A Neat Approach To Genetic Programming

The evolution of explicitly represented topologies such as graphs involves devising methods for mutating, comparing and combining structures in meaningful ways and identifying and maintaining the necessary topological diversity. Research has been conducted in the area of the evolution of trees in genetic programming and of neural networks and some of these problems have been addressed independently by the different research communities. In the domain of neural networks, NEAT (Neuroevolution of Augmenting Topologies) has shown to be a successful method for evolving increasingly complex networks. This system\u27s success is based on three interrelated elements: speciation, marking of historical information in topologies, and initializing search in a small structures search space. This provides the dynamics necessary for the exploration of diverse solution spaces at once and a way to discriminate between different structures. Although different representations have emerged in the area of genetic programming, the study of the tree representation has remained of interest in great part because of its mapping to programming languages and also because of the observed phenomenon of unnecessary code growth or bloat which hinders performance. The structural similarity between trees and neural networks poses an interesting question: Is it possible to apply the techniques from NEAT to the evolution of trees and if so, how does it affect performance and the dynamics of code growth? In this work we address these questions and present analogous techniques to those in NEAT for genetic programming

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

Trainee Evaluation Through After-Action Review By Comparison

We describe an investigation into how to automate after-action review (AAR) to provide non-trivial, individual feedback to trainees in a military training context. On a high-level basis, our approach is to monitor the actions of the trainee(s) and compare them with those of software agents (called expert agents) whose behavior represents that of an expert-level performer. By identifying and logging discrepancies between the trainee and the expert agent, a measure of valuable feedback can be given to the trainee to whom the expert agent was assigned to ‘shadow’. The comparisons are made in two dimensions concurrently: the physical dimension and the tactical dimension. In a physical comparison, the trainee is compared with the physical location of the expert agent. In the tactical comparison, the context of the agent is compared with that of the trainee. If the latter comparison agrees, then it can be said that the trainee is employing the same tactics as the agent. The context of the trainee is un-intrusively inferred through a novel process called context agents. A prototype is built and tested with data from an instrumented live exercise. Results indicate that the procedure has significant promise to provide much-needed automation in AAR. © 2009, The Societyfor Modeling and Simulation International. All rights reserved

Picbreeder: Collaborative Interactive Evolution of Images

Picbreeder [1] is a new website that is open to the public for collaborative interactive evolution of images. A unique feature of Picbreeder is that users can continue evolving other users’ images by branching. The continual process of evolving and branching means that images can continue to improve and increase in complexity indefinitely, yielding a proliferation of artistic novelty that requires no explicit artistic talent to produce. Interactive Evolutionary Computation Picbreeder borrows ideas from Evolutionary Computation (EC), which allows computers to produce a myriad of digital artifacts, from circuit designs to neural networks, by emulating the process of natural selection. In EC, a population of individuals is evaluated for fitness and mutated and/or mated, to produce the next generation. This cycle continues until evolution produces an individual considered significant. Interactive Evolutionary Computation (IEC), originally explored by Dawkins [2], is a type of EC in which a human evaluates the fitness of individuals (see Takagi [3] for a review). This technique is particularly effective at evolving artifacts that are too subjective for the computer to evaluate itself, including artwork, music, and designs. Picbreeder uses a specialized evolutionary algorithm called Compositiona

Picbreeder: Evolving Pictures Collaboratively Online

Picbreeder is an online service that allows users to collaboratively evolve images. Like in other Interactive Evolutionary Computation (IEC) programs, users evolve images in Picbreeder by selecting ones that appeal to them to produce a new generation. However, Picbreeder also offers an online community in which to share these images, and most importantly, the ability to continue evolving others\u27 images. Through this process of branching from other images, and through continually increasing image complexity made possible by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC systems. Picbreeder enables all users, regardless of talent, to participate in a creative, exploratory process. This paper details how Picbreeder encourages innovation, featuring images that were collaboratively evolved. Copyright 2008 ACM

Picbreeder: evolving pictures collaboratively online

Picbreeder is an online service that allows users to collaboratively evolve images. Like in other Interactive Evolutionary Computation (IEC) programs, users evolve images in Picbreeder by selecting ones that appeal to them to produce a new generation. However, Picbreeder also offers an online community in which to share these images, and most importantly, the ability to continue evolving others ’ images. Through this process of branching from other images, and through continually increasing image complexity made possible by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC systems. Picbreeder enables all users, regardless of talent, to participate in a creative, exploratory process. This paper details how Picbreeder encourages innovation, featuring images that were collaboratively evolved