Author: Epstein, Jonathan ; Schoolland, Cory ; Cordell, Ron ; Brundage, Myles ; Iscoe, Neil ; Dean, Randy ; Nazari, Sam ; Lamba, Gurmeet ; Shagrin, Aaron ; Miikkulainen, Risto

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.

20 Jul 2016·Proceedings of the Genetic and Evolutionary Computation Conference 2016

Estimating the Advantage of Age-Layering in Evolutionary Algorithms

Author: Hodjat, Babak ; Miikkulainen, Risto ; Shahrzad, Hormoz

In an age-layered evolutionary algorithm, candidates are evaluated on a small number of samples first; if they seem promising, they are evaluated with more samples, up to the entire training set. In this manner, weak candidates can be eliminated quickly, and evolution can proceed faster. In this paper, the fitness-level method is used to derive a theoretical upper bound for the runtime of (k+1) age-layered evolutionary strategy, showing a significant potential speedup compared to a non-layered counterpart. The parameters of the upper bound are estimated experimentally in the 11-Multiplexer problem, verifying that the theory can be useful in configuring age layering for maximum advantage. The predictions are validated in a practical implementation of age layering, confirming that 60-fold speedups are possible with this technique.

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