The field of metaheuristics for the application to combinatorial optimization problems is a
rapidly growing field of research. This is due to the importance of combinatorial optimization …

Discovering the conditions under which an optimization algorithm or search heuristic will
succeed or fail is critical for understanding the strengths and weaknesses of different …

Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization
problems. As their popularity has increased, the number of GA applications has grown in …

" It is well known that" building blocks", whether they be the atoms of chemistry, the words of
a language, or the modules of a computer, play a key role in our understanding of the world …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

One common characterization of how simple hill-climbing optimization methods can fail is
that they become trapped in local optima-a state where no small modification of the current …

The previous chapter has discussed how hierarchy can be used to reduce problem
complexity in black-box optimization. Additionally, the chapter has identified the three …

Analyzing Evolutionary Algorithms: The Computer Science Perspective | SpringerLink Skip to
main content Advertisement SpringerLink Log in Menu Find a journal Publish with us Search …

In recent years, a gradual increase in the sophistication of multiobjective evolutionary
algorithms (MOEAs) for Pareto optimization has been seen, accompanied by an ever …

There are four primary goals of this dissertation. First, design a competent optimization
algorithm capable of learning and exploiting appropriate problem decomposition by …