GRASP, or greedy randomized adaptive search procedure, is a multi-start metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this chapter we consider ways to hybridize GRASP to create new and more effective metaheuristics. We consider several types of hybridizations: constructive procedures, enhanced local search, memory str...

Many metaheuristic search methods are, in one form or another, based on populations. Besides the most prominent member of this group, the genetic algorithms (GAs), there is also a number of interesting methods which deal with the algorithmic inclusion of a pool component. To nominate the most important, there are the scatter search (SCS), the path relinking concept or simply restart techniques,...

GRASP is an iterative multi-start metaheuristic for solving difficult combinatorial problems. Each GRASP iteration consists of two phases: a greedy adaptive randomized construction phase and a local search phase. Starting from the feasible solution built during the greedy adaptive randomized construction phase, the local search explores its neighborhood until a local optimum is found. The best ...

The objective of this work is to propose ten efficient scaling techniques for the Wisconsin Diagnosis Breast Cancer (WDBC) dataset using support vector machine (SVM). These are linear programming approach. SVM with proposed was applied on WDBC dataset. are, namely, arithmetic mean, de Buchet three cases <mfenced open="(" close=")" separa...

The third ACES-MB workshop brought together researchers and practitioners interested in model-based software engineering for realtime embedded systems, with a particular focus on the use of models for architecture description and domain-specific design, and for capturing non-functional constraints. Twelve presenters proposed contributions on metaheuristic search techniques for UML, modelling la...

Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature...

Using advanced techniques of econometrics and a metaheuristic optimization approach, this study attempts to evaluate the potential advantages of international portfolio diversification for East Asian international investors when investing in the Middle Eastern emerging markets. Overall, the results of both econometric and the metaheuristic optimization methods are supporting each other. Finding...