Introducing new algorithmic ideas is a key part of the continuous improvement of existing optimization algorithms. However, when introducing a new component into an existing algorithm, assessing its potential benefits is a challenging task. Often, the component is added to a default implementation of the underlying algorithm and compared against a limited set of other variants. This assessment ignores any potential interplay with other algorithmic ideas that share the same base algorithm, which is critical in understanding the exact contributions being made. We explore a more extensive procedure, which uses hyperparameter tuning as a means of assessing the benefits of new algorithmic components. This allows for a more robust analysis by not only focusing on the impact on performance, but also by investigating how this performance is achieved. We implement our suggestion in the context of the Modular CMA-ES framework, which was redesigned and extended to include some new modules and several new options for existing modules, mostly focused on the step-size adaptation method. Our analysis highlights the differences between these new modules, and identifies the situations in which they have the largest contribution. CCS CONCEPTS• Theory of computation → Design and analysis of algorithms; Bio-inspired optimization.

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In this study, we analyze behaviours of the well-known CMA-ES by extracting the time-series features on its dynamic strategy parameters. An extensive experiment was conducted on twelve CMA-ES variants and 24 test problems taken from the BBOB (Black-Box Optimization Bench-marking) testbed, where we used two different cutoff times to stop those variants. We utilized the tsfresh package for extracting the features and performed the feature selection procedure using the Boruta algorithm, resulting in 32 features to distinguish either CMA-ES variants or the problems. After measuring the number of predefined targets reached by those variants, we contrive to predict those measured values on each test problem using the feature. From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants. We conducted a hierarchical clustering analysis on the test problems and noticed a drastic change in the clustering outcome when comparing the longer cutoff time to the shorter one, indicating a huge change in search behaviour of the algorithm. In general, we found that with longer time series, the predictive power of the time series features increase. CCS CONCEPTS• Theory of computation → Bio-inspired optimization; • Mathematics of computing → Time series analysis; Exploratory data analysis.

In this study, an experiment is conducted to measure the performance in speed and accuracy of interactive visualizations. A platform for interactive data visualizations was implemented using Django, D3, and Angular. Using this platform, a questionnaire was designed to measure a difference in performance between interactive and noninteractive data visualizations. In this questionnaire consisting of 12 questions, participants were given tasks in which they had to identify trends or patterns. Other tasks were directed at comparing and selecting algorithms with a certain outcome based on visualizations. All tasks were performed on high content screening data sets with the help of visualizations. The difference in time to carry out tasks and accuracy of performance was measured between a group viewing interactive visualizations and a group viewing noninteractive visualizations. The study shows a significant advantage in time and accuracy in the group that used interactive visualizations over the group that used noninteractive visualizations. In tasks comparing results of different algorithms, a significant decrease in time was observed in using interactive visualizations over noninteractive visualizations.