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Erschienen in:
2019 | OriginalPaper | Buchkapitel
19. Maximum a Posteriori Based Evolutionary Algorithm
verfasst von : Asmaa Ghoumari, Amir Nakib, Patrick Siarry
Erschienen in: Bioinspired Heuristics for Optimization
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Abstract
This work is dedicated to the presentation and the analysis of the performance of Maximum a posteriori based Evolutionary Algorithm (MEA). MEA allows a hybridization of set of operators to preserve the diversity during the search. This approach is based on a set of search strategies which are composed of one crossover with one mutation method, respectively. The algorithm uses the Maximum a Posteriori Principle (MAP) to select the most probable strategy from those available in the search set. Experiments were performed on well-known continuous optimization problems to observe the impact of population size and operators rates on MEA’s behaviour, robustness and performance.