From: mk_thisisit
Evolutionary algorithms are designed to mimic the theory of evolution, specifically Darwin’s theory of natural selection [01:30:20]. These algorithms are employed to identify a unit or object that possesses the optimal characteristics based on predefined criteria [01:41:43].
How Evolutionary Algorithms Work
The process typically involves:
- Defining a Goal The algorithm starts with a specific problem to solve, such as creating an “ideal” table based on defined criteria [01:51:52].
- Population Creation A “population” of many units (e.g., tables) is generated, each slightly differing from the others [02:06:06].
- Modification/Mutation These units undergo a phase of modification or mutation [02:11:00].
- Selection The tables that best meet the initial criteria are selected [02:17:00].
- Iteration This process is repeated iteratively with the expectation that over time, the generated units will increasingly approach the “ideal” solution [02:22:00].
This approach can be likened to a “game of life,” where algorithms, including artificial neural networks, attempt to replicate or simulate natural life processes [02:29:20]. However, not all algorithms designed to solve problems (such as sentence completion) necessarily incorporate an evolutionary element [03:00:00].