{'fraction_non_alphanumeric': 0.018534119629317607, 'fraction_numerical': 0.003369839932603201, 'mean_word_length': 4.739130434782608, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Genetic algorithms for scene interpretation from prototypical semantic description

Use of a genetic algorithm assumes the existence of a figure of merit called fitness, for which there is a value for every candidate solution. The fitness must be measurable over the representation of the solution by means of a computable function. The fitness function is, in most cases, independent of the other factors, including the algorithm used. Often, the fitness is an estimation of the nearness to an ideal solution or the distance from a default solution. In image scene interpretation, the solution takes the form of a set of labels corresponding to the components of an image and its fitness is difficult to conceptualize in terms of distance from a default or nearness to an ideal. Here we describe a model in which a semantic net is used to capture the salient properties of an ideal labeling. Instantiating the nodes of the semantic net with the labels from a candidate solution (a chromosome) provides a basis for estimating a logical distance from a norm. This domain‐independent model can be applied to a broad range of scene‐based image analysis tasks. © 2000 John Wiley & Sons, Inc.