Aslan, Bilal and Correa da Silva, Flavio and Nitschke, Geoff (2024) Multi-Objective Evolution for Chemical Product Design, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2024), 14-18 July, Melbourne, Australia, ACM.
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Abstract
The design of chemical products requires the optimization of desired properties in molecular structures. Traditional techniques are based on laboratory experimentation and are hindered by the intractable number of alternatives and limited capabilities to identify feasible molecules and either test or infer their properties for optimization. Computational techniques based on deep learning and multi-objective evolutionary optimization have spurred chemical product design, but the definition of appropriate metrics to compare techniques is challenging. We suggest the adoption of two complementary assessments to account for quantitative as well as qualitative features of different techniques, and then test our proposed assessments by comparing two heuristics to build new generations of molecular candidates, termed respectively, direct correlation and extended search.
| Item Type: | Conference paper |
|---|---|
| Subjects: | Computing methodologies > Artificial intelligence Computing methodologies > Modeling and simulation > Simulation types and techniques |
| Date Deposited: | 19 Jun 2024 11:11 |
| Last Modified: | 19 Jun 2024 11:11 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1660 |
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