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On the class of hybrid adaptive evolutionary algorithms (chavela)

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Abstract

There is no doubt that both determining theoretical properties and characterizing the observed behavior of an evolutionary algorithm allow us to understand when to use such an algorithm in solving a class of optimization problems. One of those evolutionary algorithms is the Hybrid Adaptive Evolutionary Algorithm (haea). The general scheme followed by a haea algorithm is to evolve every individual of the population by selecting genetic operators according to a kind of chaotic competition mechanism. This paper proposes and studies, from both theoretical and experimental points of view, the class of hybrid adaptive evolutionary algorithms (called chavela), i.e., the class of evolutionary algorithms that follow such a general scheme. In this way, this paper presents a formal characterization of the chavela class in terms of Markov kernels; establishes convergence properties; proves that (parallel) hill-climbing algorithms belong to the chavela class; develops generational, steady-state, and classic versions; and analyzes the running behavior of chavela on well-known optimization functions.

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  1. Computer Systems Engineering, Engineering School, Universidad Nacional de Colombia, Bogotá, Colombia

    Jonatan Gómez & Elizabeth León

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  1. Jonatan Gómez
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  2. Elizabeth León
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Correspondence to Jonatan Gómez.

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Gómez, J., León, E. On the class of hybrid adaptive evolutionary algorithms (chavela). Nat Comput 20, 377–394 (2021). https://doi.org/10.1007/s11047-021-09843-5

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