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.

Linear Genomes for Structured Programs

Abstract: In most genetic programming systems, candidate solution programs themselves serve as genome upon which variation operators act. However, because of the hierarchical structure of computer programs and the syntactic constraints that they must obey, it is difficult to implement variation operators that affect different parts of programs with uniform probability. This lack of uniformity can have detrimental effects on evolutionary search, such as increases in code bloat. In prior work, structured programs were linearized prior to variation in order to facilitate uniform variation. However, this necessitated syntactic repair after variation, which reintroduced non-uniformities. In this chapter we describe a new approach that uses linear genomes that are translated into hierarchical programs for execution. We present the new approach in detail and show how it facilitates both uniform variation and the evolution of programs with meaningful structure.

Interactions between nectar robbers and seed predators mediated by a shared host plant, Ipomopsis aggregata

Abstract: Animals that consume plant parts or rewards but provide no services in return are likely to have significant impacts on the reproductive success of their host plants. The effects of multiple antagonists to plant reproduction may not be predictable from studying their individual effects in isolation. If consumer behaviors are contingent on each other, such interactions may limit the ability of the host to evolve in response to any one enemy. Here, we asked whether nectar robbing by a bumblebee (Bombus occidentalis) altered the likelihood of pre-dispersal seed predation by a fly (Hylemya sp.) on a shared host plant, Ipomopsis aggregata (Polemoniaceae). We estimated the fitness consequences of the combined interactions using experimental manipulations of nectar robbing within and among sites. Within sites, nectar robbing reduced the percentage of fruits destroyed by Hylemya. However, the negative effects of robbing on seed production outweighed any advantages associated with decreased seed predation in robbed plants. We found similar trends among sites when we manipulated robbing to all plants within a local population, although the results were not statistically significant. Taken together, our results suggest that seed predation is not independent of nectar robbing. Thus, accounting for the interactions among species is crucial to predicting their ecological effects and plant evolutionary response.

Visualizing Genetic Programming Ancestries

Abstract: Previous work has demonstrated the utility of graph databases as a tool for collecting, analyzing, and visualizing ancestry in evolutionary computation runs. That work focused on sections of individual runs, whereas this paper illustrates the application of these ideas on the entirety of large runs (up to three hundred thousand individuals) and combinations of multiple runs. Here we use these tools to generate graphs showing all the ancestors of successful individuals from a variety of stack-based genetic programming runs on software synthesis problems. These graphs highlight important moments in the evolutionary process. They also allow us to compare the dynamics for successful and unsuccessful runs. As well as displaying these full ancestry graphs, we use a variety of standard techniques such as size, color, pattern, labeling, and opacity to visualize other important information such as fitness, which genetic operators were used, and the distance between parent and child genomes. While this generates an extremely rich visualization, the amount of data can also be somewhat overwhelming, so we also explore techniques for filtering these graphs that allow us to better understand the key dynamics.