Neuroevolution: from architectures to learning

Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these problems. New insights in both neuroscience and evolutionary biology have led to the development of increasingly powerful neuroevolution techniques over the last decade. This paper gives an overview of the most prominent methods for evolving ANNs with a special focus on recent advances in the synthesis of learning architecture

Arms races and car races

Evolutionary car racing (ECR) is extended to the case of two cars racing on the same track. A sensor representation is devised, and various methods of evolving car controllers for competitive racing are explored. ECR can be combined with co-evolution in a wide variety of ways, and one aspect which is explored here is the relative-absolute fitness continuum. Systematical behavioural differences are found along this continuum; further, a tendency to specialization and the reactive nature of the controller architecture are found to limit evolutionary progress

Intelligent Prediction of Ship Maneuvering

In this paper the author presents an idea of the intelligent ship maneuvering prediction system with the usage of neuroevolution. This may be also be seen as the ship handling system that simulates a learning process of an autonomous control unit, created with artificial neural network. The control unit observes input signals and calculates the values of required parameters of the vessel maneuvering in confined waters. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task efficiently. The main task of the system is to learn continuously and predict the values of a navigational parameters of the vessel after certain amount of time, regarding an influence of its environment. The result of a prediction may occur as a warning to navigator to aware him about incoming threat

Autonomous Intersection Driving with Neuro-Evolution

Neuro-Evolution (NE) has been used to evolve controllers in land-based vehicles that accomplish various tasks. However, there has been little work on evolving coordinated movement for maximizing traffic flow through intersections. This study used NE to synthesize collective driving behaviors for given road networks (interconnected intersections), where there were no traffic signals to assist with vehicle coordination and navigation. Rather, NE automates controller design where collective driving behavior emerges in response to the task of maximizing traffic throughput and minimizing delays at intersections

How to Best Automate Intersection Management

Recently there has been increased research interest in developing adaptive control systems for autonomous vehicles. This study presents a comparative evaluation of two distinct approaches to automated intersection management for a multiagent system of autonomous vehicles. The first is a centralized heuristic control approach using an extension of the Autonomous Intersection Management (AIM) system. The second is a decentralized neuro-evolution approach that adapts vehicle controllers so as they collectively navigate intersections. This study tests both approaches for controlling groups of autonomous vehicles on a network of interconnected intersections, without the constraints of traffic lights or stop signals. These task environments thus simulate potential future scenarios where vehicles must drive autonomously without specific road infrastructure constraints. The capability of each approach to appropriately handle various types of interconnected intersections, while maintaining an efficient throughput of vehicles and minimizing delay is tested. Results indicate that neuro-evolution is an effective method for automating collective driving behaviors that are robust across a broad range of road networks, where evolved controllers yield comparable task performance or out-perform an AIM controller

Gene regulated car driving: using a gene regulatory network to drive a virtual car

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition

Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others\u27 images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems

Using Neuroevolution Method NEAT

Import 04/11/2015Tato bakalářská práce se zabývá optimalizací protivníka zvolené logické hry Lodě. Optimalizace byla provedena pomocí metody NEAT a implementace SharpNeat pro jazyk C#. Práce popisuje neuronové sítě, evoluční algoritmy a metodu NEAT.The bachelor thesis deals with an adversary optimization of chosen logical game The Ships. The optimization was made by NEAT method and SharpNeat implementation for language C#. This thesis describes neural networks, evolution algorithms and NEAT method.460 - Katedra informatikyvelmi dobř