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Evolutionary 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 ...

The 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 (...

Providing 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 ...

We 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 ...

Cartesian 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 ...

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 ...

Probabilistic 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. ...

This 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 ...

This work presents a PSO implemention in CUDA architecture, aiming to speed up the algorithm on problems which has large amounts of data. PSO-GPU algorithm was designed to customization, in order to adapt for any problem that can be solved by a PSO ...

A fast compression based technique is proposed, capable of detecting promising emergent space-time patterns of cellular automata (CA). This information can be used to automatically guide the evolutionary search toward more complex, better performing ...

Co-optimization test-based problems is a class of tasks approached typically with coevolutionary algorithms. It was recently shown that such problems exhibit underlying objectives that form internal problem structure, which can be extracted and analyzed ...

We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its ...

Evolutionary algorithms have been used to effectively generate solutions to artificial life problems. However, this process may take a number of generations to complete. Research to accelerate evolutionary search has been reported, yet, insights into ...