Search Results (4)
Characterization of constrained continuous multiobjective optimization problems
Despite the large volume of recently published papers in the field of constrained multiobjective optimization, the understanding and characterization of constrained multiobjective optimization problems (CMOPs) for benchmarking multiobjective evolutionary algorithms (MOEAs) and the related constraint handling techniques (CHTs) remain unsatisfactory. Therefore, selecting appropriate CMOPs for benchmarking is challenging and lacks …
Searching for Credible Relations in Machine Learning
Can a model constructed by machine learning or data mining programs be trusted? For example, it is known that a decision tree model can contain less-credible parts caused by pathologies in induction algorithms, noise and missing values in data, or simply because of the complexity of a domain. Such models …
Parallelization of an Evolutionary Algorithm for Multiobjective Optimization
Solving real-life optimization problems numerically is often very time demanding, because of high complexity of the simulations that are usually involved. Solving such problems becomes highly impractical for this reason and can even lead to use of less complex and also less accurate models. Fortunately, evolutionary algorithms, often used in …
Stigmergy as an Approach to Metaheuristic Optimization
Developing metaheuristics to solve optimization problems is a rapidly growing field of research. This is due to the importance of optimization problems in the scientific as well as the industrial world. The methods developed in this dissertation are based on stigmergy: a method of communication in emergent systems, where the …