Technical note: Although you may specify "C" for a continuous ranged parameter, the range may still be discretized if the search space encoding representation that you choose does not support continuous/ real-valued numbers.įor instance, the StandardBinar圜hromosome and GrayBinar圜hromosome encodings will use only 16 bits for a continuous parameter, which allows the parameter to take on up to 65,536 values.įor encodings that do allow continous parameters (such as MixedTypeChromosome), the "continuous" variable will technically be limited to 64-bit floating-point precision. Boolean and discrete-choice parameters (i.e.However, in BehaviorSearch it is also possible to specify a continuous range for a parameter, by using "C" for the increment - see examples below. You may notice that the syntax for specifying parameter ranges is very similar to that for BehaviorSpace. The total size of this search space is 41×21×81×81×81 = 457570701 possible combinations of parameter settings. In this case, the population parameter (number of birds) is being held constant, whereas each of the other parameters are allowed to range.įor instance, the vision parameter ranges from 0 up to 10, by increments of 0.25. One search space specification for the Flocking model consists of the following definitions (also shown in Figure 2): SLIDERS, CHOOSERS, and SWITCHES).įigure 2: An example search space specification for the Flocking model from NetLogo's models library. The easiest way to get started is to click the "Load param ranges from model interface" button, which will automatically extract the parameters/ranges that are included in your model's interface tab (i.e. The next step is to specify settings, or ranges of settings, for each of the model's parameters. Let's assume you choose the Flocking model from NetLogo's models library (in the Biology category). We will now discuss each of these regions in more detail, and how the various options affect the search process.įirst, choose the model you are interested in exploring (with the "Browse for model" button, or by typing in the path). The yellow region (lower left) specifies what search algorithm and search space encoding to use, as well as any additional parameters needed to configure the search algorithm.The red region (lower right) specifies how many times to run the model, as well as how to take the data collected from the model runs and turn it into an objective function for the search method to minimize or maximize.The green region (upper right) specifies how to run the model, what measure to collect, and when.The blue region (upper left) specifies the parameters to vary, and valid ranges for each parameter.The thin purple region at top specifies the NetLogo model you're working with.However, it is organized in logical subsections, as shown in Figure 1. The experiment editor may feel daunting at first, with so many choices and options to be filled in. The BehaviorSearch GUI helps you create, open, modify, and save these experiments (stored as files with the ".bsearch" extension). When you first open BehaviorSearch, the window that appears is the BehaviorSearch Experiment Editor.īehaviorSearch is centered around the paradigm of an experiment (or search protocol), which contains all of the information necessary to specify how the search should be performed on a model. Run the search and examine the results.įigure 1: BehaviorSearch Experiment Editor GUI, color coded by the different functional areas of the interface.Choose a search algorithm to find parameters that maximize (or minimize) your measure.Design a quantitative measure for a behavior of interest.Choose model parameters to vary and what ranges are allowed.Additional help, feedback, bug reports, etc.īehaviorSearch is a software tool designed to help with automating the exploration of agent-based models (ABMs), by using genetic algorithms and other heuristic techniques to search the parameter-space.īehaviorSearch interfaces with the popular NetLogo ABM development platform, to provide a low-threshold way to search for combinations of model parameter settings that will result in a specified target behavior.Running the search and examining the results.
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