Multi-objective formulations are a realistic model for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Due to the multiplicity in solutions, these problems were proposed to be solved suitably using evolutionary algorithms which use a population approach in its search procedure. Since Genetic Algorithm (GA) works with a set of individual solutions called population, it is natural to adopt GA schemes for Multi-Objective Optimization problems so that once can capture a number of solutions simultaneously.

## Tag Archive for Genetic Algorithm

## Delta Debugging

Delta Debugging is an automated approach to debugging that isolates failures systematically.

It takes two steps of input, one which yields a correct result and another one which cause a failure. Delta Debugging then modifies both input with the goal of minimizing their difference while preserving the test outcome.

It includes a compile-time step, which produces a mutations of the program to be analyzed, and a run-time step where the outcome of that mutation is verified by testing.