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Program Optimization for Faster Genetic Programming

We have used genetic programming to develop efficient image processing software. The ultimate goal of our work is to detect certain signs of breast cancer that cannot be detected with current segmentation and classification methods. Traditional techniques do a relatively good job of segmenting and classifying small-scale features of mammograms, such as micro-calcification clusters. Our strongly-typed genetic programs work on a multi-resolution representation of the mammogram, and they are aimed at handling features at medium and large scales, such as stellated lesions and architectural distortions. The main problem is efficiency. We employ program optimizations that speed up the evolution process by more than a factor of ten. In this paper we present our genetic programming system, and we describe our optimization techniques.