Hampshire College

About: Hampshire College is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Genetic programming & Population. The organization has 461 authors who have published 998 publications receiving 40827 citations.

Comparison of Linear Genome Representations for Software Synthesis

Abstract: In many genetic programming systems, the program variation and execution processes operate on different program representations. The representations on which variation operates are referred to as genomes. Unconstrained linear genome representations can provide a variety of advantages, including reduced complexity of program generation, variation, simplification and serialization operations. The Plush genome representation, which uses epigenetic markers on linear genomes to express nonlinear structures, has supported the production of state-of-the-art results in program synthesis with the PushGP genetic programming system. Here we present a new, simpler, non-epigenetic alternative to Plush, called Plushy, that appears to maintain all of the advantages of Plush while providing additional benefits. These results illustrate the virtues of unconstrained linear genome representations more generally, and may be transferable to genetic programming systems that target different languages for evolved programs.

Children's strategies for discovering cause‐effect relationships

Abstract: This study describes strategies children use to solve a complex problem. The problem asked children to figure out how to control a “vehicle” that they “drove” by pressing particular keys on a computer. The problem can be viewed as scientific in that variables must be identified and hypotheses formulated and tested to discover cause-effect relationships. Subjects were fifth and sixth graders sampled from public and private schools. The primary purpose of the study was to examine the transition between implicit, immature problem-solving strategies and explicit, mature theorizing characteristic of scientific problem solving. The study manipulated the problem perspective subjects were given and the number of cause-effect relationships in the problem. The study's description of children's problem solving highlights “focusing,” problem-solving behavior that is aimed at forming a mental representation, model, or theory about the problem, as a key link between mature and immature reasoning. Subject's school and school X perspective interaction was found to affect problem-solving performance.

Teaching the evolution of behavior with SuperDuperWalker

Abstract: SuperDuperWalker is a software-based framework for experiments on the evolution of locomotion. It simulates the behavior of evolving agents in a 3D physical simulation environment and displays this behavior graphically in real time. A genetic algorithm controls the evolution of the agents. Students manipulate parameters with a graphical user interface and plot outputs using standard utilities. The software supports an inquiry cycle that has been piloted in CS193T: Biocomputational Developmental Ecology at Hampshire College.

Expressive Genetic Programming: Concepts and Applications

Abstract: The language in which evolving programs are expressed can have significant impacts on the dynamics and problem-solving capabilities of a genetic programming system. In GP these impacts are driven by far more than the absolute computational power of the languages used; just because a computation is theoretically possible in a language, it doesn't mean it's readily discoverable or leveraged by evolution. Highly expressive languages can facilitate the evolution of programs for any computable function using, as appropriate, multiple data types, evolved subroutines, evolved control structures, evolved data structures, and evolved modular program and data architectures. In some cases expressive languages can even support the evolution of programs that express methods for their own reproduction and variation (and hence for the evolution of their offspring). This tutorial will present a range of approaches that have been taken for evolving programs in expressive programming languages. We will then provide a detailed introduction to the Push programming language, which was designed specifically for expressiveness in genetic programming systems. Push programs are syntactically unconstrained but can nonetheless make use of multiple data types and express arbitrary control structures, potentially supporting the evolution of complex, modular programs in a particularly simple and flexible way. Interleaved with our description of the Push language will be demonstrations of the use of analytical tools such as graph databases and program diff/merge tools to explore ways in which evolved Push programs are actually taking advantage of the language's expressive features. We will illustrate, for example, the effective use of multiple types and type-appropriate functions, the evolution and modification of code blocks and looping/recursive constructs, and the ability of Push programs to handle multiple, potentially related tasks. We will conclude with a brief review of over a decade of Push-based research, including the production of human-competitive results, along with recent enhancements to Push that are intended to support the evolution of complex and robust software systems.

Modeling Gas Exchange by Actinorhizal Root Nodules Using Network Simulation Analysis

Abstract: All organisms which use the enzyme nitrogenase to obtain reduced nitrogen, whether in a symbiosis or in the free-living state, have means to protect nitrogenase from excessively high oxygen concentrations and to provide large amounts of ATP and reductant to the site of nitrogenase (Robson and Postgate 1980). Nitrogenase is extremely oxygen labile and requires large amounts of reductant and ATP (Bergersen 1982). In aerobic microorganisms oxygen protection of nitrogenase involves a diffusion barrier surrounding the sites of nitrogen fixation and of oxidative energy metabolism. The barrier slows the entrance of oxygen into the nitrogenase-containing compartment to a rate equivalent to the rate of oxygen uptake by respiration, ensuring a steady energy supply and a low oxygen concentration in the vicinity of nitrogenase.