The amazing Genetic Algorithms!

Why do we say Data Science? is this a part of the Science? if you think as an Statistics or Engineer it’s difficult to understand.
One of the most beautiful things in the earth is the nature. We don’t think about that but we are rounded about nature inspired objects, for example planes (like birds), buildings (like hives) or submarines (like whales). When we talk about the Computer World we have also looked at the nature world to learn how to find the best solutions to the most difficult problems.
In the Data Science Universe, more concretely in the algorithm side, we have interesting nature oriented solutions from fields like Neural Networks or Genetics or Swarm Intelligence.
Is really interesting thinking in how to find algorithms that emulate Neural Networks to solve daily problems we have, or look at the bees or ants to use Swarm Intelligence and replicate this behaviour to apply solutions in Healthcare or Public Administration to improve the quality of live of the people. This is my main objective try to give VALUE to the society and through the Data Science Universe I believe that it’s a reality!
Here you find examples of biological systems that have inspired computational algorithms.
Also in this blog you find more detail with examples around Algorithms in Nature.Turnig back to this post I focus in the  Genetics Algorithm, in other posts I will talk about other Nature Algorithms.

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems.
Biologists have been increasingly relying on sophisticated computational methods, especially over the last two decades as molecular data have rapidly accumulated. Computational tools for searching large databases, including BLAST (Altschul et al, 1990), are now routinely used by experimentalists. Genome sequencing and assembly rely heavily on algorithms to speed up data accumulation and analysis (Gusfield, 1997; Trapnell and Salzberg, 2009; Schatz et al, 2010).
Computational methods have also been developed for integrating various types of functional genomics data and using them to create models of regulatory networks and other interactions in the cell (Alon, 2006; Huttenhower et al, 2009; Myers et al, 2009). Indeed, several computational biology departments have been established over the last few years that are focused on developing additional computational methods to aid in solving life science’s greatest mysteries.

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