On Practical Automated Engineering Design

Nolle, Lars; Krause, Ralf; Cant, Richard

doi:10.1007/978-3-319-33786-9_10

Cited by 4 publications

(1 citation statement)

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“…The exploitation and exploration abilities of genetic algorithms [1] can be further improved by incorporating local search methods such as steepest-descent search [2]. The capability of local search methods to integrate with genetic algorithms is widely used to solve real-world problems using minimum resources [3,4]. Hybridization of a genetic algorithm with a local search method can enhance the global search with local knowledge that can guide and accelerate the search towards the global optimum [5].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive learning for hybrid genetic algorithms

2020

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Local search can be introduced into genetic algorithms to create a hybrid, but any improvement in performance is dependent on the learning mechanism. In the Lamarckian model, a candidate solution is replaced by a fitter neighbour if one is found by local search. In the Baldwinian model, the original solution is retained but with an upgraded fitness if a fitter solution is found in the local search space. The effectiveness of using either model or a variable proportion of the two within a hybrid genetic algorithm is affected by the topology of the fitness function and the details of the hybrid algorithm. This paper investigates an intelligent adaptive approach to decide on the learning mechanism to be used by an individual over the course of the search. Evolution is used to self-adapt both the frequency of a steepest-descent local search and the relative proportions of Lamarckian and Baldwinian inheritance. Experiments have shown that this form of adaptive learning can improve the ability to find high-quality solutions and can accelerate the hybrid search without the need to find optimal control parameters for the learning process.

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Section: Introductionmentioning

confidence: 99%