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- tutorialJuly 2011
Evolutionary games: the Darwin connection
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1469–1480https://doi.org/10.1145/2001858.2002145Evolutionary game theory has been introduced essentially by biologists in the seventies and has immediately diffused into economical and sociological circles. Today, it is a main pillar of the whole edifice of game theory and widely used both in theory ...
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- tutorialJuly 2011
Algorithm and experiment design with heuristiclab: an open source optimization environment for research and education
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1411–1438https://doi.org/10.1145/2001858.2002143The tutorial demonstrates how to apply and analyze metaheuristics using HeuristicLab, an open source optimization environment. It will be shown how to parameterize and execute evolutionary algorithms to solve combinatorial optimization problems (...
- tutorialJuly 2011
Automatic and interactive tuning of algorithms
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1361–1380https://doi.org/10.1145/2001858.2002141Providing tools for algorithm tuning (and the related statistical analysis) is the main topic of this tutorial. This tutorial provides the necessary background for performing algorithm tuning with state-of-the-art tools. We will discuss pros and cons of ...
- tutorialJuly 2011
Automated heuristic design
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1321–1342https://doi.org/10.1145/2001858.2002139This tutorial will discuss state-of-the-art techniques for automating the design of heuristic search methods, in order to remove or reduce the need for a human expert in the process of designing an effective algorithm to solve a search problem. Using ...
- tutorialJuly 2011
Large scale data mining using genetics-based machine learning
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1285–1310https://doi.org/10.1145/2001858.2002137We are living in the peta-byte era. We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human ...
- tutorialJuly 2011
GECCO 2011 tutorial: cartesian genetic programming
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1261–1284https://doi.org/10.1145/2001858.2002136Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000.
In its classic form, it uses a very simple integer based genetic representation of a program ...
- tutorialJuly 2011
Representations for evolutionary algorithms
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1191–1212https://doi.org/10.1145/2001858.2002132Successful and efficient use of evolutionary algorithms (EAs) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices ...
- tutorialJuly 2011
Evolutionary multiobjective optimization
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1111–1136https://doi.org/10.1145/2001858.2002129Many optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum ...
- tutorialJuly 2011
Evolving neural networks
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 1011–1028https://doi.org/10.1145/2001858.2002124Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially ...
- tutorialJuly 2011
CMA-ES: evolution strategies and covariance matrix adaptation
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 991–1010https://doi.org/10.1145/2001858.2002123Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rn. Many of them can be ...
- tutorialJuly 2011
Learning classifier systems
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 941–962https://doi.org/10.1145/2001858.2002121In the 1970s, John H. Holland designed Learning Classifier Systems (LCSs) as highly adaptive, cognitive systems. Since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995 and the modular analysis of several LCSs ...
- tutorialJuly 2011
Probabilistic model-building genetic algorithms
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 913–940https://doi.org/10.1145/2001858.2002120Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...
- tutorialJuly 2011
Evolutionary computation: a unified approach
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 899–912https://doi.org/10.1145/2001858.2002119The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, ...
- tutorialJuly 2011
Evolution strategies: basic introduction
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationPages 875–898https://doi.org/10.1145/2001858.2002118This tutorial gives a basic introduction to evolution strategies, a class of evolutionary algorithms. Key features such as mutation, recombination and selection operators are explained, and specifically the concept of self-adaptation of strategy ...