Biological evolution is the process describing how organisms evolve over time to adapt to the environment for better survival. This adaption happens in a most optimal way possible and the definition of optimality is quite dynamic decided by the environment. Definitely, to develop such adaption is the result of a very long learning process evolving as a part of fundamental natural instincts. Understanding the way it happens across generations can be leveraged effectively for many AI applications in practice. A large class of such applications is contributed by selection of optimal alternative over large other options and learning such optimal alternatives through experimentations. Genetic algorithms represent such natural evolution process.

Read more about the basics of GA with the illustration in the whitepaper, here.

Umesh Hivarkar


Multidisciplinary skills with a unique combination of scientific R&D, academics, and industry profile. Doctoral research in fiber optics & sensors, Instrumentation, Integrated optics, and Modeling & Simulation. Spanning over 24+ years of application-oriented R&D and multi-sector industrial experience. Expertise in Engineering Analytics, automation, and control, and Advanced Intelligent Systems. Known for innovative solutioning and industrialization of cutting edge technologies.

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