Expensive Optimization with Model-Based Evolutionary Algorithms Applied to Medical Image Segmentation Using Deep Learning

Recently great achievements have been obtained with Artificial Intelligence (AI) methods including human-level performance in such challenging areas as image processing, natural language processing, computational biology, and game playing. Arguably, one of the most societally important application fields of such methods is healthcare. <br/>AI is...

A surrogate-assisted evolutionary algorithm based on inverse distance weighting: Applied to a multi-objective deformable image registration problem

Solutions to many real-life optimization problems take a long time to evaluate. This limits the number of solutions we can evaluate. When optimizing with an Evolutionary Algorithm (EA) a frequently used approach is to approximate the objective using a surrogate function, replacing the time-consuming real evaluation. This surrogate model is...

Hybrid Single Parent-Offspring MVMO for Solving CEC2018 Computationally Expensive Problems

Mean-Variance Mapping Optimization (MVMO) belongs to the family of evolutionary algorithms, and has proven to be competitive in solving computationally expensive problems proposed in the Icompetitions CEC2014, CEC2015, and CEC2016. MVMO can tackle such problems by evolving a set of solutions (population based approach) or a single solution ...