Unveiling evolutionary algorithm representation with DU maps

Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly available to all users, or too general to convey detailed information. In this work, we study the Diversity and Usage map (DU map), a compact visualization for analyzing a key component of every EA, the representation of solutions. In a single heat map, the DU map visualizes for entire runs how diverse the genotype is across the population and to which degree each gene in the genotype contributes to the solution. We demonstrate the generality of the DU map concept by applying it to six EAs that use different representations (bit and integer strings, trees, ensembles of trees, and neural networks). We present the results of an online user study about the usability of the DU map which confirm the suitability of the proposed tool and provide important insights on our design choices. By providing a visualization tool that can be easily tailored by specifying the diversity (D) and usage (U) functions, the DU map aims at being a powerful analysis tool for EAs practitioners, making EAs more transparent and hence lowering the barrier for their use

One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes

Visualization techniques for the decision space of continuous multi-objective optimization problems (MOPs) are rather scarce in research. For long, all techniques focused on global optimality and even for the few available landscape visualizations, e.g., cost landscapes, globality is the main criterion. In contrast, the recently proposed gradient field heatmaps (GFHs) emphasize the location and attraction basins of local efficient sets, but ignore the relation of sets in terms of solution quality. In this paper, we propose a new and hybrid visualization technique, which combines the advantages of both approaches in order to represent local and global optimality together within a single visualization. Therefore, we build on the GFH approach but apply a new technique for approximating the location of locally efficient points and using the divergence of the multi-objective gradient vector field as a robust second-order condition. Then, the relative dominance relationship of the determined locally efficient points is used to visualize the complete landscape of the MOP. Augmented by information on the basins of attraction, this Plot of Landscapes with Optimal Trade-offs (PLOT) becomes one of the most informative multi-objective landscape visualization techniques available.Comment: This version has been accepted for publication at the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI

Visualising Evolution History in Multi- and Many-Objective Optimisation

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III

Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs

Evaluation of Au/ZrO [sub] 2 catalysts prepared via postsynthesis methods in CO [sub] 2 hydrogenation to methanol

Au nanoparticles supported on ZrO2 enhance its surface acidic/basic properties to produce a high yield of methanol via the hydrogenation of CO2. Amorphous ZrO2-supported 0.5–1 wt.% Au catalysts were synthesized by two methods, namely deposition precipitation (DP) and impregnation (IMP), characterized by a variety of techniques, and evaluated in the process of CO2 hydrogenation to methanol. The DP-method catalysts were highly advantageous over the IMP-method catalyst. The DP method delivered samples with a large surface area, along with the control of the Au particle size. The strength and number of acidic and basic sites was enhanced on the catalyst surface. These surface changes attributed to the DP method greatly improved the catalytic activity when compared to the IMP method. The variations in the surface sites due to different preparation methods exhibited a huge impact on the formation of important intermediates (formate, dioxymethylene and methoxy) and their rapid hydrogenation to methanol via the formate route, as revealed by means of in situ DRIFTS (diffuse reflectance infrared Fourier transform spectroscopy) analysis. Finally, the rate of formation of methanol was enhanced by the increased synergy between the metal and the support

Benchmarking MO-CMA-ES and COMO-CMA-ES on the Bi-objective bbob-biobj Testbed

International audienceIn this paper, we propose a comparative benchmark of MO-CMAES, COMO-CMA-ES (recently introduced in [12]) and NSGA-II,using the COCO framework for performance assessment and the Bi-objective test suite bbob-biobj. For a fixed number of pointsp, COMO-CMA-ES approximates an optimal p-distribution of the Hypervolume Indicator. While not designed to perform on archive-based assessment, i.e. with respect to all points evaluated so far by the algorithm, COMO-CMA-ES behaves well on the COCO platform. The experiments are done in a true Black-Blox spirit by using a minimal setting relative to the information shared by the 55 problems of the bbob-biobj Testbed

Benchmarking Algorithms from the platypus Framework on the Biobjective bbob-biobj Testbed

International audienceOne of the main goals of the COCO platform is to produce, collect , and make available benchmarking performance data sets of optimization algorithms and, more concretely, algorithm implementations. For the recently proposed biobjective bbob-biobj test suite, less than 20 algorithms have been benchmarked so far but many more are available to the public. We therefore aim in this paper to benchmark several available multiobjective optimization algorithms on the bbob-biobj test suite and discuss their performance. We focus here on algorithms implemented in the platypus framework (in Python) whose main advantage is its ease of use without the need to set up many algorithm parameters