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Evolution strategies use real-vectors as coding representation, and primarily mutation and selection as search operators. As common with Evolutionary Algorithms , the operators are applied in a loop. An iteration of the loop is called a generation. The sequence of generations is continued until a termination criterion is met. Mutation is normally performed by adding a Normally Distributed random value to each vector component. The step size or mutation strength (ie. the standard deviation of the normal distribution) is often governed by self-adaptation (see Evolution Window ). Individual step sizes for each coordinate or correlations between coordinates are either governed by self-adaptation or by covariance matrix adaptation ( CMA-ES ). The (environmental) selection in evolution strategies is deterministic and only based on the fitness rankings, not on the actual fitness values. The simplest ES operates on a population of size two: the current point (parent) and the result of its mutation. Only if the mutant has a higher fitness than the parent, it becomes the parent of the next generation. Otherwise the mutant is disregarded. This is a (1+1)-ES. More generally, λ mutants can be generated and compete with the parent, called (1+λ)-ES. In a (1,λ)-ES the best mutant becomes the parent of the next generation while the current parent is always disregarded. Contemporary derivatives of evolution strategy often use a population of μ parents and also recombination as an additional operator (called (μ/ρ+,λ)-ES). This is believed to make them less prone to get stuck in local optima. SEE ALSO REFERENCES
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