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Linkage in Evolutionary Computation

In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily ”fooled” by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.

Ying-ping Chen. Genetic Algorithms for the Airport Gate Assignment: Linkage,
Representation and Uniform Crossover Xiao-Bing Hu1 and Ezequiel Di Paolo2 1
Centre for Computational Neuroscience and Robotics, University of Sussex ...

Success in Evolutionary Computation

Darwinian evolutionary theory is one of the most important theories in human history for it has equipped us with a valuable tool to understand the amazing world around us. There can be little surprise, therefore, that Evolutionary Computation (EC), inspired by natural evolution, has been so successful in providing high quality solutions in a large number of domains. EC includes a number of techniques, such as Genetic Algorithms, Genetic Programming, Evolution Strategy and Evolutionary Programming, which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting rapidly evolving field.

Optimizing Multiplicative General Parameter Finite Impulse Response Filters
Using Evolutionary Computation Jarno Martikainen1 and Seppo J. Ovaska2 1
Helsinki University of Technology, Department of Electrical and Communications
 ...

Evolutionary Computation in Combinatorial Optimization

11th European Conference, EvoCOP 2011, Torino, Italy, April 27-29, 2011, Proceedings

This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

Prominent examples of metaheuristics are evolutionary algorithms, tabu search,
simulated annealing, scatter search, memetic algorithms, variable neighborhood
search, iterated local search, greedy randomized adaptive search procedures, ...

Evolutionary Computation in Combinatorial Optimization

12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012, Proceedings

This book constitutes the refereed proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. . The 22 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

Prominent examples of metaheuristics are evolutionary algorithms, tabu search,
simulated annealing, scatter search, memetic algorithms, variable neighborhood
search, iterated local search, greedy randomized adaptive search procedures, ...

Evolutionary Computation in Combinatorial Optimization

7th European Conference, EvoCOP 2007, Valencia, Spain, April 11-13, 2007, Proceedings

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers presented were carefully reviewed and selected from 81 submissions. The papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, greedy randomized adaptive search procedures, ant colony optimization, and particle swarm optimization algorithms. The papers are specifically dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems and cover any issue of metaheuristic for combinatorial optimization. The papers deal with representations, heuristics, analysis of problem structures, and comparisons of algorithms. The list of studied combinatorial optimization problems includes prominent examples like graph coloring, knapsack problems, the traveling salesperson problem, scheduling, graph matching, as well as specific real-world problems.

Prominent examples of metaheuristics are evolutionary algorithms, simulated
annealing, tabu search, scatter search, memetic algorithms, variable
neighborhood search, iterated local search, greedy randomized adaptive search
procedures, ...

Genetic and Evolutionary Computation--GECCO 2003

Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003 : Proceedings

The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionaty Computation Conference, GECCO 2003, held in Chicago, IL, USA in July 2003. The 193 revised full papers and 93 poster papers presented were carefully reviewed and selected from a total of 417 submissions. The papers are organized in topical sections on a-life adaptive behavior, agents, and ant colony optimization; artificial immune systems; coevolution; DNA, molecular, and quantum computing; evolvable hardware; evolutionary robotics; evolution strategies and evolutionary programming; evolutionary sheduling routing; genetic algorithms; genetic programming; learning classifier systems; real-world applications; and search based softare engineering.

Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16
, 2003 : Proceedings Erick Cantú-Paz ... We explore the advantages of DNA-like
genomes for evolutionary computation in silico. Coupled with simulations of ...

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 ...

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 ...