Meta-learning computational intelligence architectures

In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii

Divide and Conquer Evolutionary TSP Solution for Vehicle Path Planning

The problem of robotic area coverage is applicable to many domains, such as search, agriculture, cleaning, and machine tooling. The robotic area coverage task is concerned with moving a vehicle with an effector, or sensor, through the task space such that the sensor passes over every point in the space. For covering complex areas, back and forth paths are inadequate. This paper presents a real-time path planning architecture consisting of layers of a clustering method to divide and conquer the problem combined with a two layered, global and local optimization method. This architecture is able to optimize the execution of a series of waypoints for a restricted mobility vehicle, a fixed wing airplane

A Survey of Neural Computation on Graphics Processing Hardware

Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. From machine vision to finite element analysis, CPU\u27s are being used in diverse applications, collectively called general purpose graphics processor utilization. This paper explores the capabilities and limitations of modern GPU\u27s and surveys the neural computation technologies that have been applied to these devices

Adaptive Multi-Vehicle Area Coverage Optimization System and Method

A mission planning system for determining an optimum use of a plurality of vehicles in searching a predefined geographic area (PGA). A discretizer subsystem may be used for sensing the capabilities of each vehicle to produce a point set defining a number of points within the PGA that the vehicles must traverse to completely search the PGA. A task allocator subsystem may determine an optimum division of the PGA into different subregions to be handled by specific ones of the vehicles, thus to minimize an overall time needed to search the PGA. A path optimizer subsystem may determine an optimum path through a particular vehicle\u27s assigned subregion to minimize the time needed for each specific vehicle to traverse its associated subregion

Adaptive multi-vehicle mission planning for search area coverage

This thesis explores the problem of optimizing the behavior of a swarm of heterogeneous agents executing a search area coverage task. Each agent is equipped with a sensing apparatus and the swarm must collectively explore a partially occluded environment to obtain a required probability of observation for each location in the search area. The problem is further complicated with the introduction of dynamic agent and environmental properties making adaptability a necessary requirement. Novel methods for search space discretization and task allocation are presented. Additionally modifications are made to conventional path optimization methods to account for the mobility characteristics of an agent following a path. A mission planning architecture is presented that incorporates these techniques to provide adaptive multi-vehicle search coverage optimization for heterogeneous vehicles --Abstract, page iii

Computational Intelligence Meets the NetFlix Prize

The NetFlix Prize is a research contest that will award $1 Million to the first group to improve NetFlix\u27s movie recommendation system by 10%. Contestants are given a dataset containing the movie rating histories of customers for movies. From this data, a processing scheme must be developed that can predict how a customer will rate a given movie on a scale of 1 to 5. An architecture is presented that utilizes the Fuzzy-Adaptive Resonance Theory clustering method to create an interesting set of data attributes that are input to a neural network for mapping to a classification

Hierarchical Mission Management

The different advantageous embodiments further provide a system for hierarchical mission management comprising a number of mission planners, a data processing system, and a communication system. The number of mission planners are associated with a number of agent groups. The data processing system is configured to execute the number of mission planners. The communication system is configured to provide communication between the number of mission planners