Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited number of combinations through multivariate testing, making it possible to evaluate only a small fraction of designs in a vast design space. This paper describes Sentient Ascend, an automatic conversion optimization system that uses evolutionary optimization to create effective web interface designs. Ascend makes it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. Moreover, evaluation of design candidates is done in parallel online, i.e. with a large number of real users interacting with the system. A case study on an existing media site shows that significant improvements (i.e. over 43%) are possible beyond human design. Ascend can therefore be seen as an approach to massively multivariate conversion optimization, based on a massively parallel interactive evolution.

Bronze Medal in the Human-Competitive Results Competition.

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In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2017, Berlin, Germany), 2017.

@inproceedings{miikkulainen:gecco17, title={Conversion Rate Optimization through Evolutionary Computation}, author={Risto Miikkulainen and Neil Iscoe and Aaron Shagrin and Ron Cordell and Sam Nazari and Cory Schoolland and Myles Brundage and Jonathan Epstein and Randy Dean and Gurmeet Lamba}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2017, Berlin, Germany)}, url="http://nn.cs.utexas.edu/?miikkulainen:gecco17", year={2017} }