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

Computational Creativity Support: Using Algorithms And Machine Learning To Help People Be More Creative

The emergence of computers as a core component of creative processes, coupled with recent advances in machine-learning, signal-processing, and algorithmic techniques for manipulating creative media, offers tremendous potential for building end-user creativity-support tools. However, the scientific community making advances in relevant algorithmic techniques is not, in many cases, the same community that is currently making advances in the design, evaluation, and user-experience aspects of creativity support. The primary objective of this workshop is thus to bring together participants from diverse backgrounds in the HCI, design, art, machine-learning, and algorithms communities to facilitate the advancement of novel creativity support tools

Parallelization Of Fuzzy Artmap To Improve Its Convergence Speed: The Network Partitioning Approach And The Data Partitioning Approach

One of the properties of FAM, which can be both an asset and a liability, is its capacity to produce new neurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure. We provide two methods for speeding up the FAM algorithm. The first one, referred to as the data partitioning approach, partitions the data into subsets for independent processing. The second one, referred to as the network partitioning approach, uses a pipeline to distribute the work between processes during training. We provide experimental results on a Beowulf cluster of workstations for both approaches that confirm the speedup of the modifications. © 2005 Elsevier Ltd. All rights reserved

Parallelizing The Fuzzy Artmap Algorithm On A Beowulf Cluster

Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that it takes Fuzzy ARTMAP to converge to a solution increases rapidly as the number of patterns used for training increases. In this paper we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up Fuzzy ARTMAP\u27s training process. In particular, we first parallelized Fuzzy ARTMAP, without the match-tracking mechanism, and then we parallelized Fuzzy ARTMAP with the match-tracking mechanism. Results run on a Beowulf cluster with a well known large database (Forrest Covertype database from the UCI repository) show linear speedup with respect to the number of processors used in the pipeline. © 2005 IEEE

Analyzing The Fuzzy Artmap Matchtracking Mechanism With Co-Objective Optimization Theory

In the process of learning a pattern I, the Fuzzy ARTMAP algorithm templates (i.e., the weight vectors corresponding to nodes of its category representation layer) compete for the representation of the given pattern. This competition can induce matchtracking: a process that iterates a number of times over the template set searching for a template w* of the correct class that best represents the pattern I. In this paper, we analyze the search for a winning template from the perspective of bi-criterion optimization and prove that it is actually a walk along the Pareto front of an appropriately defined co-objective optimization problem. This observation allows us to propose the basis for an implementation variant of Fuzzy ARTMAP that (a) produces exactly the same network as Fuzzy ARTMAP, (b) avoids matchtracking by explicitly keeping track of a subset of the Pareto front, (c) finds the correct template to represent an input pattern through a single pass over the template set and (d) eliminates the need for the Fuzzy ARTMAP parameter ε. ©2007 IEEE

PREDATOR: A Protocol for Ad-hoc and Brokered Dynamic Spectrum Management

If technological trends are any indication, we are coming upon a future where we will have highly-cognitive transmitters and receivers capable of using many different frequencies, transmission powers, modulation schemes and MAC protocols. Future generations of mobile devices will be able to bid for the spectrum that they require from a broker, or will have ways of automatically reducing interference by negotiation with other devices. Despite the many different algorithms and policies that could be used to support this, to the best of our knowledge, there is currently a lack of a unified protocol to allow negotiation of spectrum for brokered and non-brokered environments. The proposed protocol, PREDATOR (PRotocol for Equitable, Dynamic AllocaTion of Radio spectrum), accommodates both brokered and ad hoc configurations. In this paper, we provide a detailed description of how the protocol works, as well as results from a sample application environment to show its efficacy. © 2007 IEEE

A Privacy Preserving Probabilistic Neural Network For Horizontally Partitioned Databases

In this paper, we present a version of the Probabilistic Neural Network (PNN) that is capable of operating on a distributed database that is horizontally partitioned. It does so in a way that is privacy-preserving: that is, a test point can be evaluated by the algorithm without any party knowing the data owned by the other parties. We present an analysis of this algorithm from the standpoints of security and computational performance. Finally, we provide performance results of an implementation of this privacy preserving, distributed PNN algorithm. ©2007 IEEE