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Intelligence Computation and Evolutionary Computation

Results of 2012 International Conference of Intelligence Computation and Evolutionary Computation ICEC 2012 Held July 7, 2012 in Wuhan, China

2012 International Conference of Intelligence Computation and Evolutionary Computation (ICEC 2012) is held on July 7, 2012 in Wuhan, China. This conference is sponsored by Information Technology & Industrial Engineering Research Center. ICEC 2012 is a forum for presentation of new research results of intelligent computation and evolutionary computation. Cross-fertilization of intelligent computation, evolutionary computation, evolvable hardware and newly emerging technologies is strongly encouraged. The forum aims to bring together researchers, developers, and users from around the world in both industry and academia for sharing state-of-art results, for exploring new areas of research and development, and to discuss emerging issues facing intelligent computation and evolutionary computation.

Results of 2012 International Conference of Intelligence Computation and
Evolutionary Computation ICEC 2012 Held July 7, 2012 in Wuhan, China Zhenyu
Du. A PCA-Based Automated Method for Determination of Human Body
Orientation ...

Applications of Evolutionary Computation

19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings

The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

8. Kruisselbrink, J.W., Emmerich, M.T.M., Deutz, A.H., Bäck, T.: A robust
optimization approach using kriging metamodels for robustness approximation in
the CMA-ES. In: Proceedings of IEEE Congress on Evolutionary Computation (
CEC), pp.

Markov Networks in Evolutionary Computation

Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.

This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general.

Self-Adaptive Heuristics for Evolutionary Computation

This book introduces various types of self-adaptive parameters for evolutionary computation. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.

Oliver Kramer. Oliver Kramer Studies in Computational Intelligence,Volume 147
Oliver Kramer Self-Adaptive Heuristics for. Self-Adaptive Heuristics for
Evolutionary Computation Fig. 2.2. Pseudocode of a generalized evolutionary
algorithm.

Evolutionary Computation in Economics and Finance

After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.

14. Evolutionary. Computation. and. Economic. Models: Sensitivity. and.
Unintended. Consequences. David B. Fogel1, Kumar Chellapilla2, and Peter J.
Angeline3 1 Natural Selection, Inc. d ...

Evolutionary Computation in Gene Regulatory Network Research

"This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC)"--

"This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC)"-- This book serves as a handbook for gene regulatory network research using evolutionary algorithms, with ...

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

5th European Conference, EvoBIO 2007, Valencia, Spain, April 11-13, 2007, Proceedings

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences data in order to unravel the mysteries of biological function, leading to new drugs and therapies for human disease. Life sciences data come in the form of biological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c patterns present in a given dataset and then to interpret those patterns. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the Fifth European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2007) was to bring experts in computer science - gether with experts in bioinformatics and the biological sciences to explore new and novel methods for solving complex biological problems. The ?fth EvoBIO conference was held in Valencia, Spain during April 11-13, 2007 at the Universidad Politecnica de Valencia. EvioBIO 2007 was held jointly with the Tenth European Conference on Genetic Programming (EuroGP 2007), the Seventh European Conference on Evolutionary Computation in Combina- rial Optimisation (EvoCOP 2007), and the Evo Workshops. Collectively, the c- ferences and workshops are organized under the name Evo* (www. evostar. org).

... clinical safety (black), efficacy (red), formulation (green), PK/bioavailability (
blue), commercial (yellow), toxicology (gray), cost of goods (purple) and others (
white). development of computational tools applicable for pharmacokinetic
profiling, ...

Automatic Generation of Neural Network Architecture Using Evolutionary Computation

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.

7.2 Evolutionary Computing to Analyse a NN Although this combination of GAs
and NNs is not common, GAs can be used to analyse or explain neural networks.
In [13] GAs are used as a neural network inversion tool in that they can find the ...

Progress in Evolutionary Computation

AI '93 and AI '94 Workshops on Evolutionary Computation, Melbourne, Victoria, Australia, November 16, 1993, Armidale, NSW, Australia, November 21-22, 1994. Selected Papers

This volume contains the best carefully revised full papers selected from the presentations accepted for the AI '93 and AI '94 Workshop on Evolutionary Computation held in Australia. The 21 papers included cover a wide range of topics in the field of evolutionary computation, from constrained function optimization to combinatorial optimization, from evolutionary programming to genetic programming, from robotic strategy learning to co-evolutionary game strategy learning. The papers reflect important recent progress in the field; more than half of the papers come from overseas.

AI '93 and AI '94 Workshops on Evolutionary Computation, Melbourne, Victoria,
Australia, November 16, 1993, Armidale, NSW, Australia, November 21-22, 1994.
Selected Papers Xin Yao ...

Evolutionary Computation in Dynamic and Uncertain Environments

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.

1 Explicit Memory Schemes for Evolutionary Algorithms in Dynamic
Environments Shengxiang Yang Department of ... Problem optimization in
dynamic environments has atrracted a growing interest from the evolutionary
computation ...