text: An approach to the acceleration of parametric weak classifier boosting is proposed. Weak classifier is called parametric if it has fixed number of parameters and, so, can be represented as a point into multidimensional space. Genetic algorithm is used instead of exhaustive search to learn parameters of such classifier. Proposed approach also takes cases when effective algorithm for learning some of the classifier parameters exists into account. Experiments confirm that such an approach can dramatically decrease classifier training time while keeping both training and test errors small.
  @cite_0: Recently, P. Viola and M.J. Jones (2001) presented a method for real-time object detection in images using a boosted cascade of simple features. In This work we show how an evolutionary algorithm can be used within the Adaboost framework to find new features providing better classifiers. The evolutionary algorithm replaces the exhaustive search over all features so that even very large feature sets can be searched in reasonable time. Experiments on two different sets of images prove that by the use of evolutionary search we are able to find object detectors that are faster and have higher detection rates.
  @cite_3: have introduced a fast object detection scheme based on a boosted cascade of haar-like features. In this paper, we introduce a novel ternary feature that enriches the diversity and the flexibility significantly over haar-like features. We also introduce a new genetic algorithm based method for training effective ternary features. Experimental results showed that the rejection rate can reach at 98.5 with only 16 features at the first layer of the cascade detector. We confirmed that the training time can be significantly shortened while the performance of the resulted cascade detector is comparable to the previous methods.
  @cite_4: This paper presents an efficient method for automatic training of performant visual object detectors, and its successful application to training of a back-view car detec- tor. Our method for training detectors is adaBoost applied to a very general family of visual features (called “control-point” features), with a specific feature-selection weak-learner: evo-HC, which is a hybrid of Hill-Climbing and evolutionary-search. Very good results are obtained for the car-detection application: 95 positive car detection rate with less than one false positive per image frame, computed on an independant validation video. It is also shown that our original hybrid evo-HC weak-learner allows to obtain detection performances that are unreachable in rea- sonable training time with a crude random search. Finally our method seems to be potentially efficient for training detectors of very different kinds of objects, as it was already previously shown to provide state-of-art performance for pedestrian-detection tasks.
  @cite_7: 
summary: Usage of genetic algorithm for weak learner acceleration was already proposed in several works. For example, in @cite_0 genetic weak learner with special crossover and mutation operators was used to learn classifier based on extended haar feature set. In @cite_7 genetic algorithm was used to select a few thousand weak classifiers with smallest error on unweighed training set before boosting process starts. Then exhaustive search over selected classifiers was performed on each boosting iteration to select the one with minimal weighed loss. In @cite_3 boosting procedure was completly integrated with genetic algorithm. Few classifiers were selected on each boosting iteration from solution population and added to the strong classifier. That selected classifiers were then used to produce new population members by applying genetic operators. Then, in @cite_4 authors used for weak learner some special evolutionary algorithm they've called Evolutionary Hill-Climbing . Crossover operator was not used in it. Instead, @math different mutations were applied to every population member on each algorithm iteration. Result of each mutation was rejected when it did not improve fitness function value.