A Genetic Algorithm for Multiobjective Optimization in C++
Introduction Many real-world optimization problems require multiple, often conflicting objectives, to optimized simultaneously. Historically, their solution was frequently addressed by single fitness function consisting of a weighted sum of the multiple attributes. This approach can be problematic for a number of reasons. Firstly, the final solution obtained can be highly sensitive to small changes in the weighting factors. The result obtained is a single point solution that will largely depend on the weights assigned to each objective. Secondly, this approach is inefficient because it cannot find multiple, Pareto-optimal solutions in a single run. The classical approach would need to be run at least as many times as the desired number of Pareto-optimal solutions.
