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Evolutionary algorithms are based on Darwinian evolution. Some basic concepts are: - Population - Fitness - Selection - Mutation Individuals that adapt well have a high probability of generating offspring.
Evolution is a process that acts on populations of individuals. Information is stored in the form of chromosomes with each chromosome containing many genes. In an EA - A population of candidate solutions is formed (randomly) - Each individual is evaluated (decode their information/chromosomes then evaluated) and assigned a fitness score based on performance. - New individuals are formed through the process of selection, crossover, and mutation - Repeat until a new population is formed. - Once new population is formed we check if we’ve met our termination condition, if not start the whole process again.
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