possibility to play an important role in natural language processing (NLP) research for tasks such as grammatical evolution, knowledge discovery, and rule learning. In this light, this Special Session focuses on the introduction, presentation, and discussion of novel NLP systems that are not merely based on domain-dependent corpora or word co-occurrence counts, but rather systems that can be considered intelligent and evolutionary. The main motivation for the Special Session, in particular, is to go beyond a mere word-level analysis of text and provide novel concept-level approaches to natural language processing that allow a more efficient passage from (unstructured) textual information to (structured) machine-process able data, in potentially any domain.

​SS3 -Evolutionary algorithm and Machine Learning in Remote Sensing Image Processing

Organizers: Dr. Yanfei Zhong, Wuhan University, China

​                       Dr. Bo Du, Wuhan University, China

Proposal: (Click here)

Nowadays, more and more satellites with remote sensing sensors are launched into the outer space, by which remote sensing images with different characteristics can be acquired. For example, hyperspectral remote sensing images with many bands have high spectral resolution. However, it results in the high dimensionality. Another example is high resolution remote sensing image, which has high spatial resolution. It can cause the problem that different objects may have the same spectral characteristics and the same objects may have different spectral characteristics. Some object-based or scene-based techniques are attempted to processing very high resolution remote sensing images. Also, there are some specific characteristics in other types of remote sensing images such as SAR and LIDAR. Due to the diversity of their characteristics, it has been a big challenge to extract useful information from these images.

​SS4 -Evolutionary Multi-objective Optimization: Methods and Engineering Application

Organizers: Dr. Hailin Liu, Guangdong University of Technology, China

​                       Dr.Yuping Wang, Xidian University, China

                       Dr. Zhun Fan, Shantou University, China

Proposal: (Click here)

Many problems from science and engineering involve several conflicting objectives that have to be optimized simultaneously. A number of evolutionary multi-objective optimization (EMO) algorithms have already been proposed to solve such multi-objective optimization problems. This is motivated mainly because the population-based nature of EAs enables them to get a set of Pareto-optimal solutions in a single run. 

Currently, researchers in the EMO community are working towards finding out effective and efficient approaches to handle many-objective problems with more than two or three conflicting objectives to be optimized simultaneously. Also, there is an increasing interest in using evolutionary algorithms to various engineering problems. These engineering applications, which often have many objectives, provide problems of various types of difficulties for the design and testing of evolutionary algorithms.

​SS5 -Evolutionary Computation and Data Mining 

Organizers: Dr. Chuan-Kang Ting, National Chung Cheng University, Taiwan

​                       Dr.Ying-Ping Chen, National Chiao Tung University, Taiwan

                       Dr. Jing Liang, Zhengzhou University, China

Proposal: (Click here)

Evolutionary computation has been successfully applied to many aspects of data mining. For example, as reported in the literature, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and several evolutionary algorithms have been adopted to handle data clustering and classification. On the other hand, advances in data mining, an important section in data engineering and automated learning, also assist optimization algorithm designers to develop better methods. For instance, Apriori algorithm has been utilized for finding the relationship among decision variables for optimizers. In order to bridge the concepts and methodologies from the two ends, this invited session concentrates

on the related topics of integrating and utilizing algorithms in evolutionary computation and data mining. It provides the opportunity for practitioners handling their data mining issues by using evolutionary computation methodologies and for researchers investigating evolutionary computation with data mining approaches to share findings and look into future directions.

​SS6 -Collaborative Learning and Optimization 

Organizers: Dr. Ke Tang, University of Science and Technology of China, China

​                       Dr. Kai Qin, RMIT University, Australia

                       Dr. Aimin Zhou, East China Normal University, China

Proposal: (Click here)

Learning and optimization are two essential tasks that computational intelligence aims to address. Numerous techniques have been developed for these two purposes separately. In fact, learning and optimization are closely related. On the one hand,learning can be formulated as a model-centric or data-centric optimization problem,and accordingly solved by optimization techniques. On the other hand, optimization can be regarded as an adaptive learning process, and thus tackled by learning methodologies. Recent years have seen remarkable attempts at collaborative learning and optimization. For instance, learning classifier systems, evolutionary neural networks, evolutionary ensemble learning, evolutionary kernel machines,evolutionary clustering, evolutionary data generation and extraction for learning,supervised classifiers’ domain of competence analysis using evolutionary multi-objective optimization, theoretical analysis of optimization techniques using learning theory, optimization by building and using probabilistic models,self-adaptive and tuning-free optimization, ensemble optimization, statistical analysis of evolutionary computation, automatic heuristic design, etc. These research efforts have leaded to a great deal of cutting-edge techniques in the corresponding research fields. Nowadays, the emergence of more and more complex problems in real-world applications insistently calls for in-depth investigations of synergy between learning and optimization. Moreover, feasibility of implementing collaborative learning and optimization techniques on massive parallel systems must be seriously taken into account to ensure that large-scale problems can be solved in a reasonable time. This symposium aims at providing a forum for academic and industrial researchers from both learning and optimization communities to review the past effort, to report the latest progress, and to explore the future direction of synergy between techniques from these two areas.

SS7 -Nature Inspired Algorithm for Real World Problems 

Organizers: Dr. Nasser R. Sabar, The University of Nottingham Malaysia Campus

​                       Dr. Siang Yew Chong, The University of Nottingham Malaysia Campus

Many real world optimization problems are complex and very difficult to solve and they present a great challenge for the research communities. This is due to large and heavily constrained search spaces which

make their modeling (let alone solving) a very complex task. Although they have received a significant amount of attention from various research communities, there is still a quest for an efficient and effective algorithm that is able to produce very good results within acceptable amount of time and can adapt itself
to the solution landscape changes. Nature inspired algorithms, such as particle swarm optimization, ant colony optimization, harmony search and artificial bee colony, have proven to be effective solution methods for various optimization problems. The objective of the session is to bring researchers on nature
inspired algorithms to develop an efficient and effective algorithm that is able to support the decision maker and can produce very good results for a real word problem such as airport optimization, cutting and packing, educational timetabling, healthcare applications, network routing, personnel scheduling,
portfolio optimization, production scheduling, transportation and logistics optimization, feature selection, clustering and dynamic optimization problem. 

​SS8 - Swarm Intelligence in Transportation

Organizers:  Dr. Anand Jayant Kulkarni, University of Windsor, Canada

​                       Dr. Kang Tai, Nanyang Technological University, Singapore

                       Dr. Jeevan Kanesan, University Malaya, Malaya

                       Dr. Saeid Kazemzadeh Azad, Middle East Technical University, Turkey

Proposal: (Click here)

The transportation industry no longer serves only a few big long-term customers whose shipments are transported to fixed destinations over a relatively stable schedule. The complexty has significantly increased due to considerable increase in ad hoc shippers with comparatively smaller shipments destined to many different locations. The efficient and cost effective transportation has become a key issue for the carriers especially when dealing with the combination of goods of different types. Planning for optimal routes, modes, packing, etc. are the example key issues. As the problem size is increasing along with the complexity of the problem domain, the nature inspired optimization techniques in the Swarm Intelligence domain are becoming more relevant and needs to be tested for effective problem solving in the transportation. We welcome relevant papers on Swarm Intelligence, including (but not limited to) the topics of Self Organizing Systems, Cohort Intelligence, Particle Swarm Optimization, Ant Colony Systems, Agent Simulations, Collective Intelligence, Distributed Optimization, Decentralized Systems, Swarm Robotics, Artificial Bee Colony Systems, Bio/Nature Inspired Approaches, Reinforcement Learning, Computational Learning Theory, etc. With a successful session we expect approximately 8 to 10 papers.

​SS9 - Evolutionary Scheduling

Organizers:  Dr. Su Nguyen, Victoria University of Wellington, New Zealand

​                       Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand

                       Dr. Kay Chen Tan, National University of Singapore, Singapore

Scheduling problems have arisen from a large number of real-world applications and have become an important research area in Artificial Intelligence and Operations Research. Solving scheduling problems are generally challenging due to complex constraints, large-scale, dynamic and uncertainty issues. Evolutionary scheduling aims to employ evolutionary computational techniques to handle scheduling problems. Evolutionary techniques are suitable for these problems since they are highly flexible in terms of handling constraints, dynamic changes and multiple conflicting objectives. 

​SS10 - Complex Networks and Evolutionary Computation

Organizers:  Dr. Jiang Liu, Xidian University, China


Proposal: (Click here)

The application of complex networks to evolutionary computation (EC) has received considerable attention from the EC community in recent years. The most well-known study should be the attempt of using complex networks, such as small-world networks and scale-free networks, as the potential population structures in evolutionary algorithms (EAs). Structured populations have been proposed to as a means for improving the search properties because several researchers have suggested that EAs populations might have structures endowed with spatial features, like many natural populations. Moreover, empirical results suggest that using structured populations is often beneficial owing to better diversity maintenance, formation of niches, and lower selection pressures in the population favouring the slow spreading of solutions and relieving premature convergence and stagnation. Moreover, the study of using complex networks to analyse fitness landscapes and designing predictive problem difficulty measures is also attracting increasing attentions. On the other hand, using EAs to solve problems related to complex networks, such as community detection, is also a popular topic.

​SS11 - Evolutionary Computation for Constrained Optimization: Methods and  Real-world                          Application

Organizers:  Dr. Xinye Cai, Nanjing University of Aeronautics and Astronautics, China

                Dr. Zhun Fan, Shantou University, China

                        Dr. Jing Liang, Zhengzhou University, China

Proposal: (Click here)

Most real-world optimization problems are usually subject to various types of constraints and how to tackle these constrained optimization problems (COPs) has been extensively raised concerns.Evolutionary Computation, such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO) , Ant Colony Optimization(ACO), Differential Evolution(DE), and the like, has been reported in successfully solving various constrained optimization problems, yet, there remain many open issues and opportunities that are continually emerging as intriguing challenges for the field.  This special session aims to bring together advancing theories, technologies and real-world applications in evolutionary computation for constrained optimization.

​SS12 - Differential Evolution and Its Applications

Organizers:  Dr. Wenyin Gong, China University of Geoscicences, China

                Dr. Yong Wang, Central South University, China

                        Dr. Xinchao Zhao, Beijing University of Posts and Telecommunications, China

Proposal: (Click here)

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimizer more than a decade ago and has now developed into one of the most promising research areas in the field of evolutionary computation. This method has recently been shown to produce superior results in a wide variety of real-world applications. Nonetheless, 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.

​SS13 - Computational and Data-Centric Economics and Management Sciences

Organizers:  Dr. Aki-Hiro Sato, Kyoto University, Japan

                Dr. Takao Terano, Tokyo Institute of Technology, Japan

Proposal: (Click here)

The information generated in the society is doubling every two years. In 2011 the world will create a staggering 1.8ZB according to IDC. It is estimated that the digital data generated in our society will reach 50 times the amount of information and 75 times the number of "information containers" by 2020. Therefore, it is worth establishing methodologies of computational and data-centric approaches in this era. Sufficient amounts of data should tell us something interesting or something new. However, since human ability is finite, data generated as digital data is not used sufficiently. The cyber-enabled approach is a promising to overcome the limitation of human ability. The computational and data-centric strategy requires computation for rich data. This approach is classified into a cyber-enabled scientific methodology differing from human-centric scientific methodology that is studied in theoretical and experimental sciences. To do so, data should be digitalized, validated and stored as computer-readable formats and computing systems are needed. Recent development and spread of information and communication technology, we can seek to understand our economic activities from rich data on our society by applying empirical investigations based on substantial amounts of data and computation.

​SS14 - Multiagent System for Industrial Applications

Organizers:  Dr. Partha Dutta, Rolls-Royce Singapore Pte Ltd, Singapore

                Dr. Jie Zhang, Nanyang Technological University, Singapore

Over the past 10 years or so, research in the area of intelligent agents has been active. As a result, plenty of multi-agent systems have been developed, with the aims of, for example, supporting individuals’ decision making in complex situations, coordinating team work, optimizing overall performance of complex systems/problems, simulating complex dynamical processes, etc. However, many of those research studies often make simplified assumptions about the problem formulation and about the data used to test and validate the models, for the purpose of reaching mathematically sound proofs or feasible solutions. While this generates stimulating scientific debate about the theoretical properties of the models, the opportunity for delivering an immediate benefit to industry and, to a greater extent, to society as a whole, by developing practical deployable systems can be overlooked. Against this background, the goal of this invited session is to bring researchers from both the research communities and industries and to provide a platform for them to communicate and discuss the gap between developed multiagent systems and industrial needs. And, by doing so, we hope to unravel new challenges and therefore foster further research and development required for making multi-agent systems increasingly more applicable for solving the most challenging industrial problems.

​SS15 - Subspace Learning and Neural Networks

Organizers:  Dr. Jian Cheng Lv, Sichuan University, China

                Dr. Hong Qu, University of Electronic Science and Technology of China, China


A subspace learning algorithm learns a low dimensional subspace from the original high dimensional feature space, wherein specific statistical properties can be well preserved. Subspace learning has provided valuable tools for understanding and capturing the intrinsic non-linear structure of visual data encountered in many important problems, such as face identification and authentication, human gait analysis, video tracking, document classification, scene understanding, object categorization, and multimedia information retrieval.


Neural networks are computational models inspired by biological neural networks. The networks have been used to solve a wide variety of tasks. Recently, using neural networks as subspace learning algorithms to learn the low dimensional subspace has achieved promising performance and attracted increasing interests. The session intends to bring researchers in studying neural networks for subspace learning to report new results in this area. This session will also provide a good platform for the researchers and students to discuss the future research direction and potential challenges.  

​SS16 - Intelligent and Evolutionary Systems for Mobile and Entertainment Computing

Organizers:  Dr. Kyung-Joong Kim, Sejong University, Korea

                Dr. Sung-Bae Cho, Yonsei University, Korea

Proposal: (Click here)

Recently, computing platforms have been replaced from stationary desktops to mobile devices (smart phones, tablets, wearable watch, glasses and so on). They promote the development of new intelligent and evolutionary systems to extract full potential from them. They could record huge amount of data on user's daily life from multimodal embedded sensors. It also allows to discover user's intention, behavior, and habits from tons of mobile data. One of the potential applications of the life data analysis is adaptive entertainment for users. Based on the user's life patterns, the entertainment applications could adapt to users. They could enable interactive entertainment systems for games, movies, arts, music, robots and so on. In this session, we attempt to see the recent development in mobile and entertainment computing independently while discussing on the future applications from their hybridization.

​SS17 - Intelligent Information Processing with Fuzzy Logic: Theories and  Applications

Organizers:  Dr. Laszlo T. Koczy,Szechenyi Istvan University,  and Budapest University of Technology  and Economics, Hungary

                        Dr. Kai Meng Tay, Universiti Malaysia Sarawak, Malaysia

                        ​Dr. ​Chee Peng Lim, Deakin University, Australia

Proposal: (Click here)

In general, intelligent information processing attempts to utilize advanced knowledge processing methodologies for extracting and elucidating useful information from various sources, which include data, text, as well as audio and visual signals.  In this context, fuzzy logic provides a useful means to design and develop effective models and tools for intelligent information processing.  Introduced by Prof. L.A Zadeh in 1965, fuzzy logic constitutes a form of multi-valued logic, and is capable of dealing with the concept of partial truth, imprecision, and vagueness.  It has become a popular and important methodology which is able to exploit the approximate reasoning capability of the human mind and the underlying information from human linguistic variables in undertaking various challenging problems.  

​SS18 - Collaborative Supply Chain and Logistics Management by Complex Systems Approach

Organizers:  Dr. Li Zhengping, Singapore Institute of Manufacturing Technology, A*STAR, Singapore 

                Dr. Zhang Nengsheng, Singapore Institute of Manufacturing Technology, A*STAR, Singapore

Proposal: (Click here)

Supply chain and logistics systems in the current age are complex networks as the result of globalization, outsourcing and lean initiatives. The increasing interests in complex systems are driven predominantly by new trends, challenges and demands in practical systems such as supply chain and logistics systems where industries have the interest on how to design, manage, build and control systems as they increase in scale and complexity. They hope to build systems that are scalable, robust, and adaptive by using properties such as self-organization, self-adaptation, and manage the disruptions happening in their systems. 

​SS19 - Swarm Intelligent Applications

Organizers:  Dr. Wei-Chang Yeh, National Tsing Hua University, Taiwan 


Proposal: (Click here)

Today, Swarm Intelligence has become an increasingly important driving role for business strategies and real modern world applications. The essential idea of Swarm Intelligence is the emergent collective intelligence of groups of simple agents. There are several popular algorithms based on these concepts, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithms and Simplified Swarm Optimization (SSO), etc. Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. The aims of this special session are to demonstrate the current state-of-the-art concepts, theory and practice of Swarm Intelligence, to reflect on the latest advances in swarm intelligent design and applications for real world problems, and to explore the future directions in Swarm Intelligence.

​SS20 - Agent-Based Computational Economics and Experimental Economics

Organizers:  Dr. Shu-Heng ChenNational Chengchi University, Taiwan 

                        Dr. Ying-Fang KaoNational Chengchi University, Taiwan                     

Proposal: (Click here)

This special session attempts to forge a discussion on the recent developments and the future directions of Agent-Based Computational Economics, in the light of advances in Experimental Economics. In particular, this calls for a greater attention to the design of artificial agents for which the findings from Experimental Economics might prove to be quite insightful. Agent Based Modeling (ABM) has been steadily gaining more importance in the field of Economics and Finance. It is being viewed as an alternative approach to the conventional equation-based economic modeling, and as a useful methodology to investigate complex systems around us. In its early stages, developments in ABM paralleled those in Computational Intelligence, such as Zero-Intelligence Agents, Cellular Automata, Artificial Neural Networks, Fuzzy Logic, Swarm Intelligence, Genetic Algorithm, and Genetic Programming. It marked a truly interdisciplinary approach to social sciences, drawing insights from a variety of fields such as mathematics, biology, neuroscience, and computer science among others. Today, the development of ABM has reached a stage where the evidence concerning human decision making, largely arising from lab and filed experiments can no longer be neglected if it were to provide a formidable alternative. Among the factors affecting human decision making, cognitive capacity, personality, culture, emotion and social preference are well recognized and documented. However, integrating these attributes within the existing models of artificial agents is far from being trivial.

​SS21 - Complex Structure and Systems

Organizers:  Dr. Saori Iwanaga, Japan Coast Guard Academy, Japan

​                        Dr. Akira Ishii, Tottori University, Japan             

Proposal: (Click here)

There are many real network such as Internet, social networks, citation networks,infection network, diffusion network, and bank networks. And many scientists havemodeled and developed methods to help us understand the behavior of systems. In this field, we seek to explain models of network growth and dynamical processes taking place on networks. It is said that there is much to be done by M. E. J. Newman in this field. First, while we are beginning to understand some of the patterns and statistical regularities in the structure of real world networks, our techniques for analyzing networks are at present no more than a grab-bag of miscellaneous and largely unrelated tools. Second, there is much to be done in developing more sophisticated models of networks, both to help us understand network topology and to act as a substrate for the study of processes taking place on networks.

​SS22 - Intelligent and Evolutionary Networks

Organizers:  Dr. Vajirasak Vanijja, King Mongkuts University of Technology Thonburi, Thailand

​                        Dr.  Pongpisit Wuttidittachotti, King Mongkut's University of Technology North Bangkok, Thailand

​                        Dr. Therdpong Daengsi, King Mongkut's University of Technology North Bangkok, Thailand     

Proposal: (Click here)

The Internet is the largest network.   The Internet architecture is fundamentally a “client-server" architecture, with limited service capability and static routing/addressing. The intriguing interplay of local and topological dynamics makes robust self-organization possible in these networks. Recently important breakthroughs in the understanding of adaptable networks have been made and it is becoming more apparent that they could hold the key to many phenomena observed in a wide variety of applications. The goal of this special session is to promote research and development in Intelligent network which therefore presently receive rapidly increasing attention from researchers from very different fields including Genetic Algorithm, Artificial Intelligent, Practical Swarm Optimisation, Evolutionary Multi-objective Optimisation Algorithms, Evolutionary Algorithm, etc. The next wave of research in the field of Internet Architecture should solve remaining problems and bring the most promising options closer to deployment.

​SS23 - Smart Homes and Health Monitoring Systems: Theories and Applications

Organizers:  Dr. Chakarida Nukoolkit, King Mongkuts University of Technology Thonburi, Thailand

​                        Dr.  Kittichai Lavangnananda, King Mongkut's University of Technology Thonburi, Thailand

​                        Dr. Wallapak TavanapongIowa State University, USA

Proposal: (Click here)

In today’s modern life, societies have to embrace digital technologies in their homes and use them to enhance the quality of life. The emerging of aging societies around the world prompts more urgent need for having more affordable and easy to set up “smart home systems”, as well as assisted technologies used inside a home with high performance and efficient solutions and platforms.   Adoption of smartphones, smartTVs, and other smart devices, adds ubiquity and mobility to existing home-based self-care and health monitoring systems and expands the horizon of endless possibility in research and development.

​SS24 - Memetic Computing

Organizers:  Dr. Maoguo Gong, Xidian UniversityChina

​                        Dr.  Wenyin Gong, China University of Geosciences, China

​                        Dr. Zexuan Zhu, Shenzhen University, China

Proposal: (Click here)

Memetic Computing (MC) represents a broad generic framework using the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. MC usually emerges as population-based meta-heuristic algorithms or hybrid global-local search, inspired by Darwinian principles of natural selection and Dawkins’ notion of a meme defined as a unit of cultural evolution that is capable of local/individual refinements. Taking advantage of both biological selection and cultural selection, a plethora of potentially rich MC methodologies, frameworks and operational meme-inspired algorithms have been developed with considerable success in various real-world applications. Yet, there remain many open questions emerging as intriguing challenges for the field. The goal of this session is to serve as a forum for reseachers in this field to exchange the latest advances in theories, technologies, and practice of MC.

​SS25 - Computational Intelligence Algorithms and Techniques and Its Application to Real World Problems

Organizers:  Dr. Pandian Vasant, Universiti Teknologi PETRONAS, Malaysia

​                        Dr.  Irraivan Elamvazuthi, Universiti Teknologi PETRONAS, Malaysia

​                        Dr. Timothy Ganesan, Universiti Teknologi PETRONAS, Malaysia

Proposal: (Click here)

Computational intelligent Techniques are attractive global optimization methods inspired by the various phenomena arising in nature and man-made problems. They include Bat algorithm, Teaching and Learning based algorithm, Artificial Neural Networks, Fire fly, Simulated annealing, Tabu search, Variable neighborhood search, Support vector machine, Genetic Algorithms, Memetic Algorithms, Differential Evolutions, Particle Swarm Optimization (PSO), Glowworm Swarm Optimization, Bee Algorithms, Bacterial Foraging, Ant Colony Algorithms, Chaotic algorithm etc. These are relatively a newer addition to the class of numerical optimization algorithms. These methods have been successfully applied to a wide range of real-world application problems. Natural disasters and made chaotic problems partially can be solve by classical optimization techniques. Modern optimization algorithms are capable to handle and tackle these problems with higher level of degree of satisfaction.

Call for Invited Session Proposals

IES 2014 include Invited Sessions on highly-specialized and focused topics reporting technological frontiers and state-of-art methodologies which are in the scope of the conference. The call for invited sessions is currently open, and more information on the proposal procedure can be obtained in

                                                 [Call for Invited Sessions]

The deadline for submission is 1 August 2014.  Please allow two weeks for the evaluation.​ 

Justin Pang Chee Khiang (Invited Sessions Chair)

​NOTICE: Instructions for the accepted Invited Session organizers for EasyChair paper submission system

​Please (Click here)