Variation, Selection, Inheritance
Topic: Evolution
by Ziwei, 2020 Cohort
Evolution commonly refers to the theory in biological science of how characteristics in populations of organisms change over time, and is used to explain the emergence of the vast variety of complex life on earth. It is a core concept in biology and forms the foundation for much of the ideas in the discipline. Theodosius Dobzhansky, a central figure in evolutionary biology, has famously said that “Nothing in biology makes sense except in the light of evolution”. Evolution as a high level concept is widely known even outside of biology, but not many are familiar with the underlying mechanisms of it. Evolution is the consequence of a number of processes, each affecting one another, resulting in the emergence of certain trends within a population, which ultimately leads to change of the entire population over long periods of time. These processes can be applied to problems outside of biology, making them useful tools in solving complex problems. The core processes in evolution are variation, selection and inheritance. In a biological context, variation pertains to the differences in the genetics of organisms, which lead to the differences in traits among individuals of a population. Selection pertains to how well an individual can survive and reproduce in its environment, this will be largely influenced by what traits they possess. Inheritance pertains to the individual being able to pass on their traits to the next generation of individuals. Over time, the traits in a population will trend towards the traits which were well suited for survival and reproduction in the environment. Given enough time and generations, the traits present in the population will be so different from what it was initially that the population can be seen as having become something entirely different. In problem solving, we already intuitively apply some of the processes in evolution. When faced with a problem, we generate a slew of ideas for potential solutions (variation), we try these solutions and the ones which are proven to not work are not considered further (selection), and we retain knowledge of which solutions are effective in case we encounter similar problems in the future (inheritance). Being aware of these processes can give you a clear idea of what you should be doing in each stage and give you a structure for problem solving. Additionally, simulating the processes in evolution have helped in optimizing output in agriculture and in medicine. By simulating different selection pressures in the environment and causing variation with mutations, scientists are able to artificially evolve useful protein products. Similarly in computer science, evolutionary algorithms simulate evolution and are able to optimize themselves over many iterations based on the problem, and since the optimization is directly driven by the problem, the resulting algorithm can be more effective than human designed algorithms. By recognizing problem scenarios which lend themselves well to the core processes of evolution, one can effectively simulate evolution in order to generate optimal solutions which may be difficult to generate purely by human design. In complex problems, this may be a very useful approach as there may simply be too many factors for a human designed solution to consider. If these factors can be instead represented as selection pressures, and solutions can be generated with a certain degree of randomness, an optimized solution could emerge if solutions are tested against these selection pressures to be eliminated or retained.
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