Wallacei

www.wallacei.com

Wallacei is an evolutionary multi-objective optimisation engine that allows users to run evolutionary simulations in Grasshopper 3D through utilising highly detailed analytic tools coupled with various comprehensive selection methods to assist users to better understand their evolutionary runs, and make more informed decisions at all stages of their evolutionary simulations; including setting up the design problem, analysing the outputted results and selecting the desired solution or solutions for the final output. Additionally, Wallacei provides users with the ability to select, reconstruct and output any phenotype from the population after completing their simulation. The free plugin is streamlined to give users efficient access to the data outputted by their evolutionary simulations, and enable clear and efficient methods for analysis and selection – The aim is for users (of all degrees of expertise) to better understand their evolutionary simulations, gain a thorough understanding of the outputted numeric values, and seamlessly extract the optimised data; all within one user interface.

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Wallacei is an evolutionary multi-objective optimisation engine that allows users to run evolutionary simulations in Grasshopper 3D through utilising highly detailed analytic tools coupled with various comprehensive selection methods to assist users to better understand their evolutionary runs, and make more informed decisions at all stages of their evolutionary simulations; including setting up the design problem, analysing the outputted results and selecting the desired solution or solutions for the final output. Additionally, Wallacei provides users with the ability to select, reconstruct and output any phenotype from the population after completing their simulation. The free plugin is streamlined to give users efficient access to the data outputted by their evolutionary simulations, and enable clear and efficient methods for analysis and selection – The aim is for users (of all degrees of expertise) to better understand their evolutionary simulations, gain a thorough understanding of the outputted numeric values, and seamlessly extract the optimised data; all within one user interface.

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City (Headquarters)

London

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Employees

1-10

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Founded

2018

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