Tuning Multi-Objective Evolutionary Algorithms on Different Sized Problem Sets

Multi-Objective Evolutionary Algorithms

Evolutionary Multi-Objective Algorithms

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary co...

Multi-Objective Evolutionary Algorithms

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...

Evolutionary algorithms for the multi-objective test data generation problem

Automatic test data generation is a very popular domain in the field of search-based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test t...

Optimization of Multi-objective Transportation Problem Using Evolutionary Algorithms

The Multi-objective transportation problem (MOTSP) is a of linear type optimization problem which contains all equality type constraints and the multi variable objectives. Here we have presented an application of Evolutionary Algorithms to the multi-objective transportation problem (MOTSP). We consider the multi-objective transportation problem as linear optimization problem and use a special t...