Evolutionary algorithms for the multi-objective test data generation problem

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...

Multi-objective evolutionary algorithms for data clustering

In this work we investigate the use of Multi-Objective metaheuristics for the data mining task of clustering. We first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality and then perform investigations into the performance of different Multi-Objective clustering algorith...

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...