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In computer science, evolution strategy (ES) is an optimization technique based on ideas of adaptation and evolution. In Mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function It was created in the 1960s and 70s by Ingo Rechenberg and his co-workers, and belongs to the more general class of evolutionary computation or artificial evolution. Ingo Rechenberg (born January 20 1934 in Berlin) is a German computer scientist and professor In Computer science evolutionary computation is a subfield of Artificial intelligence (more particularly Computational intelligence) that involves In Artificial intelligence, an evolutionary algorithm (EA is a Subset of Evolutionary computation, a generic population-based Metaheuristic For a peer-reviewed definition, consult also Scholarpedia's Evolution Strategies.

Evolution strategies use natural problem-dependent representations, and primarily mutation and selection as search operators. As common with evolutionary algorithms, the operators are applied in a loop. In Artificial intelligence, an evolutionary algorithm (EA is a Subset of Evolutionary computation, a generic population-based Metaheuristic An iteration of the loop is called a generation. The sequence of generations is continued until a termination criterion is met.

As far as real-valued search spaces are concerned, mutation is normally performed by adding a normally distributed random value to each vector component. The normal distribution, also called the Gaussian distribution, is an important family of Continuous probability distributions applicable in many fields The step size or mutation strength (i. e. the standard deviation of the normal distribution) is often governed by self-adaptation (see evolution window). It was observed in evolution strategies that significant progress toward the fitness/objective function's optimum, generally only can happen in a narrow band of the mutation Individual step sizes for each coordinate or correlations between coordinates are either governed by self-adaptation or by covariance matrix adaptation (CMA-ES). CMA-ES stands for Covariance Matrix Adaptation Evolution Strategy

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.

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