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We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed(More)
Mammographic mass detection is an important task for early detection of breast cancer diagnosis and treatment. This is however still remains a challenging task. In this paper, we have proposed a multilevel thresholding algorithm for segmenting the tumor. This paper compares two most popular method, namely between class variance (Otsu) and entropy criterion(More)
We study how different types of preference information coming from a human decision maker can be utilized in an interactive multiobjective evolutionary optimization algorithm (MOEA). The idea is to convert different types of preference information into a unified format which can then be utilized in an interactive MOEA to guide the search towards the most(More)
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