European Conference on Applications of Evolutionary Computation 

About: European Conference on Applications of Evolutionary Computation is an academic conference. The conference publishes majorly in the area(s): Evolutionary algorithm & Population. Over the lifetime, 611 publications have been published by the conference receiving 7412 citations.

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

Abstract: Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning—pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators—such as synthetic feature constructors—that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.

Parallel genetic algorithm on the CUDA architecture

Abstract: This paper deals with the mapping of the parallel island-based genetic algorithm with unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is tested using Rosenbrock’s, Griewank’s and Michalewicz’s benchmark functions. The obtained results indicate that our approach leads to speedups up to seven thousand times higher compared to one CPU thread while maintaining a reasonable results quality. This clearly shows that GPUs have a potential for acceleration of GAs and allow to solve much complex tasks.

Influence Maximization in Social Networks with Genetic Algorithms

Abstract: We live in a world of social networks Our everyday choices are often influenced by social interactions Word of mouth, meme diffusion on the Internet, and viral marketing are all examples of how social networks can affect our behaviour In many practical applications, it is of great interest to determine which nodes have the highest influence over the network, ie, which set of nodes will, indirectly, reach the largest audience when propagating information These nodes might be, for instance, the target for early adopters of a product, the most influential endorsers in political elections, or the most important investors in financial operations, just to name a few examples Here, we tackle the NP-hard problem of influence maximization on social networks by means of a Genetic Algorithm We show that, by using simple genetic operators, it is possible to find in feasible runtime solutions of high-influence that are comparable, and occasionally better, than the solutions found by a number of known heuristics (one of which was previously proven to have the best possible approximation guarantee, in polynomial time, of the optimal solution) The advantages of Genetic Algorithms show, however, in them not requiring any assumptions about the graph underlying the network, and in them obtaining more diverse sets of feasible solutions than current heuristics

Search-based procedural content generation

Abstract: Recently, a small number of papers have appeared in which the authors implement stochastic search algorithms, such as evolutionary computation, to generate game content, such as levels, rules and weapons. We propose a taxonomy of such approaches, centring on what sort of content is generated, how the content is represented, and how the quality of the content is evaluated. The relation between search-based and other types of procedural content generation is described, as are some of the main research challenges in this new field. The paper ends with some successful examples of this approach.

Evolving behaviour trees for the commercial game DEFCON

Abstract: Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.