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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, 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, ...

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 ...

Evolutionary Computation in Combinatorial Optimization

10th European Conference, EvoCOP 2010, Istanbul, Turkey, April 7-9, 2010, Proceedings

Constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2010, held in Instanbul, Turkey, in April 2010. This book discusses developments and applications in metaheuristics.

An Evolutionary Algorithm Guided by Preferences Elicited According to the
ELECTRE TRI Method Principles Eunice Oliveira1 and Carlos Henggeler
Antunes2 1 School of Technology and Management, Polytechnic Institute of
Leiria, Morro do ...

Evolutionary Computation in Practice

This book is loaded with examples in which computer scientists and engineers have used evolutionary computation - programs that mimic natural evolution - to solve many real-world problems. They aren’t abstract, mathematically intensive papers, but accounts of solving important problems, including tips from the authors on how to avoid common pitfalls, maximize the effectiveness and efficiency of the search process, and many other practical suggestions.

COMPUTATION. IN. PRACTICE. Tina Yu1 and Lawrence Davis2 1 2 Memorial
University of Newfoundland; VGO Associates
DeployingEvolutionaryComputation(EC)solutionstoreal-worldproblems involves
a wide spectrum of activities, ...

Applications of Evolutionary Computation in Chemistry

H. M. Cartwright: An Introduction to Evolutionary Computation andEvolutionary Algorithms; B. Hartke: Application of Evolutionary Algorithms to Global Cluster Geometry Optimization; K.D.M. Harris, R.L. Johnston, S. Habershon: Application of Evolutionary Computation in Structure Solution from Diffraction Data; S. M.

1 10 (2004): 1-32 DOI 10.1007/b 13931 An Introduction to Evolutionary
Computation and Evolutionary Algorithms Hugh M. Cartwright Physical and
Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford
OX 1 3QZ, UK ...

Genetic And Evolutionary Computation- GECCO 2004

Genetic And Evolutionary Computation Conference, Seattle, Wa, Usa, June 26-30, 2004, Proceedings

MostMOEAsuseadistancemetricorothercrowdingmethodinobjectivespaceinorder to maintain diversity for the non-dominated solutions on the Pareto optimal front. By ensuring diversity among the non-dominated solutions, it is possible to choose from a variety of solutions when attempting to solve a speci?c problem at hand. Supposewehavetwoobjectivefunctionsf (x)andf (x).Inthiscasewecande?ne 1 2 thedistancemetricastheEuclideandistanceinobjectivespacebetweentwoneighboring individuals and we thus obtain a distance given by 2 2 2 d (x, x )= f (x )?f (x )] + f (x )?f (x )] . (1) 1 2 1 1 1 2 2 1 2 2 f wherex andx are two distinct individuals that are neighboring in objective space. If 1 2 2 2 the functions are badly scaled, e.g. ?f (x)] ?f (x)], the distance metric can be 1 2 approximated to 2 2 d (x, x )? f (x )?f (x )] . (2) 1 2 1 1 1 2 f Insomecasesthisapproximationwillresultinanacceptablespreadofsolutionsalong the Pareto front, especially for small gradual slope changes as shown in the illustrated example in Fig. 1. 1.0 0.8 0.6 0.4 0.2 0 0 20 40 60 80 100 f 1 Fig.1.Forfrontswithsmallgradualslopechangesanacceptabledistributioncanbeobtainedeven if one of the objectives (in this casef ) is neglected from the distance calculations. 2 As can be seen in the ?gure, the distances marked by the arrows are not equal, but the solutions can still be seen to cover the front relatively well.

Genetic And Evolutionary Computation Conference, Seattle, Wa, Usa, June 26-
30, 2004, Proceedings Kalyanmoy Deb. A 10 × 10 board requires a neural
network with 100 inputs and 100 outputs, but is still simple enough to be solved
without ...

Introduction to Evolutionary Computing

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.