GECCO Tutorials 2015

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Full Citation in the ACM Digital Library

TUTORIAL SESSION: Tutorial Chair's Welcome

Session details: Tutorial Chair's Welcome

  • Simões Anabela

TUTORIAL SESSION: Introductory Tutorials

Genetic Programming: A Tutorial Introduction

  • O'Reilly Una-May

Evolutionary Computation: A Unified Approach

  • De Jong Kenneth A.

Tutorial on Evolutionary Multiobjective Optimization

  • Brockhoff Dimo

Many 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 ...

Particle Swarm Optimization

  • Engelbrecht Andries

Model-Based Evolutionary Algorithms

  • Thierens Dirk

Runtime Analysis of Evolutionary Algorithms: Basic Introduction

  • Lehre Per Kristian

Evolutionary algorithm theory has studied the time complexity of evolutionary algorithms for more than 20 years. Different aspects of this rich and diverse research field were presented in four different advanced or specialized tutorials at last year's ...

Evolving Neural Networks

  • Miikkulainen Risto

Neuroevolution, 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 ...

Introduction to Complex Networks

  • Tomassini Marco

Cartesian Genetic Programming

  • Miller Julian

Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed ...


  • Woodward John R.

Tutorial on Evolutionary Robotics

  • Bredeche Nicolas

In the same way as evolution shapes animals, is it possible to use artificial evolution to automatically design robots? This attractive vision gave birth to Evolutionary Robotics (ER), an interdisciplinary field that incorporates ideas from Biology, ...

Introducing Rule-based Machine Learning: A Practical Guide

  • Urbanowicz Ryan

Multimodal Optimization

  • Preuss Mike

Multimodal optimization is currently getting established as a research direction that collects approaches from various domains of evolutionary computation that strive for delivering multiple very good solutions at once. We start with discussing why this ...

Continuous Optimization and CMA-ES

  • Akimoto Youhei

Representations for Evolutionary Algorithms

  • Rothlauf Franz

Successful and efficient use of evolutionary algorithms (EA) 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 ...

TUTORIAL SESSION: Advanced Tutorials

Constraint-Handling Techniques used with Evolutionary Algorithms

  • Coello Coello Carlos Artemio

Evolutionary Algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear)...

Blind No More: Constant TimeNon-Random Improving Moves and Exponentially Powerful Recombination

  • Whitley Darrell

Expressive Genetic Programming

  • Spector Lee

The language in which evolving programs are expressed can have significant impacts on the problem-solving capabilities of a genetic programming system. These impacts stem both from the absolute computational power of the languages that are used, as ...

Parameterized Complexity Analysis of Evolutionary Algorithms

  • Neumann Frank

Theory of Swarm Intelligence

  • Sudholt Dirk

Social animals as found in fish schools, bird flocks, bee hives, and ant colonies are able to solve highly complex problems in nature. This includes foraging for food, constructing astonishingly complex nests, and evading or defending against predators. ...

Evolutionary Image Analysis, Signal Processing and Pattern Recognition

  • Zhang Mengjie

Generative and Developmental Systems Tutorial

  • Stanley Kenneth O.

Evolutionary Algorithms for Protein Structure Modeling

  • Shehu Amarda

In the last two decades, great progress has been made in molecular modeling through computational treatments of biological molecules grounded in evolutionary search techniques. Evolutionary algorithms (EAs) are gaining popularity beyond exploring the ...

Solving Complex Problems with Coevolutionary Algorithms

  • Heywood Malcolm I.

Theory of Evolution Strategies and Related Algorithms

  • Akimoto Youhei

Introduction to Gene Regulatory Networks

  • Cussat-Blanc Sylvain

Gene regulatory networks are a central mechanism in the regulation of gene expression in all living organisms? cells. Their functioning is nowadays very well understood: they are based on the production of proteins enhanced or inhibited by other ...

Semantic Genetic Programming

  • Moraglio Alberto

Semantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the 'black box' of the evaluation function and make explicit use of more information on program behavior in the search. In the most common ...

Evolutionary Computation for Dynamic Optimization Problems

  • Yang Shengxiang

Many real-world optimization problems are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic optimization problems (DOPs) are challenging ...

TUTORIAL SESSION: Specialized Tutorials

Medical Applications of Evolutionary Computation

  • Smith Stephen L.

The application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and ...

    Automatic (Offline) Configuration of Algorithms

    • Stützle Thomas

    Most optimization algorithms, including evolutionary algorithms and metaheuristics, and general-purpose solvers for integer or constraint programming, have often many parameters that need to be properly configured (i.e., tuned) for obtaining the best ...

    Intelligent Systems for Smart Cities

    • Alba Enrique

    The concept of Smart Cities can be understood as a holistic approach to improve the level of development and management of the city in a broad range of services by using information and communication technologies.

    It is common to recognize six axes of ...

    Low or no cost distributed evolutionary computation

    • Merelo-Guervós Juan J.

    From the era of big science we are back to the "do it yourself", where you do not have any money to buy clusters or subscribe to grids but still have algorithms that crave many computing nodes and need them to measure scalability. Fortunately, this ...

    Synergies between Evolutionary Algorithms and Reinforcement Learning

    • Drugan Madalina M.

    A recent trend in evolutionary algorithms (EAs) transfers expertise from and to other areas of machine learning. An interesting novel symbiosis considers: i) reinforcement learning (RL), which learns on-line and off-line difficult dynamic elaborated ...